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ALPW analysis for the 21 August 2021 Flood Event in central Tennessee

By Sheldon Kusselson and Dan Bikos

Corresponding ALPW Loop

30 November 2020 Severe Weather Event in the Mid-Atlantic

By Dan Bikos, Sheldon Kusselson and Jorel Torres

On 30 November 2020, a trough affected the Mid-Atlantic region and was responsible for the following severe weather reports:

https://www.spc.noaa.gov/climo/reports/201130_rpts.html

In this blog post, we’ll examine aspects of the synoptic scale setup for this event.

The following GOES-16 low-level water vapor (7.3 micron) animation:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30nov20/band10/&loop_speed_ms=200

depicts an extra-tropical cyclone moving northeastward.  Thunderstorms developed in northeast Virginia around 1800 UTC and moved northeast across Maryland, Delaware, Pennsylvania and New Jersey.  These thunderstorms appear to have developed at the nose of a dry slot apparent in the imagery as a region of warmer brightness temperatures.  Click on the image below to see the thunderstorms of interest (yellow circle) and approximate boundary of the dry slot over the region of interest (green line):

 What role did the dry slot have on the thunderstorms?

One of the more useful observational products to assess atmospheric water vapor in 4 dimensions is the Advected Layered Precipitable Water (ALPW) product which makes use of microwave instruments on several polar orbiting satellites.  Water vapor is retrieved without the use of NWP output in the retrieval, short-term NWP output wind information is used only to advect the retrieved moisture fields.  A loop of ALPW looks like this:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30nov20/ALPW/&loop_speed_ms=550

Upper-left panel: Surface to 850 mb Precipitable Water

Upper-right panel: 850 to 700 mb Precipitable Water

Lower-left panel: 700 to 500 mb Precipitable Water

Lower-right panel: 500 to 300 mb Precipitable Water

The loop goes back to 1800 UTC the previous day so that we may view how the moisture fields are changing in time.  At low-levels, areas of higher moisture indicate the warm sector with moisture advection northward ahead of the developing cyclone.  As we get higher into the atmosphere we see indications of the upper low in northern Texas / Oklahoma with drier air on its southern / southwest flank wrapping cyclonically around it on the eastern flank:

Subsidence within the dry slot would account for dry air at mid- to upper levels, but are there other sources of dry air?

Next, we’ll consider this 4 panel animation:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30nov20/LR/700-500/&loop_speed_ms=550

Upper-left: GFS 700-500 mb lapse rate

Upper-right: ALPW product  in the 700 to 500 mb layer

Lower-left: GOES-16 7.3 micron imagery

Lower-right: ALPW product in the 850 to 700 mb layer

Early in the loop, during the daytime hours of November 29 we see a rather obvious region of steeper mid-level lapse rates in Arizona and northwest Mexico.  The evening sounding from Tucson, AZ:

Shows a deep well-mixed layer with very steep (nearly dry-adiabatic) mid-level lapse rates.  In the GOES 7.3 micron imagery we see very warm brightness temperatures since the weighting function peaks around 600-700 mb, within the well mixed warm / dry air mass.  The ALPW product in the 700-500 mb layer shows this as a very dry air mass as well.  The animation indicates that this air mass advected eastward towards Texas, so we may suspect some portion of the dry air observed further east may have come from the Elevated Mixed Layer (EML) across Arizona and northern Mexico.  Next we’ll look at soundings to assess this.

The 00Z soundings from Del Rio, TX:

and Corpus Christi, TX:

depict dry air at mid-levels, as well as very steep mid-level lapse rates characteristic of what you may expect with an EML as we observed in the Tucson sounding.

The animation appears to show the dry air mass moving from Arizona into Texas and Louisiana combining with the developing trough where you’d expect to see a subsidence signal (mid-level drying).  In fact a sounding north of the EML plume as indicated by the steeper mid-level lapse rates in the GFS at Fort Worth, TX:

shows the dry air mass at mid-levels, but not the steeper mid-level lapse rates like we observe further south in Texas, much like you would associate with being in a subsidence region of a developing trough.  The dry air mass we observe on satellite images appears to be a combination of both the subsidence region of a developing extra-tropical cyclone and an EML with origins over Arizona / northern Mexico.  The EML is of particular note since the steeper mid-level lapse rates may lead to a more favorable environment for severe thunderstorms.  Click on this image to compare the above 00Z sounding sites with the imagery at the corresponding time:

Next, let’s assess November 30 12Z soundings, we’ll be referring to the following sounding sites (corresponding imagery from 12Z is shown):

Was the EML evident in the Nov. 30 12Z soundings?

Yes, in Atlanta, GA:

and also in Tallahassee, FL:

Note that the region of warmer brightness temperatures in the GOES 7.3 micron band and drier air mass in the ALPW 700-500 mb layer is not quite to central North Carolina at 12Z:

 

The mid-level dry air mass had not yet arrived at sounding time, however without an 18Z RAOB is there another way to assess the air mass at Greensboro, NC (GSO)?

Yes, NUCAPS provides soundings (except no wind data) around 18Z, here is the NUCAPS sounding at that time a little southeast of Greensboro, NC within the dry mid-level air mass:

We can see that the mid-level dry air mass has moved through Greensboro, and the mid-level lapse rates are quite steep (7.5 degrees C per km), this is confirmation of the mid-level lapse rates caused by a combination of the EML and being in the subsidence region of the extra-tropical cyclone.

Compare the 12Z Greensboro sounding with the 18Z NUCAPS sounding near Greensboro

Note that low-level features such as sharp capping inversions may not be adequately resolved with NUCAPS soundings, however that is not an issue here since we are assessing mid-level drying and lapse rates.

Moving on to the time of the severe thunderstorms, we analyze the GOES visible (0.64 micron) imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30nov20/vis/&loop_speed_ms=100

Cloud coverage was quite extensive during the morning hours over the region that experienced severe weather which likely limited the overall severe threat, however there was a narrow region of clearing that developed that allowed for sufficient insolation and thus destabilization that contributed to the severe thunderstorms.

ALPW depiction of many tropical cyclones & disturbances – September 14, 2020

By Sheldon Kusselson

ALPW depiction of mid-level dry air associated with Tropical Storm Isaias

By Sheldon Kusselson

Loop of ALPW 700-500 mb layer over a long time period

Impacts of Water Vapor on Satellite Dust Detection of the 16-17 February 2020 Saharan Air Layer Dust Event over the Eastern Atlantic

Lewis Grasso1, Dan Bikos1, Jorel Torres1, John Forsythe1, Heather Q. Cronk1, Curtis J. Seaman1, Emily Berndt2

1Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO

2NASA Marshall Space Flight Center, Short-term Prediction Research and Transition Center, Huntsville, AL

In the afternoon of 16 February 2020 a dust layer moved off western Africa. A loop of GeoColor imagery  from ABI on GOES-16 exhibits the dust layer, which is typically part of a Saharan Air Layer (SAL), that moved westward over the eastern Atlantic Ocean. At approximately 15:40 UTC 16 February 2020, CALIPSO moved northward over the SAL; a green line segment in Fig. 1a indicates the ground track of the CALIPSO satellite. Data from CALIOP, the primary sensor onboard CALIPSO, was used to produce a Vertical Feature Mask (VFM), also shown in Fig. 1b. As seen in the VFM, the primary constituent was dust and is indicated in the VFM as a yellow shading, from the surface to about 3.0 km.

Figure 1: (a) GeoColor imagery diagnosed from ABI on GOES-16 valid at 1540 UTC 16 February 2020 along with a portion of the ground track (green line segment) of CALIPSO from 1530 UTC to 1543 UTC 16 February 2020. (b) Dust, in yellow, is displayed in the vertical feature mask from CALIOP.

Annotations are placed on GeoColor imagery valid 17:50 UTC 16 February 2020 that distinguish between a northern dust region (NDR) and a southern dust region (SDR), see Fig. 2. A black oval is used to indicate the NDR while a horizontal, black, dashed line segment denotes the SDR in the figure.

Figure 2: GeoColor imagery diagnosed from ABI on GOES-16, valid 1750 UTC 16 February 2020, along with the following annotations: A black oval bounds dust in the NDR while the horizontal, black, dashed line highlights dust in the SDR. Within the black oval are additional annotations in red. Further the letters A (upper left portion of the figure), B, and C appear. All annotations are used for comparison purposes with Fig. 3.

A companion image to Fig. 2 is Fig. 3, which displays the ABI infrared channel difference, Tb(10.35 um) – Tb(12.3 um)

Figure 3: Channel difference, Tb(10.35 µm) – Tb(12.3 µm) (˚C), from ABI on GOES-16 valid at 1750 UTC 16 February 2020. Annotations are the same as in Fig. 2. Dust is indicated by the blue and purple colors within the black oval in the northern dust region. There was a lack of a dust signal in the southern dust region.

A dust signal is evident in the NDR with negative to near zero values of the channel difference. In sharp contrast, the SDR is void of a dust signal with values of the channel difference near 3˚C in Fig. 3. In addition, the red oval in Fig. 2 surrounds thick dust, which is coincident with a strong dust signal in Fig 3. Southwest of the red dashed contour in Fig. 2, dust appears thin, which was a region with a weak dust signal in Fig. 3. In the northwest portion of Figs. 2 and 3 the letter A is used to highlight the sharp boundary of low-level liquid clouds as opposed to the diffuse boundary of dust, despite the same channel difference color. That is, different features in Fig. 2 may have similar values of the channel difference in Fig. 3. Further, the letter B denotes locations of thin cirrus along the western portions of the horizontal, black, dashed line segment of the SDR. Although the letter C identifies a region with a similar channel difference color as thin cirrus in Fig. 3, a comparison of the region C in Figs. 2 and 3 reveal clear skies.

Imagery from VIIRS on NOAA-20 at 1510 UTC 16 February 2020 is displayed in Fig. 4.

Figure 4: Data from VIIRS on NOAA-20 valid at approximately 1510 UTC 16 February 2020 showing (a) True-Color, as opposed to GOES-16 ABI GeoColor, imagery and (b) VIIRS channel  difference, Tb(10.76 µm) – Tb(12.01 µm), with the same color table shown in Fig. 3.

Note the similarity of patterns in True-Color imagery from VIIRS (Fig. 4a) and the channel difference (Fig. 4b) compared to those from ABI in Figs. 2 and 3. One unanswered question is why does dust in the NDR and SDR (Fig. 2) have such a different appearance in the channel difference (Fig. 3).

Values of the total precipitable water (TPW) from the GFS analysis are superimposed on GeoColor imagery, both valid at 1800 UTC 16 February 2020 (Fig. 5). As illustrated in the figure, values of TPW varied between 0.5 inches to 0.75 inches in the NDR; in contrast, TPW values increased significantly to near 1.5 inches in the SDR.

Figure 5: TPW (in) from the GFS analysis plotted on a GeoColor image derived from GOES-16 ABI; all data is valid at 1800 UTC 16 February 2020.

Values of the TPW from the GFS analysis were also superimposed on the ABI channel difference (Fig. 6), valid at the same time as Fig. 5.

Figure 6: Same as Fig. 5, except TPW (in) is plotted on the GOES-16 ABI Tb(10.35 µm) – Tb(12.3 µm) channel difference (˚C).

One striking feature in Fig. 6 is the clear link between low values of TPW (0.5-075 inches) and a dust signal in the NDR as opposed to high values of TPW (1.0-2.0 inches) an a lack of a dust signal in the SDR.

Satellite retrievals of Advected Layer Precipitable Water (ALPW) retrieved from microwave instruments, valid 0300 UTC 17 February 2020, are displayed in Fig. 7.

Figure 7: Advected Layer Precipitable Water product (in) for the surface to 850 hPa layer valid at 0300 UTC 17 February 2020.

As seen in the figure, the NDR was characterized has having the lowest values of LPW, from the surface to 850 hPa. Values increased sharply in the SDR where a dust signal was absent in ABI channel difference images.

Values of TPW diagnosed or computed from retrieved NUCAPS soundings, valid 03:30 17 February 2020, are shown in Fig. 8. Similar to the patterns in Fig. 7, values of TPW were the lowest over the NDR and largest over the SDR.

Figure 8: Gridded NUCAPS TPW (mm) valid at 0333 UTC 17 February 2020, which represents the time of the granule in the image.

Observations of the water vapor field for this dust event allows for the development of a hypothesis. Based on GFS analysis and satellite retrievals of water vapor, dust is detected/masked when water vapor content in the atmosphere is low/high. That is, when water vapor values are low enough infrared channel differences, and dust products, will detect a dust signal. However, when water vapor content is above a critical value the channel difference will be dominated by a water vapor signal, thus masking dust.

One beneficial consequence of infrared channel differencing of dust in a dry environment is the ability to track dust layers when the sun sets. During the night, GeoColor imagery will be unable to reveal dust layers as a result of the loss of reflection of solar energy off dust. Figure 9 is an animation of the channel difference, which allows a forecaster to follow the nocturnal morphology of dust.

Moisture products for the tornadic storm of 4 July 2020 in Saskatchewan

On 4 July 2020 a thunderstorm developed in southern Saskatchewan that led to numerous tornadoes (video, picturepictures).

Let’s analyze various satellite derived moisture products in the time period leading up to the tornadic storm.  The following loop 4-panel loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/4july20/4panel_moisture&loop_speed_ms=80

Upper-left: GOES-16 visible (0.64 micron) imagery.

Upper-right: Advected Layer Precipitable Water (ALPW) product in the surface to 850 mb layer with RAP 00hr surface winds.

Lower-left: GOES-R baseline Total Precipitable Water (TPW) product with RAP 00hr surface winds.

Lower-right: Merged (GOES + POES) Advected TPW product with surface observations.

It’s important to note the status of the 3 moisture products.  The GOES-R baseline TPW product has been operational in AWIPS, the ALPW product is non-operational but will become operational in AWIPS within the next 1-2 years, while the Merged Advected TPW product is very much experimental (still in development).

First looking at the visible imagery, we observe morning convection while later we see storms develop along a north-south oriented boundary.  Most of the storms move off to the northeast, however one of the storms appears to develop at the intersection of this boundary with another boundary that is almost east-west oriented and this storm moves southeast.  Tornadoes are associated with the storm as it’s moving southeast and exhibits inflow feeder clouds.

The RAP surface winds and surface observations provide indications of a triple point, however the signal is not strong since the winds are not strong. If we use this data along with indications of the boundary in the visible imagery to annotate with red dashed lines where the boundaries are:

The triple point shows up nicely with west winds (shortly later northwest winds) with the cold front to the west, light east/southeast winds in the warm sector and southwest winds further south with drier air (78/47 in the observation) – a classic dryline/cold front triple point although in this case much more subtle since the winds were relatively light and the boundaries and air mass differences were subtle (i.e., the weak cold front may be analyzed as a trough).  The WPC official surface analysis at 18Z shows the triple point analyzed as a trough to the north and southwest, with a warm front to the east:

Note the color table and range for the bottom 2 panels of the 4 panel image above are corresponding so as to make comparison between the 2 products.  One limitation of this however is that gradients at key ranges for this particular case may be smoothed out.  An alternative would be a  different color table that shows increased contrast across the feature of interest so that it stands out more readily such as this animation depicts:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/4july20/4panel_moisture_v2&loop_speed_ms=80

Viewing the animation of the moisture products to focus on the dryline (the analyzed trough extending southwest of the surface low), the TPW products show subtle indications of the relatively drier versus relatively more moist airmasses.  However, in comparison with the ALPW surface to 850 mb layer we see that the ALPW product really shines at highlighting the various airmass differences.  This shouldn’t be surprising since low-level moisture is key for severe thunderstorms, particularly in an environment with an elevated mixed layer as seen in the RAP 2200 UTC 00hr profile from Regina, SK:

Keep in mind that the ALPW product has 4 layers, and they may be viewed for this case here:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/4july20/ALPW_4panel&loop_speed_ms=400

2 key points from this loop:

1) The triple point is very much a low-level feature, it only shows up in a more subtle way in the 850-700 mb layer and does not show up above that.

2) There is rich /deep moisture in the warm sector east of the triple point in a relatively narrow corridor and the storm moves towards this region as it turns to the right (moving southeast in an environment of southwest flow aloft).

One final question worth considering, did the morning convection produce an outflow boundary that moved southwest and contributed to higher moisture / convergence for the afternoon thunderstorms?  There are some indications of that in the moisture products with varying degrees of subtlety.  The GOES-R baseline product seems to show some increase in TPW to the southwest of the morning convection, while the ALPW seems to show some increase in low-level moisture as well.  The following animation supports this hypothesis if we make some continuity assumptions of where the weak outflow boundary would exist, but the key is that it may have reinforced the weak warm front:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/4july20/later_annotations&loop_speed_ms=900

Note the inflow feeder clouds towards the end of the animation, a satellite storm-scale signature that indicates the storm is likely severe.

In summary, we have a triple point pattern that led to a tornadic storm.  often times we think of a triple point pattern with strong convergence along the boundaries and obvious air mass differences, making identification of the triple point pattern relatively easy.  In this case, the winds are much weaker and the boundaries and air mass differences are much more subtle.  It’s definitely more challenging to identify this pattern in these circumstances, however ALPW and other moisture products  help with respect to identification of air mass differences and boundaries (from the PW gradients).

When using the moisture products operationally, latency should be kept in mind.  Let’s first discuss latency as defined as receipt time on AWIPS.  The GOES-R baseline TPW product has the least latency (~15 minutes) with ALPW latency a little over 45 minutes while the merged TPW product is about the same albeit still experimental.  These products can be used for relatively short-fuse type of events, so long as it’s not so short that latency becomes an issue.  Keep in mind, the products that contain POES data have a “hidden latency” in that the most recent passes that are advected to make the products are generally 2 to 6 hours old.  Despite these limitations, the evolution of the triple point is captured quite well in the surface to 850 mb layer ALPW.  Perhaps the GOES product may have shown this as well, however we have the limitation of cloud obscuration and it is a total precipitable water product.  Some of the important changes that occur at lower-levels (therefore captured by the lowest layer ALPW) are “washed out” in a TPW product.

Analysis of June 17, 2020 Wyoming Snow event with JPSS products

By Sheldon Kusselson

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Jun1803Advect_LPW_ALT_anim.gif

May 2020 flooding in central Michigan

This blog entry by Sheldon Kusselson summarizes satellite moisture imagery and products leading to the flood event over central Michigan on 18 May 2020.  Comparison with previous events are included.

Animation of ALPW every 3 hours from 03 UTC 18 May to 09 UTC 19 May 2020:

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Apr3003Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Mar2906Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Jan1209Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2019Sep2306Advect_LPW_ALT_anim.gif

Heavy rain event around 18 May 2020 that contributed to dam failures in Michigan

GOES/JPSS Observations of Oklahoma Severe Storms and Elevated Mixed Layer

By Jorel Torres, Dan Bikos and Ed Szoke

A line of severe storms moved through the southern plains on 4 May 2020, producing numerous hail and wind reports across the region (accessed via SPC). The GOES-16 Day Cloud Phase Distinction RGB is shown below, overlaid onto the GOES-16 CAPE product from 17Z, 4 May 2020 to 00Z, 5 May 2020. Notice how the atmospheric environment in Oklahoma becomes more unstable (2500-3500 J/Kg – peak observations) before convective initiation occurs. A line of agitated cumulus develops around 20Z, 4 May 2020 just north of Oklahoma City, OK. moving east. The RGB observes liquid water clouds (seen in blue) that become glaciated (green), and start to grow rapidly upscale. The rapid vertical development indicates strong updrafts within the embedded line of storm cells, where mid-to-high level ice clouds are depicted in yellows, oranges and reds within the RGB.

Another way to observe atmospheric instability is by using Gridded NUCAPS, that is a product derived from polar-orbiting satellites (i.e. in this case, NOAA-20). Gridded NUCAPS provides users temperature and moisture fields via plan-view and cross-sections. For brevity sake, a plan-view of the temperature field is observed at 1912Z, 4 May 2020, highlighting the 850mb-500mb lapse rate (i.e. temperature change with height). Although Gridded NUCAPS imagery is static (i.e. not an animation), notice how lapse rates steepen with height from central Oklahoma to southern and southwestern Oklahoma, where values range from 6.5C/km to 9C/km. The steeper lapse rates indicate a more unstable environment favorable for severe storms to develop. Conversely weaker lapse rates (less than 5.5C/km; see northeast OK and northwest AR) are a sign of a stable environment. Note stable wave clouds were observed in these respective areas, earlier in the day, due to early morning convection.

But what about the moisture component? Look no further than the Advected Layered Precipitable Water (ALPW) product that helps users inspect precipitable water values in 4 separate layers: surface-850mb, 850mb-700mb, 700-500mb, and 500-300mb. An ALPW animation (click image) is observed below from 16-23Z, 4 May 2020. Moisture is concentrated in the low levels of the atmosphere mainly between surface-850mb. Now to be fair, marginal precipitable water values are observed between 850-700mb early in the day, however dry air moves into this region at ~18Z. 

Now this mid-level dry air appears to indicate the presence of an Elevated Mixed Layer (EML) which can be integral for the severe thunderstorm environment. An EML typically has a steep mid-level lapse rate, mid-level dry air, and a strong capping inversion, inhibiting convection. With an EML in place, this allows the possibility for high amounts of CAPE to exist but the question is whether the inversion can break. In this case, a lifting mechanism was present, a front, to presumably aid in upward forcing (i.e. rising motion associated with converging low level air) to break the inversion, and subsequently generate rapid convective initiation. For interested readers, more information on EMLs and how they can be identified and tracked can be accessed here: (Gitro et al 2019).

Furthermore, note the rapid drying in the moisture profiles of the 12Z and 19Z KOUN RAOB soundings, specifically from 850mb-700mb. Drying corresponds with ALPW 850mb-700mb precipitable water layer. Additionally, observe the steep mid-level and low-level lapse rates (also seen by Gridded NUCAPS above) along with the presence of a strong inversion depicted in the 19Z KOUN sounding. To zoom-in, click on individual soundings.

The EML can also be seen by GOES-16 7.3um from 16Z, 4 May 2020 to 00Z, 5 May 2020. See animation below. The low-level water vapor channel observes a narrow extent of very warm brightness temperatures oriented from southwest-to-northeast from the Texas Panhandle into Oklahoma. The southwest-to-northeast line of very warm temperatures then rapidly cool, due to the front generating upward motion, subsequently eroding the capping inversion, leading to rapid thunderstorm development.

 

Advected Layer Precipitable Water (ALPW) analysis for 30 April 2020 event

Nighttime view of inflow feeder clouds from GOES Nighttime Microphysics RGB

During the overnight hours of March 18-19, 2020, there were numerous reports of severe weather (including tornadoes) in north Texas:

https://www.spc.noaa.gov/climo/reports/200318_rpts.html

This blog entry will focus on the storm repsonsible for the tornado reports between 0615 – 0650 UTC near Abilene, TX.

The storm of interest can be viewed in this 4-panel display zoomed in to the storm near Abilene, TX:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/20mar20/4panel&loop_speed_ms=60

Upper-left: GOES-16 Nighttime Microphysics RGB

Upper-right: GOES-16 IR (10.3 micron) band with default color table

Lower-left: GOES-16 Flash Extent Density grid (1 minute update)

Lower-right: MRMS 0.5 km MSL composite reflectivity

Note the clouds that develop east of Abilene that are circled here:

The clouds are bands oriented parallel to the low-level wind direction and exist in the converging inflow region of the storm.  These are inflow feeder clouds – see this schematic which depicts where they are typically found (to the right of the flanking line in this diagram) and some examples to illustrate how they may appear in visible imagery:

Since the resolution of the IR bands more coarse than that of visible bands, it’s typically more difficult to identify storm scale features seen from satellite at night such as inflow feeder clouds.  In this case, they are not obscured by anvil cirrus and can be seen, albeit not as clearly as they typically appear with visible imagery analysis during daytime.

Let’s zoom in to view a larger perspective of the nighttime microphysics RGB:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/20mar20/nmp&loop_speed_ms=60

Note that a mesoscale domain sector was available at this time, providing 1-minute imagery that was likely crucial for detecting inflow feeder clouds.  How does this compare with other bands and products?

First, the IR imagery with the default color enhancement:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/20mar20/ir_default&loop_speed_ms=60

A different color table applied to the same imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/20mar20/ir_cimss&loop_speed_ms=60

and the fog product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/20mar20/fog&loop_speed_ms=60

The nighttime microphysics product appears to offer the most unambiguous view of the inflow feeder clouds at night.

 

Fall 2019 through Winter 2020 Heavy Precipitation Events

By Sheldon Kusselson

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Jan1503Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Jan1215Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Jan0309Advect_LPW_ALT_anim.gif

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ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2019Oct1518Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2019Oct1615Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2019Oct3015Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2019Oct3115Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2019Dec2303Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2019Dec1715Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Mar0315Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Mar0415Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Mar0515Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Mar0515Advect_LPW_ALT_anim.gif

ftp://ftp.cira.colostate.edu/ftp/Forsythe/LPW/Anim_GIF/2020Mar0615Advect_LPW_ALT_anim.gif

CIRA Snow/Cloud Layer Product & VIIRS observations of the 12 Feb 2020 Blowing Snow Event

By Ed Szoke and Jorel Torres

On 12 Feb 2020 a strong cold front pushed southward across the Northern Plains and Midwest bringing dramatically colder temperatures and howling northerly winds, creating widespread blowing snow and blizzard conditions during the daytime hours of 12 Feb.  While dramatic, such conditions are not unusual for the Northern Plains, where forecasters have noticed that such widespread blowing snow actually appears in GOES-16/17 in some of the bands and RGB products.  For this case there are two excellent blogs out there on this event: one from Carl Jones of the Grand Forks, North Dakota WFO at https://satelliteliaisonblog.com/2020/02/13/arctic-cold-front-blizzard-feb-12-2020/ , the other by Scott Bachmeier of CIMSS at https://cimss.ssec.wisc.edu/satellite-blog/archives/35635.

Here we add to their excellent insight with a look at a couple of other satellite products.  One is the CIRA Snow/Cloud Layer Product, an RGB product that discriminates snow from clouds, but unlike other RGBs the snow appears as white.

Here is a loop of the CIRA Snow/Cloud Layer Product along with a zoomed-in loop of the blowing snow by the Day Snow-Fog RGB from 15-21Z, on 12 Feb 2020.

Here is a METAR time series for Grand Forks, ND.  The cold front comes by in the early hours of 12 Feb followed by a period of light snow and howling northerly winds.  By daybreak the clouds had cleared with visibilities quite low in the widespread blowing snow.

Corresponding animation of surface observations, from 15-21Z, 12 Feb 2020, can be seen below as well.

As noted in the two blogs referenced earlier, we are likely able to see the blowing snow from the snow cover because the blowing snow has smaller sized crystals with different reflectance properties, making it appear differently from the underlying snow in Band 5, which contributes to the two RGB products previously shown.

In addition to the high temporal refresh rate from GOES imagery, polar-orbiting satellites from JPSS can provide observations of surface features at high spatial resolution, in this case, blowing snow. Polar-orbiting satellites, SNPP and NOAA-20 contain the VIIRS instrument, comprising 22 spectral channels that exhibit 375-m and 750-m spatial resolutions.

The 1.6µm channel from NOAA-20 VIIRS (I-3) and the GOES 1.6µm (Band 5, previously mentioned above) are known for discriminating cloud phase: liquid water clouds (reflect at 1.6µm) versus ice clouds (absorb at 1.6µm). The 1.6µm spectral channel can also provide land/water contrast in the imagery, depict snow cover (i.e. black colors, since snow absorbs rather than reflects at 1.6µm) and observe blowing snow (i.e. greyish-white colors exhibiting varying reflective properties compared to snow cover).The main difference between GOES 1.6µm and NOAA-20 VIIRS 1.6µm is the spatial resolution: 1-km compared to 375-m, respectively. NOAA-20 VIIRS imagery, below, observes blowing snow at 1815Z, 12 Feb 2020.

Although polar-orbiters have a coarser temporal resolution compared to GOES, there were three polar-orbiting overpasses that observed the blowing snow event; 2 from NOAA-20, and 1 from SNPP. See video below; notice how blowing snow moves to the south throughout the animation. Observations were taken between 1815Z-1951Z, 12 Feb 2020.

 

Advected Layer Precipitable Water (ALPW) product for 5 February 2020 Severe and Flood event

Thin fog over snow in southwest Kansas – 29 January 2020

A snow event in southwest Kansas on 28/29 January 2020 led to widespread snow amounts of 4 to 8 inches, with locally higher amounts (above 12″).  On the 29th, a thin layer of fog developed over the snow covered land.  Inspect the GOES-16 imagery:
http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29jan20/4panel&loop_speed_ms=60

Upper left: Visible (0.64 micron) band

Upper right: CIRA Snow Cloud Layers RGB

Lower left: Day Snow Fog RGB

Lower right: Day Cloud Phase Distinction RGB

In the visible imagery, the fog is undetectable due to the lack of contrast (the snow cover and fog are the same color).

In the CIRA Snow Cloud Layers RGB, snow cover is white while  low-cloud/fog is yellowish-green.  Note how we can see through the fog since it is thin, the snow cover on the ground can be seen.

In the Day Snow Fog RGB, snow cover is red, low clouds / fog are light purple.  The transparency in this product is less than the previous RGB so that the snow cover under the fog is somewhat more subtle.

In the Day Cloud Phase Distinction RGB, snow cover is green, low-cloud/fog is cyan and there is sufficient transparency to view the snow cover under the thin fog.

The key takeaway point is to make use of RGB products to discern fog from snow cover, the visible imagery alone makes it much more challenging to make this discrimination.

As an example of data fusion, note this tweet from the NWS WFO in Dodge City which confirms the fog via web-cams:

Thin fog over snow in southwest Kansas – 29 January 2020

A snow event in southwest Kansas on 28/29 January 2020 led to widespread snow amounts of 4 to 8 inches, with locally higher amounts (above 12″).  On the 29th, a thin layer of fog developed over the snow covered land.  Inspect the GOES-16 imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29jan20/4panel&loop_speed_ms=60

Upper left: Visible (0.64 micron) band

Upper right: CIRA Snow Cloud Layers RGB

Lower left: Day Snow Fog RGB

Lower right: Day Cloud Phase Distinction RGB

In the visible imagery, the fog is undetectable due to the lack of contrast (the snow cover and fog are the same color).

In the CIRA Snow Cloud Layers RGB, snow cover is white while  low-cloud/fog is yellowish-green.  Note how we can see through the fog since it is thin, the snow cover on the ground can be seen.

In the Day Snow Fog RGB, snow cover is red, low clouds / fog are light purple.  The transparency in this product is less than the previous RGB so that the snow cover under the fog is somewhat more subtle.

In the Day Cloud Phase Distinction RGB, snow cover is green, low-cloud/fog is cyan and there is sufficient transparency to view the snow cover under the thin fog.

The key takeaway point is to make use of RGB products to discern fog from snow cover, the visible imagery alone makes it much more challenging to make this discrimination.

As an example of data fusion, note this tweet from the NWS WFO in Dodge City which confirms the fog via web-cams:

 

Snow Squalls in Pennsylvania on 8 January 2020

By Dan Bikos and Bill Line

On 8 January 2020 numerous snow squalls moved across Pennsylvania leading to the issuance of multiple snow squall warnings by the NWS.  One of the challenges with this event is that it spanned the time period around sunrise.  Obviously radar and IR satellite imagery is not affected by this, but the Day Cloud Phase Distinction RGB, which has been shown to be quite useful for snow squall events, can only be used during the daytime.  One alternative to continue monitoring during this time period is to make use of the nighttime microphysics RGB at night.  This RGB product makes use of the 10.3 minus 3.9 micron band difference for the green component which discriminates between water (positive) and ice (negative) clouds at night.

The following 4 panel GOES-16 loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8jan20/original&loop_speed_ms=120

Upper-left: Nighttime Microphysics RGB

Upper-right: Daytime Cloud Phase Distinction RGB

Lower-left: MRMS 1-km composite reflectivity

Lower right:   IR (10.3 micron) band with METARs

First look at the Daytime Cloud Phase Distinction RGB in the upper-right, later in the loop when there is daylight this product does a good job in delineating which clouds are glaciated (green and also orange/red shades) versus water clouds (cyan/lavendar).  Compare the cloud tops that are glaciated with MRMS reflectivity and we see that there is good agreement in delineating the more intense snow squalls from the less intense regions of snow.

Now trace backwards in time these glaciated clouds through the sunrise period into the overnight hours and notice how they appear in the nighttime microphyics RGB in the upper-left.  The glaciated clouds appear tan to darker shades of red and also correspond pretty well with regions of higher reflectivity observed from MRMS.

We include IR imagery (with METARs) to illustrate only a loose correspondence between colder cloud tops and higher regions of reflectivity, the RGB products definitely do a better job since they can delineate ice versus water cloud tops.

Next, we’ll focus in on a modified Day Cloud Phase Distinction RGB product that allows you to see further towards the nighttime hours compared to the default product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8jan20/modified&loop_speed_ms=120

Upper-left: Nighttime Microphysics RGB

Upper-right: Modified Daytime Cloud Phase Distinction RGB

Lower-left: MRMS 1-km composite reflectivity

Lower right:   IR (10.3 micron) band with METARs and GLM Flash Extent Density

The modifications to the Daytime Cloud Phase Distinction RGB are the same as shown in this blog entry.

Compare the modified Daytime Cloud Phase Distinction RGB to the default in the previous loop and notice that you get more images towards the nighttime hours around sunrise.

We included the GLM Flash Extent Density data as an overlay in the lower-right panel, note that there was some lightning activity associated with some of the snow squalls which further increases confidence that these were high-impact snow squalls.

A social media post illustrates the significant impacts of these snow squalls.

Later the same day during the early afternoon, a snow squall quickly moved through Philadelphia, prompting the issuance of a snow squall warning by the local NWS office. The Day Cloud Phase Distinction RGB at 1-min temporal resolution provides an alternative/supplement to radar imagery for tracking the precise evolution of the snow squall (see animation immediately below). Considering the environment in which we are viewing this RGB, the combination of convective cloud elements, cloud top glaciation, and rapid motion all apparent in the 1-min RGB imagery indicate snow squall potential with this feature.

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training\line\loops\20191218_dcpdrgb&loop_speed_ms=60

Nearby surface observations measured wind gusts to 46 mph in associated with the snow squall, and photos on social media show the approach of the snow squall into Philadelphia.

 

Elevated Mixed Layer event on 16 December 2019

On 16 December 2019, SPC issued an enhanced risk of severe thunderstorms for portions of Louisiana and Mississippi:

One of the favorable ingredients for this severe weather setup was the presence of an Elevated Mixed Layer (EML) which is depicted in the 12Z Jackson, MS sounding:

The EML is bounded between the capping inversion around 750 mb and the higher RH around 500 mb.  Within this layer, the relative humidity is quite low with a source region off the elevated plateau of northern Mexico.  Lapse-rates within this layer are relatively high, 700-500 mb lapse rate is 7.6 degrees C / km and the maximum is shown in the magenta layer on the sounding of 8.1 degrees C / km.

At times, the EML may show itself as a region of warmer brightness temperatures in the GOES 7.3 micron imagery.  In this particular case, the region of warmer brightness temperatures may be traced back to diurnal heating from the previous day over the elevated Mexican plateau and advecting northeastward across Texas, Louisiana and Mississippi:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16dec19/band10&loop_speed_ms=150

Clouds develop during the early morning hours which obscure the EML signature, however it may still be tracked via the Advected Layer Precipitable Water (ALPW) product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16dec19/alpw&loop_speed_ms=150

ALPW is a product based on microwave instruments aboard polar satellites which can see through most clouds.  The 700-500 mb layer is useful in that it depicts the source region of the dry air associated with the EML and it can be tracked towards Mississippi.  This dry air exists underneath a moist air mass with origins from the Gulf of Mexico as seen in ALPW in the lower layers.  ALPW allows a four dimensional perspective of assessing moisture in the vertical in time.

More information on EML tracking via satellite imagery and products:

http://rammb.cira.colostate.edu/training/visit/training_sessions/tracking_the_elevated_mixed_layer_with_a_new_goes_r_water_vapor_band/

Australian Wildfires

Nighttime visible imagery (i.e.Near-Constant Contrast or NCC) clearly shows the Australian wildfires that are raging and ablaze in New South Wales, a state in southeastern Australia. NCC detects emitted lights from the fires, and at times shows the fire perimeter lines and reflected light from the extensive smoke plumes. The animation below displays nightly Suomi-National Polar-orbiting Partnership (SNPP) NCC imagery between 13-15Z from 11-13 November 2019. Cloud cover and emitted city lights from Sydney and Brisbane can also be seen. The moon phase and moon elevation angle are depicted in the bottom-left corner. The full moon phase of the lunar cycle and positive moon elevation angle imply the moon was above the horizon and provided adequate moonlight to illuminate atmospheric features (i.e. an ideal time to view NCC).

 

 

The smoke from the fires can be accentuated further utilizing an NCC enhancement technique. The AWIPS NCC color table scale can be customized to bring out certain atmospheric features in the imagery. In the images below, the default NCC 0-1 enhancement is compared to the NCC 0-0.5 enhancement on 12 November 2019. Notice how the NCC 0-0.5 enhancement brings out the smoke from the fires along with nearby cloud cover, but increases the saturation of city lights in comparison to the NCC 0-1 enhancement.  For interested users, this quick guide provides steps on how to apply different enhancements to NCC imagery.

But how can one differentiate between emitted lights from fires to the emitted city lights? Users can overlay NCC with VIIRS infrared 3.74µm to identify fire hotspots at 375-m spatial resolution. NCC and VIIRS IR imagery are observed at ~1430Z, 13 November 2019 below. Hotspots (i.e. high brightness temperatures) and outlines of fire perimeters coincide with certain emitted lights. The emitted lights from cities disappear when compared to the thermal infrared. 

 

To get an idea of the atmospheric instability near the fires users can display NUCAPS soundings; polar-orbiting satellite derived soundings that are optimal in clear-sky environments, and where in-situ observations are poor or limited. The NUCAPS sounding that is picked for this event is circled in blue (see below), observed right over one of the fires. It shows a moderately unstable, dry environment conducive for fire initiation and fire spread, where precipitable water values (derived from the sounding) are 0.31 inches. Note NUCAPS soundings are overlaid onto NCC imagery on 14 November 2019. NCC imagery observes smoke advection towards the coastline.

Downwind of the fire, another NUCAPS sounding is observed near the coastline (see blue circle). NUCAPS depicts a shallow inversion indicating a stable environment. Sounding is predominately dry, albeit there is slightly more moisture observed near the surface; increased precipitable water values (i.e. 0.55 inches) could be due to maritime influences and/or ‘fire produced water vapor advection’ towards the coastline.

Australian Wildfires

Nighttime visible imagery (i.e. Near-Constant Contrast or NCC) clearly shows the Australian wildfires that are raging and ablaze in New South Wales, a state in southeastern Australia. NCC detects emitted lights from the fires, and at times shows the fire perimeter lines and reflected light from the extensive smoke plumes. The animation below displays nightly Suomi-National Polar-orbiting Partnership (SNPP) NCC imagery between 13-15Z from 11-13 November 2019. Cloud cover and emitted city lights from Sydney and Brisbane can also be seen. The moon phase and moon elevation angle are depicted in the bottom-left corner. The full moon phase of the lunar cycle and positive moon elevation angle imply the moon was above the horizon and provided adequate moonlight to illuminate atmospheric features (i.e. an ideal time to view NCC).

 

The smoke from the fires can be accentuated further utilizing an NCC enhancement technique. The AWIPS NCC color table scale can be customized to bring out certain atmospheric features in the imagery. In the images below, the default NCC 0-1 enhancement is compared to the NCC 0-0.5 enhancement on 12 November 2019. Notice how the NCC 0-0.5 enhancement brings out the smoke from the fires along with nearby cloud cover, but increases the saturation of city lights in comparison to the NCC 0-1 enhancement.  For interested users, this quick guide provides steps on how to apply different enhancements to NCC imagery.

 

But how can one differentiate between emitted lights from fires to the emitted city lights? Users can overlay NCC with VIIRS infrared 3.74µm to identify fire hotspots at 375-m spatial resolution. NCC and VIIRS IR imagery are observed at ~1430Z, 13 November 2019 below. Hotspots (i.e. high brightness temperatures) and outlines of fire perimeters coincide with certain emitted lights. The emitted lights from cities disappear when compared to the thermal infrared. 

 

To get an idea of the atmospheric instability near the fires users can display NUCAPS soundings; polar-orbiting satellite derived soundings that are optimal in clear-sky environments, and where in-situ observations are poor or limited. The NUCAPS sounding that is picked for this event is circled in blue (see below), observed right over one of the fires. It shows a moderately unstable, dry environment conducive for fire initiation and fire spread, where precipitable water values (derived from the sounding) are 0.31 inches. Note NUCAPS soundings are overlaid onto NCC imagery on 14 November 2019. NCC imagery observes smoke advection towards the coastline.

Downwind of the fire, another NUCAPS sounding is observed near the coastline (see blue circle). NUCAPS depicts a shallow inversion indicating a stable environment. Sounding is predominately dry, albeit there is slightly more moisture observed near the surface; increased precipitable water values (i.e. 0.55 inches) could be due to maritime influences and/or ‘fire produced water vapor advection’ towards the coastline.

Lake-effect snow from 7 November 2019

GOES-16 imagery depicts lake-effect snowbands over the western Great Lakes on 7 November 2019:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/7nov19/4panel&loop_speed_ms=60

Upper left: 0.64 micron visible band

Upper right: 10.3 micron IR band with default color table (IR_Color_Clouds_Winter)

Lower left: 10.3 micron IR band with GOES Snow Squall color table

Lower right: Day Cloud Phase Distinction RGB

The 0.64 micron visible band provides detailed information on the location of the band, but reflectance values do not provide information about the intensity of the band.

IR imagery at 10.3 microns provides cloud top brightness temperatures, however lake-effect snowbands are typically shallow so that the difference in a precipitating lake-effect band and non-precipitating band is small.  The small difference may not even be easy to identify if the color table has insufficient contrast around those brightness temperatures, as we see in the IR imagery with the IR_Color_Clouds_Winter color table.  The IR imagery with the GOES Snow Squall color table helps somewhat but does not provide information on glaciation like the next product.

Finally, the Day Cloud Phase Distinction RGB provides the most useful information at a glance for assessing lake-effect snowbands.  Not only does it identify the location of the snowbands, but also indicates clouds that are glaciated versus clouds that are not glaciated due to the 1.6 micron band.  In this RGB product, glaciated clouds have transitioned from light blue to green (and perhaps even yellow if more developed although not shown in this case).  This allows one to identify clouds that have glaciated and thus much more likely to be producing precipitation, even heavier precipitation if conditions are favorable.

The sounding from Alpena, MI provides useful information about the environment:

The sounding supports lake-effect snow in the region with the deeper PBL and large low-level lapse rates under the capping inversion.  Note the winds are northwest in this layer, indicating this airmass was modified by sensible and latent heat fluxes from Lakes Superior and Michigan.

Lake-effect snow from 7 November 2019

GOES-16 imagery depicts lake-effect snowbands over the western Great Lakes on 7 November 2019:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/7nov19/4panel&loop_speed_ms=60

Upper left: 0.64 micron visible band

Upper right: 10.3 micron IR band with default color table (IR_Color_Clouds_Winter)

Lower left: 10.3 micron IR band with GOES Snow Squall color table

Lower right: Day Cloud Phase Distinction RGB

The 0.64 micron visible band provides detailed information on the location of the band, but reflectance values do not provide information about the intensity of the band.

IR imagery at 10.3 microns provides cloud top brightness temperatures, however lake-effect snowbands are typically shallow so that the difference in a precipitating lake-effect band and non-precipitating band is small.  The small difference may not even be easy to identify if the color table has insufficient contrast around those brightness temperatures, as we see in the IR imagery with the IR_Color_Clouds_Winter color table.  The IR imagery with the GOES Snow Squall color table helps somewhat but does not provide information on glaciation like the next product.

Finally, the Day Cloud Phase Distinction RGB provides the most useful information at a glance for assessing lake-effect snowbands.  Not only does it identify the location of the snowbands, but also indicates clouds that are glaciated versus clouds that are not glaciated due to the 1.6 micron band.  In this RGB product, glaciated clouds have transitioned from light blue to green (and perhaps even yellow if more developed although not shown in this case).  This allows one to identify clouds that have glaciated and thus much more likely to be producing precipitation, even heavier precipitation if conditions are favorable.

The sounding from Alpena, MI provides useful information about the environment:

The sounding supports lake-effect snow in the region with the deeper PBL and large low-level lapse rates under the capping inversion.  Note the winds are northwest in this layer, indicating this airmass was modified by sensible and latent heat fluxes from Lakes Superior and Michigan.

Water vapor imagery in an extremely dry airmass – 31 October 2019

On 31 October 2019 a very dry airmass existed over the southwest US.  To illustrate the dry airmass, consider the sounding from Albuquerque, NM with a precipitable water amount of 0.05″ (1.27 mm):

The synoptic scale pattern was characterized by above normal PW in the east ahead of a trough, with below normal PW in the west behind the trough, as seen in the CIRA experimental TPW product:

This experimental product combines retrievals from both polar orbiting and GOES satellites.  Contrast this with the current operational blended TPW product on AWIPS which uses only polar orbiting satellite data.

Next let’s consider the GOES water vapor bands, normally the upper 2 water vapor bands (at 6.9 and 6.2 microns) do not see all the way to the surface due to water vapor absorption.  Recall that on occasion the low-level water vapor band at 7.3 microns may see to the surface depending on how dry it is.  In this extremely dry airmass over elevated terrain, what do the GOES water vapor bands show?

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/31Oct19/wv&loop_speed_ms=60

Upper left panel:  GOES-16 Water vapor band at 7.3 microns

Upper-right: GOES-16 Water vapor band at 6.9 microns

Lower-left: GOES-16 Water vapor band at 6.2 microns

Lower-right: GOES-16 GeoColor

Note that we observe clear skies across this scene in the GeoColor product.

In the 7.3 micron (low-level) water vapor band, we readily observe the earth’s surface as it warms with daytime heating.  You can compare the terrain features seen in this imagery with what is shown in GeoColor.  The warmest brightness temperatures are observed due to a combination of locally highest elevations and a relative minimum in TPW.  In the 6.9 micron imagery we observe brightness temperature maxima where we see mountain peaks across various ranges, particularly in New Mexico.  Even at 6.2 microns we observe indications of diurnal heating and warmer brightness temperatures along various peaks in New Mexico.

To help explain why the imagery sees all the way to the earth’s surface we analyze the weighting function profile from the Albuquerque sounding (courtesy of CIMSS – https://cimss.ssec.wisc.edu/goes/wf/):

Typically the weighting function profile for water vapor bands on GOES does not extend all the way to the surface, however due to the extremely dry airmass in place, the 7.3 and 6.9 micron bands have contributions from the earth’s surface.  In fact, the 6.2 micron band even had contributions at elevations just above Albuquerque which explains why higher mountain peaks, which would be above 850 mb, are being observed in the 6.2 micron imagery.

The 1.38 micron “cirrus” band also is significantly affected by water vapor absorption, which is why this band is typically used to highlight cirrus clouds since the earth’s surface is generally not seen due to significant water vapor absorption. In this particular case with an extremely dry airmass, the earth’s surface shows up readily across the region of interest:

21 October 2019 – nighttime detection of fog and outflow boundaries

During the overnight hours of 21 October 2019, we analyze multiple applications of GOES imagery at night.

First, we look over the northeast where fog developed.  Here is the GOES-16 nighttime microphysics product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21Oct19/fog&loop_speed_ms=60

We observe large areas of fog (dull aqua) or low clouds (aqua) in Pennsylvania, New York, Vermont, New Hampshire and Massachusetts.  Fog development in river valleys is particularly more easy to identify.  Focus in over New York:

where the loop shows the westward movement of fog / low cloud from the Mohawk River Valley westward towards Lake Ontario.  We also see the development of fog along Seneca Lake and Cayuga Lake.

Further southwest in Texas, severe thunderstorms developed during the evening hours of 20 October that continued through the overnight hours.  The following GOES-16 imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21Oct19/outflow&loop_speed_ms=80

is a 4 panel display:

Upper-left: Split Window Difference / IR (10.3 micron) combination product

Upper-right: Nighttime microphysics RGB

Lower-left: IR (10.3 microns) with the default color table (IR_Color_Clouds_Winter)

Lower-right: Night Fog product (10.3 minus 3.9 micron)

Analyze the southern and western flanks of thunderstorms for regions of outflow (cooler brightness temperatures).  Outflow boundaries can be important to analyze for mesoanalysis and potential future convection developing along or interacting with these boundaries.

Which of these imagery / products can you best identify the thunderstorm outflow air mass / boundaries with?  Note that in some regions there are low clouds associated with the outflow.  The IR imagery with the default color table has less contrast compared to other imagery / products for the detection of regions of outflow.  Remember to look at  other products for outflow detection at nighttime besides the IR (10.3 micron) band alone.

 

CIRA ALPW Comparison for Two Northeast US Heavy Precipitation Events

By Sheldon Kusselson

ALPW loop of 2019 event:

ALPW loop of 2017 event:

Subtropical storm Melissa

By Sheldon Kusselson and Dan Bikos

Subtropical storm Melissa exists off the Eastern coastline of the U.S. on 10-11 October 2019, as GOES-16 visible imagery on 11 October shows:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11Oct19/vis&loop_speed_ms=60

note the lack of deep convection over the center of the circulation, however convection does exist north and northeast of the center at this time.

Another perspective on this storm can be seen on the Advected Layer Precipitable Water (ALPW) product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11Oct19/ALPW&loop_speed_ms=200

The ALPW product depicts precipitable water in 4 layers.

Upper left (Surface to 850 mb), Upper right (850 to 700 mb), Lower left (700 to 500 mb), Lower right (500 to 300 mb).

Note the advection of subtropical moisture from two distinct areas in the Atlantic at the two lowest layers and at least one of the two highest layers. Dry areas are probably the blocks to the low off the East Coast.

The storm has brought rainfall from coastal New Jersey to Massachusetts, the moisture associated with this rainfall can also be viewed in a Total Precipitable Water (TPW) product. Here we show the experimental merged TPW product which makes use of observations from both microwave instruments on multiple polar orbiting satellites and the GOES-16 ABI:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11Oct19/merged_TPW&loop_speed_ms=200

 

JPSS/GOES Fire Detection Capabilities – Swan Lake Fire, AK

The Swan Lake Fire, located in the Kenai National Wildlife Refuge, south of Anchorage, AK initiated in June 2019 due to lightning. Over the past few months, the fire has steadily grown, and as of 20 August 2019, more than 130,000 acres have burned.

To get a close look at the fire refer to the following comparison (see imagery below) at 2248Z, 19 August 2019. Imagery compares SNPP VIIRS 3.7μm to the GOES-17 3.9μm infrared imagery. GOES 3.9μm is at 2-km while the VIIRS 3.7μm is at 375-m spatial resolution. Notice the fine details of the fire areal extent seen by VIIRS, exhibiting warm brightness temperatures (i.e. fire hotspots seen in black). In contrast, the GOES imagery does not capture the intricate fire perimeter, due to its coarser resolution and that it is affected by parallax. Parallax consists of the satellite displacement of features in the imagery, where geostationary observations over northern latitudes are far away from the GOES satellite subpoint (i.e. nadir). The geostationary imagery results in elongated pixels over the fire producing ambiguity in the fire location and perimeter.

Although GOES-17 has a coarser temporal resolution, the animation below highlights the fire at a finer temporal resolution (i.e. 1-minute data) from 2230-2330Z, on 19 August 2019. The visible (0.64μm) and infrared (3.9μm) imagery shows the evolution of the fire location, fire hotspots, and corresponding smoke. Surface observations are overlaid onto the imagery to highlight the air temperature/dewpoint, wind direction and speed, along with smoke and haze identifiers.

[Click animation link] ftp://rammftp.cira.colostate.edu/torres/JPSS_Blog_Swan_Lake_Fire_AK/g17_animation.mp4

To get an idea of the atmospheric environment aloft and near the surface, users can refer to satellite derived NUCAPS soundings that provide temperature and moisture profiles. Remote areas that lack RAOB observations can take advantage of NUCAPS soundings in the operational forecasting environment. The closest NUCAPS profile in proximity to the fire is chosen below (i.e. green dot encompassed by the white circle) at ~23Z, 19 August 2019.

The 23Z NUCAPS and 00Z, 20 August 2019 RAOB sounding from Anchorage, AK are compared below. The NUCAPS and RAOB soundings are ~45 miles apart from each other, where NUCAPS soundings provide a volumetric measurement of the atmosphere and ‘smoothes’ (i.e. averages) the temperature/dewpoint measurements within the profile. In contrast, RAOBs produce measurements along a ‘point’ throughout the atmosphere, producing a finer vertical resolution. Note the RAOB observation provides wind data (surface and aloft), while NUCAPS does not provide wind measurements.

The RAOB sounding observes a weak inversion containing a dry boundary layer and light surface winds, keeping smoke in the lower atmosphere. Precipitable water values are also low in both NUCAPS (0.41 inches) and RAOB (0.53 inches) observations, indicating a relatively dry atmosphere. The NUCAPS profile provides a general idea of what the atmosphere is like, and is sampled closest to the fire in comparison to RAOB (i.e. sounding further away from the fire). However, NUCAPS misses the low-level inversion along with the higher moisture content observed in the mid-levels, noticed by RAOB measurements.

Lastly, one cannot forget the VIIRS Near-Constant Contrast (NCC) product that provides a nighttime visible capability in support of active fires. From the SNPP VIIRS overnight pass at 1235Z, 20 August 2019, the NCC observes the emitted lights produced from the fire, along with the emitted city lights. But how can users decipher between the two features? The images below compare NCC to VIIRS 3.7um to address the question.  Note the emitted lights from the fires correspond with the fire hotspots (high brightness temperatures seen in black), where the emitted city lights do not exhibit this correlation. Fire is observed in between Sterling and Cooper Landing, Alaska. Emitted city lights can be seen from Nikiski to Soldtona, AK and up north, near Anchorage, AK.

VIIRS flood observations along the Arkansas River

Heavy rain fell in Kansas, Oklahoma and Arkansas the past few weeks, causing major flooding along portions of the Arkansas River. In the RealEarth image below (i.e. 1930Z on 27 May 2019), major flooding is indicated in orange and red colors and extends from Fort Gibson in northeast Oklahoma to New Blaine in northwest Arkansas.

In the example above, satellite observations are employed to identify the inundated areas, where the Visible Infrared Imaging Radiometer Suite (VIIRS) Flood Areal Extent is utilized. Product is at 375-m spatial resolution and is available for forecasters via Local Data Manager (LDM).

A VIIRS Flood Areal Extent animation is also provided (see below) from 23-28 May 2019, highlighting the flooding along the Arkansas River. The VIIRS Flood Areal Extent discriminates between different scene types (i.e. MS = missing data (black), LD = land (brown), SI = supra-snow ice (mixed ice and water, or water over ice denoted in purple), SN = snow (white), IC = ice (aqua), Cl = clouds (grey), CS = cloud shadows (dark grey), WA = open water (blue)). The product also calculates the floodwater fraction percentage of a pixel (e.g. the product determines if a pixel is 20%, 40%, 100% flooded). The floodwater fraction percentage is from 0-100% and ranges from green-to-red colors. Notice in the animation, the evolution of the flooding along the Arkansas River and the increased flooding near Fort Smith, AR.

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/JT_loops/Arkansas_River_Flooding/

For additional perspective on how much rain accumulated over the area, Advanced Hydrologic Prediction Service (AHPS) 7-day and 14-day observed and normal (i.e. average) precipitation images are shown below at 12Z on 30 May 2019. Observed precipitation is expressed as gridded data with a spatial resolution of 4 kilometers, where precipitation is represented in inches. Notice the high precipitation amounts scattered throughout northeastern Oklahoma and northwest Arkansas over the 7-day and 14-day periods, and how observed precipitation values are significantly higher than their respective 7-day and 14-day normal precipitation values. Maximum 7-day and 14-day observed precipitation reached ~5-6 inches and 10+ inches respectively. The 30-day observed and normal precipitation values (not pictured here) also inferred that soils were saturated, suggesting a conducive environment for flooding as well.

7-Day Observed (left) and Normal (right) Precipitation Values

14-Day Observed (left) and Normal (right) Precipitation Values

More flooding along the Arkansas River is expected throughout the next week, where the latest flooding updates can be accessed via the following National Weather Service (NWS) link.

VIIRS observations of Katabatic Winds from the Transcontinental Mountain Range Adjacent to the Ross Ice Shelf in Antarctica

By Lewis Grasso and Jorel Torres

One of the goals of the JPSS program set forth by NOAA is enhanced monitoring of the Earth’s environment. One specific type of event of the Earth’s environment that was captured by VIIRS on-board not only the operational NOAA-20 satellite platform, but also the demonstration S-NPP satellite platform was katabatic winds. Katabatic winds that flow through the glacial canyons of the Transcontinental Mountain Range represent a wind regime that transports some of the coldest surface air off the Antarctic ice sheet to the Ross Ice Shelf.

In the figure below a few key features are annotated. The glacial canyons where the katabatic winds flow along the Ross Ice Shelf are denoted. Furthermore, McMurdo research facility is also annotated. As a side note, McMurdo is one of the locations where VIIRS data is downloaded; Svalbard, Norway is the second location. Annotations in the figure are superimposed on top of Imagery Band (I-5, 11.45um), which has a 375-m sub-satellite footprint.

VIIRS offers high-resolution imagery as a means to monitor local-environments, however still images may limit interpretation of the imagery. The following sets of animations provide a GOES-16/17 ABI-like loops from combination of both S-NPP and NOAA-20 VIIRS instruments.

Animation 1: 29 April 2019  (click following link)

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/JT_loops/Antarctic_Katabatic_Winds/04292019/

Animation 2: 2 May 2019  (click following link)  http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/JT_loops/Antarctic_Katabatic_Winds/05022019/

Animation 3: 3 May 2019  (click following link)  http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/JT_loops/Antarctic_Katabatic_Winds/05032019/

 

For the interested reader here are some references.

The entire February 2003 MWR Volume One article is the following: Antarctic Satellite Meteorology: Applications for Weather Forecasting.

https://doi.org/10.1175/1520-0493(2003)131<0371:ASMAFW>2.0.CO;2

Besides the February 2003 MWR Volume, we offer the following articles.

A Strong Wind Event on the Ross Ice Shelf, Antarctica: A Case Study of Scale Interactions

https://doi.org/10.1175/MWR-D-15-0002.1

Circumpolar Mapping of Antarctic Coastal Polynyas and Landfast Sea Ice: Relationship and Variability

https://doi.org/10.1175/JCLI-D-14-00369.1

Insight into the Thermodynamic Structure of Blowing-Snow Layers in Antarctica from Dropsonde and CALIPSO Measurements

https://doi.org/10.1175/JAMC-D-18-0082.1

Numerical Prediction of an Antarctic Severe Wind Event with the Weather Research and Forecasting (WRF) Model

https://doi.org/10.1175/MWR3459.1 

Dryline Bulges Identified in GOES-16 Split Window Difference on 30 April 2019

By Dan Bikos and Lewis Grasso

During the afternoon of 30 April 2019, a dryline mixed eastward from New Mexico into the Texas panhandle, as seen in this GOES-16 visible loop with METARs overlaid:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30apr19/vis_metars&loop_speed_ms=60

Thunderstorms initiate along various segments of the dryline during the animation.

The moisture gradient is substantial across the dryline so we may expect to see this in the GOES Split Window Difference (SWD) (10.3 minus 12.3 micron) product as well:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30apr19/4panel_swd&loop_speed_ms=60

Upper left: CIRA SLIDER default enhancement applied to the SWD product

Upper right: AWIPS default enhancement (dust_and_moisture_split_window) applied to SWD product

Lower left: Linear enhancement with a modified range of 0 to 8 degrees Celsius applied to the SWD product

Lower right: Linear enhancement with a modified range of 0 to 5 degrees Celsius applied to the SWD product

Water vapor in the boundary layer is an absorbing gas to energy at 10.3 and 12.3 microns that is emitted from the earth’s surface.  Water vapor absorbs more energy at 12.3 compared to 10.3 microns; therefore, when the temperature decreases with height the brightness temperature at 12.3 microns is less than the brightness temperature at 10.3 microns, hence the difference is positive.  The magnitude of the SWD is greater on the moist side of the dryline compared to the dry side.

The above animation shows why it’s important to experiment with different color tables and range when looking at imagery.  Certain features of interest may stand out more than others.  For example, since there is a temperature dependence in the SWD, there is a diurnal variation in the animation as we approach sunset in the later half.  The diurnal variation may mask the moisture variation of interest, this is particularly true of the color tables in the top two panels, whereas the diurnal variation is not as obvious in the bottom two panels.

One feature that the SWD product really highlights more than visible imagery is the fact that there are smaller scale bulges along the dryline.  These are important as they may be indications of localized moisture convergence which may trigger convective initiation.  The smaller scale bulges are annotated with white arrows on the image below:

In the loop, notice that these smaller scale dryline bulges appear earlier in the bottom two panels compared to the top two.

The visible imagery shown earlier does show indications of some cumulus along segments of the dryline, so are these regions actually more moist or being obscured by clouds as seen in the GOES imagery?  To help answer that question we introduce surface observations from the west Texas Mesonet. Below is a zoomed in SWD image using the linear color table and range of 0 to 8 degrees Celsius at 22:06 UTC.  At that time, there are two dryline bulges evident in the SWD product:

The SWD product suggests that Denver City, TX is still on the moist side of the dryline while Morton, TX is on the dry side.  The meteogram below is from the observations at those locations, red arrows for the timeline in the Morton meteogram indicate the time of the above image (near 2200 UTC):

Meteograms courtesy of the West Texas Mesonet.

The dewpoint (shown in the solid green curve) clearly drops at Morton, TX BEFORE it does so in Denver City, TX.  This is confirmation that the features identified in the GOES-16 SWD product are indeed associated with smaller scale dryline bulges.  In fact, note the winds at Morton, TX increase in speed and veer in direction before it does so in Denver City, TX.

We conclude with a comparison of the GOES-16 SWD product with other familiar bands (0.64 micron visible, 10.3 micron IR, and 7.3 micron water vapor):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30apr19/4panel_B10_B13_B02_SWD&loop_speed_ms=60

How well do the dryline bulges discussed above show up in other bands?

Which bands can you see outflow from the southernmost storm in?

17 April 2019 thunderstorm event over northern Mexico as observed by GOES-16

By Louie Grasso and Dan Bikos

On the day of 17 April 2019 observations indicated a significant upper-level trough over the southwest portions of the US.  As is typical with this type of synoptic setup, southwesterly flow ahead of the trough existed over northern Mexico extending northeastward into Texas.  In addition, this synoptic setup is associated with the development of a dryline in Texas and northern Mexico.  In the following animation:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/17apr19/4panel_SWD_vis_B10_AM_early&loop_speed_ms=60

Upper left:  GOES-16 Split Window Difference (SWD; 10.3 – 12.3 micron) with METARs

Upper right: GOES-16 Visible (0.64 micron)

Lower left: GOES-16 Low-level water vapor band (7.3 micron)

Lower right: GOES-16 Air Mass RGB product

the METARs indicate advection of warm moist air from the Gulf of Mexico with dewpoints in the upper 60s along with drier southwesterly flow with dewpoints in the low 20s over southwest Texas and northern Mexico.  Several features are evident in the SWD product that are absent from the visible imagery.  For example, a moist boundary layer is displayed by deep orange/red colors ; there are two regions of blowing dust displayed as blue/purple, as shown below:

In advance of the synoptic scale trough, warm dry air is seen in the GOES-16 7.3 micron band imagery advecting northward from Mexico (yellow color) into central Texas and is associated with an Elevated Mixed Layer (EML) as confirmed by the morning Del Rio, TX sounding:

The air mass RGB product also shows the dryline quite clearly, in support of the SWD product.  The above animation ends in early afternoon prior to convective initiation.

A few sequence of events are evident in the above animation that are associated with changes in the pre-storm environment that may lead to convective initiation.  First, in the 7.3 micron imagery a region of cooling occurred coincident with the moist boundary layer in the SWD product (see annotated figure above).  In particular, the western edge of the cooling is stationary at 7.3 micron while the eastern edge was advected northeastward with the mean flow.

In the above image, which shows topography, the western edge of the moist layer is annotated with the conventional dryline symbol.  Although the dryline extends well into central Texas, the portion annotated in the figure is bounded by the approximate 2000 foot increase in elevation to the west.

As indicated in the 7.3 micron imagery in the above loop, cooling was coincident with the dryline segment.  An open question becomes, is the cooling observed in other GOES-16 water vapor bands?

The following animation:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/17apr19/4panel_wv_ir_early&loop_speed_ms=60

 

Shows the 3 GOES-16 water vapor bands in addition the to the IR band at 10.3 microns.  Note that the region of cooling observed in the 7.3 micron band also became evident at 6.9 microns, however at 6.2 microns we do not see this cooling signature.  An explanation is hypothesized that makes use of weighting functions, as shown below from the 12Z Del Rio, TX sounding:

Figure courtesy UW/CIMSS

In the weighting function profile, notice that overall the weighting function profile for the 7.3 micron band (magenta) existed at lower altitudes compared to the other 2 water vapor bands.  Likewise, the weighting function profile for the 6.2 micron band (green) is in general above the other 2 water vapor bands.  The interpretation is that the energy detected by GOES-16 ABI originated in the area between the curve and the vertical axis of each respective weighting function profile.  A word of caution, energy detected by the satellite is not coming from just the peak.  As a result of the relative position of each weighting function, brightness temperatures at 7.3 microns are generally warmer than at 6.9 microns which are themselves warmer than 6.2 microns.  The cooling at 7.3 microns is likely caused by ascending motion at the western edge of the moist boundary layer due to  backing surface winds forcing the moist boundary layer up the eastern slope of the ridge line depicted in the topographic map.  This can be characterized by convective pre-conditioning of the environment for potential convective initiation.

As mentioned above, the moist boundary layer is evident in the SWD product.  One explanation utilizes the weighting function profiles as shown below from the 12Z Del Rio, TX sounding for the 10.3 and 12.3 micron bands:

Figure courtesy UW/CIMSS

As seen in the figure, the entire weighting function profile for 10.3 microns is contained within the weighting function profile for 12.3 microns.  As a result, absorption and re-emission of energy occurs at cooler temperatures at 12.3 microns compared to 10.3 microns.  Consequently, the values of the SWD brightness temperatures is positive.

During the pre-conditioning stage, a few events can be identified.  In the following loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/17apr19/4panel_SWD_vis_B10_AM_later&loop_speed_ms=60

At approximately 2146 UTC blowing dust, suggestive of downward mixing of high momentum air, headed eastward towards the moist layer (top left panel).  The development of cumulus, cumulus congestus, and towering cumulus occurred on the western edge of the moist layer (top right panel).  Further, there is evidence of orphan anvils around 2146 UTC.  Also notice in the 7.3 micron imagery the progression of the cooling over a larger area above the moist layer (lower left panel). In addition, the westward migration of the moist layer in response to backing winds as evidence in the top left and bottom right panels.  Lastly, evidence of the cold front is seen in the METARs on the upper left panel due to veering surface winds and decreasing temperatures.  Also, the leading edge of cooler air is indicated by a subtle blue region expanding southward towards the moist layer in southwest Texas.  The expanding cold front is also slightly less subtle in the lower right panel.  These processes continued over the next few hours until convective initiation occurred slightly prior to 0000 UTC 18 April 2019.

One technique to increase contrast of features of interest is to modify color tables.  For example, in our cold front of interest, we created one possible color table to apply to the 10.3 micron band. At 22:46 UTC a segment of the cold front is denoted by a standard symbol in the figure below as means to aid the reader in identifying the leading edge of the cold airmass.

The readers attention is directed towards the evolution of the cold front in the following loop.  During the loop (click link)

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/17apr19/ir_roll&loop_speed_ms=60

notice that convective initiation occurs coincident with the region of cooling observed that was annotated in the 7.3 micron image above.  In particular, convective initiation occurs prior to the arrival of the cold front in the IR loop above.  This suggests that convective initiation occurred as a result of a process independent of the cold front.  As an aside, note that convective initiation in Texas occurs in association with the cold front.

In the Del Rio, TX WSR-88D reflectivity loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/17apr19/radar&loop_speed_ms=160

From the start of the loop, and prior to convection initiation, through about 0106 UTC horizontal streets rotate counterclockwise consistent with backing winds which would aid in the secondary circulation along the sloping ridge in northern Mexico.  Also recall the onset of orphan anvils as discussed above along with blowing dust and cooling at 7.3 microns combined indicate pre-conditioning of an environment towards convective initiation ahead of the cold front.  Around 0030 UTC convective initiation occurs in the region of interest in northern Mexico.  Also note in the radar imagery the two modes of convection – linear to the north and isolated to the south with a well defined right moving supercell.

Another useful tool to monitor convective initiation is the time-of-arrival tool in AWIPS.  Here it is used with the SWD product to track a dust plume that is interpreted as the leading edge of strong southwest flow heading towards our region of cooling at 7.3 microns and separate from the cold front.  As seen in the loop

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/17apr19/SWD_TOA&loop_speed_ms=160

as the dust (purple) advects northeastward a line of shallow cumulus form and is interpreted as the leading edge of the strong southwesterly flow that eventually arrives at the region of cooling at 7.3 micron triggering convective initiation independent of the cold front.

One last item to note: the loop speed of the above IR loop is critical in being able to identify the movement of subtle features.  If the loop speed is too low, identification of certain features may be difficult or impossible.  Therefore it is suggested that the reader experiment with different loop speeds and the rock feature to aid in identification of features of interest.

ALPW product for 26 April 2019 heavy rain / severe thunderstorm event

Nighttime Fog Monitoring

Satellite fog monitoring during the nighttime can be a challenge since geostationary datasets are limited to infrared imagery. However, with the new GOES-16/17 and JPSS datasets users can employ polar-orbiting and geostationary imagery to identify and monitor areas of fog and low stratus (a.k.a liquid water clouds). As meteorologists, we know that fog can significantly reduce ‘near-surface’ visibilities affecting aviation and shipping industries along with the general public. Below is a static comparison over eastern Kansas and western Missouri highlighting the SNPP – VIIRS NCC, the GOES-16 Night Fog Difference and Nighttime Microphysics RGB products; all imagery has hourly METAR surface observations overlaid (i.e. shown in green). Note the hourly surface observations are at 0800Z, 24 April 2019, while satellite observations are at ~0747Z, 24 April 2019.

SNPP VIIRS Near-Constant Contrast (NCC) at 0747Z, 24 April 2019

NCC, a derived product of the Day/Night Band (DNB), illuminates atmospheric features and can sense emitted light sources (i.e. city lights) and reflected light sources (i.e cloud cover) during the nighttime. NCC is known as ‘nighttime visible’ imagery that appears similar to 0.64μm daytime visible imagery. In the imagery below, NCC observes emitted lights from cities and towns that reside along the interstates and in rural areas of Kansas and Missouri. Various levels of cloud cover encompass south-central and eastern Kansas along with western Missouri, where areas of fog are not conspicuous without the assistance of surface observations (i.e. fog indicated by parallel, horizontal green lines). In contrast, in northwestern Kansas, NCC observes fairly clear skies.

 

GOES-16 Night Fog Difference (10.3μm- 3.9μm) at 0746Z, 24 April 2019

Using an approximate time stamp, the GOES-16 Night Fog Difference is utilized. The Night Fog Difference product employs a channel difference of the 10.3μm Brightness Temperatures (BT) minus the 3.9μm BT to identify the fog and low stratus. Liquid water clouds are depicted as positive Brightness Temperature Differences (BTD) (i.e. seen in blue in the imagery below) since liquid water droplets do not emit radiation at 3.9μm but do at 10.3μm; employing the channel difference computes to a positive BTD. Conversely, ice crystals that are embedded in high clouds exhibit a negative BTD (i.e. in grey, refer to the bottom-right of the image).

Note there is an ellipse in western Kansas that observes positive BTD indicating fog. But is it really fog or low stratus we are seeing? The answer is ‘No’. Remember, the NCC product (above) observed clear skies in this area, where the surface observations validate the NCC. This is a false alarm that is produced by the Night Fog Difference product, and it is critical for users to validate this product with surface observations.

 

GOES-16 Nighttime Microphysics RGB at 0746Z, 24 April 2019

For further differentiation between fog and other types of clouds, look to the Nighttime Microphysics RGB (seen below) that employs the 10.3μm-3.9μm BTD and the 12.4μm-10.4μm BTD. Notice within the same ellipse in western Kansas the RGB observes clear skies (light pink) similarly to NCC and the surface observations.

NCC monitoring severe weather during the nighttime

Monitoring severe weather during the nighttime can be challenging since GOES-16/17 is limited to infrared imagery during the overnight hours. In complement to geostationary data sets, polar-orbiting satellite data can be utilized, specifically the Near-Constant Contrast (NCC) product.

For unfamiliar readers, NCC is a derived product of the Day/Night Band (DNB) that utilizes a sun/moon reflectance model that illuminates atmospheric features and senses emitted (e.g. lights from lightning, fires, city lights) and reflected (e.g. clouds) light sources during the nighttime. The product is considered ‘nighttime visible’ imagery that looks very similar to 0.64μm visible imagery that forecasters use during the daytime. Now NCC also has its limitations, since it is dependent on the lunar phase (i.e. full moon compared to new moon) and moon elevation angle (i.e. the moon position above or below the horizon). NCC imagery can range in texture, varying from ‘crisp and clear’ imagery to ‘fuzzy and non-conspicuous’ imagery depending upon the lunar phase and moon elevation angle. NCC is at 0.7µm, exhibiting a 750-m spatial resolution.

NCC observed severe weather over the southern United States during the early morning hours of 18 April 2019. Severe weather was experienced in several states: Texas, Oklahoma, Arkansas, Louisiana and Mississippi. The NCC and GOES-16 infrared imagery (seen below) observed severe weather in the forms of convective cloud tops (i.e. very cold brightness temperatures), lightning, cloud cover and emitted lights from cities.  Imagery is taken at ~0800 UTC on 18 April 2019, where NCC imagery is seen during the full moon phase of the lunar cycle. Notice in the NCC, the lightning that is observed via horizontal white streaks. The white streaks are due to the time discontinuity between the lightning strike (i.e. on the order of milliseconds) and the satellite overpass (i.e. on the order of seconds).

NCC at 0759 UTC, 18 April 2019 – Nighttime Visible Imagery

GOES-16 10.35μm at 0801 UTC, 18 April 2019 – Infrared imagery

The Geostationary Lightning Mapper (GLM) (seen below at the same timestamp) is also used in complement to NCC, in identifying where the high density lightning strikes are observed within the line of storms (red dots); it matches up quite well with NCC. Note, GLM is overlaid onto GOES-16 10.35μm.

GLM at 0801 UTC, 18 April 2019 – Group Flash Counts Density (via CIRA SLIDER)

High Plains Snowstorm

A strong extratropical cyclone moved through the Rocky Mountains and western high plains over the course of 10 April 2019. The low-pressure system produced heavy precipitation in the forms of rain and snow, along with blustery winds.

The system produced heavy snow over a large areal extent spanning from Colorado, Wyoming, portions of Nebraska and South Dakota. Below are surface observations captured at ~20 UTC, 10 April 2019. Note the areas of snow designated by pink asterisks, where the increase in the number of asterisks corresponds with the increase in snow intensity. Northerly and northeasterly high winds gusting over 40 mph were observed as well.

Surface Observations – at 1958 UTC, 10 April 2019

In complement to the surface observations are microwave observations from polar-orbiting satellites, notably, in the form of the Blended Snowfall Rate (SFR) product exhibiting a liquid equivalent snowfall rate. Image below is a static SFR product from the Advanced Technology Microwave Sounder (ATMS) instrument on-board the Suomi-National Polar-orbiting Partnership (S-NPP) satellite. Note high liquid equivalent snowfall rates are observed in north-central Colorado, southeast Wyoming, north-central Nebraska and South Dakota, where heavy snowfall rates in South Dakota ranged from 1.5-4 mm/hr or 0.06-0.16 inches/hr. Another benefit of SFR is the product can observe snowfall rates over large domains, where in comparison to radar, radar coverage can be limited due to data gaps or beam blockages.

S-NPP ATMS -Liquid equivalent snowfall rate at 2020 UTC, 10 April 2019.

In the following two images, notice the National Weather Service (NWS) ‘preliminary’ high snow totals observed over western South Dakota and north-central Colorado are located over the same domains as the high snowfall rates observed by the SFR product.

NWS Preliminary Snow Totals as of o1oo UTC, 11 April 2019 over Western South Dakota

 

NWS Preliminary Snow Totals as of o1oo UTC, 11 April 2019 over the Front Range of Colorado

2-3 April 2019 East Coast Low – ALPW analysis

Nebraska flooding

The past two weeks Nebraska has been inundated with heavy precipitation, in the forms of rain and snow. Nebraska was significantly affected by the ‘record-breaking’ mid-latitude cyclone that past through the area from 13-15 March 2019.  Refer to the GOES-16 10.3um infrared satellite imagery below, seen from 5Z, 13 March 2019 to 22Z, 14 March 2019. Throughout the animation, notice the large areal extent of the cyclone and the cold convective cloud tops (i.e. cold brightness temperatures) indicating varying levels of precipitation in Eastern Nebraska.

 

From this storm, plus subsequent storms thereafter, extreme flooding has transpired throughout eastern Nebraska. The image below displays the observed precipitation values over Nebraska the last 14 days, valid at 12Z, 22 March 2019. Product is provided from the National Weather Service (NWS) – Advanced Hydrologic Prediction Services (AHPS). Precipitation values ranged from 1-5 inches throughout Nebraska.

Coinciding with the precipitation values is polar-orbiting satellite data that observes the magnitude of flooding via Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Flood Areal Extent product. The product (seen in animation below) shows the areas of flooding (i.e. yellow, orange, red colors) in Eastern Nebraska and Iowa between 15-21 March 2019. 17 and 19 March were omitted due to cloud obscuration observed over the area. VIIRS Flood Areal Extent calculates the floodwater fraction percentage of a pixel (i.e. from 0-100%, green-to-red colors), and discriminates between different scene types. Note in the legend: LD = land (brown), SI = supra/snow ice (mixed water and ice, or water over ice, seen in purple), IC = ice (river or lake ice, seen in aqua), CL = clouds (grey), CS = cloud shadows (dark grey), and WA = open water (blue). Spatial resolution of the product is at 375-m resolution. Within the animation, see the evolution of the floodwaters as they increase in width or move along the rivers.

Polar orbiting and geostationary lake ice monitoring

Monitoring lake ice coverage over the Great Lakes via satellite is vital and affects shipping industries, tourism and recreation, especially over the winter months when ice develops, grows and expands over the lakes. According to the Great Lakes Surface Environmental Analysis (GLSEA) and NOAA CoastWatch, the total ice coverage between all 5 lakes is 80% as of 8 March 2019. GLSEA and NOAA CoastWatch’s diagram below highlights lake ice coverage (i.e. ice depicted as grey, dark grey and black colors) and the areas of open water seen via different shades of blue (i.e. represented via water temperatures, ~0°C-5°C).

Satellite observations, combined with other data sets, are vital in producing ice coverage percentages over the Great Lakes. On 8 March 2019, under moderate clear-sky conditions, polar-orbiting and geostationary satellites observe the Great Lakes at high spatial resolution. Note, geostationary observations express high temporal resolution as well, however polar-orbiting observations exhibit coarser temporal resolution. Satellite imagery and products are shown below and are provided from RAMSDIS Online, CIRA SLIDER and RealEarth.

S-NPP Day/Night Band (DNB) – Solar Reflectance (0.7um) at 1850Z, 8 March 2019

DNB solar reflectance acts like ‘daytime visible imagery’ (i.e. 0.64um) where DNB’s satellite sensor observes the solar reflectance from atmospheric or surface features that exhibit high albedos. DNB provides imagery (below) at 750-m resolution and shows open water, land, ice and clouds, above and around the Great Lakes. However, how can users differentiate between the aforementioned scene types?

S-NPP VIIRS Snow/Cloud Layers at 1850Z, 8 March 2019

Look no further than to the polar-orbiting VIIRS Snow/Cloud Layers product which is at 750-m resolution. Observing the same domain as DNB, the VIIRS Snow/Cloud Layers differentiates between land (green), snow and ice (white), low (yellow) and high (pink) clouds and bodies of water (dark blue/black).

GOES-16 Snow/Cloud Layers from 1832-1927Z, 8 March 2019

Now incorporate a similar product, except derived from the geostationary satellite, GOES-16, users can observe the Great Lakes at high temporal resolution. Temporal resolution is from the CONUS sector, that is, 5-minute geostationary data. Notice the lake ice movement (i.e. moving white features over the Great Lakes) along with the low and high clouds moving to the east. Lake ice motion can be seen more conspicuously over Lake Huron.

 

S-NPP VIIRS Flood Detection Product at 1900Z, 8 March 2019

Additionally, another polar-orbiting product that users can observe the Great Lakes and differentiate between surface and atmospheric features, is the VIIRS Flood Detection product. Product is at 375-m resolution, discriminates between different scene types: ice = aqua, supra-snow ice (water on top of ice, or melting ice) = purple, open water = blue, clouds = grey, snow = white, and land = brown. The product also calculates the floodwater fraction percentage from 0-100% (green-red colors) as well. VIIRS Flood product can be accessed in AWIPS-II via LDM.

3 March 2019 – Severe thunderstorm and heavy rainfall event

Radar and satellite ‘snowfall rate’ observations

Forecasting snowfall and snowfall rates can be quite challenging, especially in radar-limited and or radar-deprived regions. A polar-orbiting satellite ‘Snowfall Rate’ product can be used together with radar observations to help anticipate snowfall rates, identify snowfall areal extent and snowfall maximas. To highlight the product’s capabilities, refer to the following snowfall case event over Northern Colorado, between 3-15Z, 2 March 2019 comprising of surface, radar and satellite observations.

Surface Observations over Northern Colorado from 3-15Z, 2 March 2019.

The 13-hr loop shows a decrease in air temperatures across Northern Colorado, exhibiting below-freezing temperatures. Over time, notice a surface low develop, just north of Denver, CO as southeasterly, upslope flow moved into the area. Additionally, the surface low in complement with an upper-level jet maxima (not pictured) and an increase in low-to-mid level moisture, produced enhanced snowfall totals over Northern Colorado.

Radar observations over Northern Colorado from 3-15Z, 2 March 2019.

Base Reflectivity radar observations (via Denver radar from the COD website) during the same time period, shows the evolution of higher reflectivity values (between 15-35 dBZ) observed over Northern Colorado. Notably from 11-13Z, a bright ‘snow band’ (an elongated reflectivity maxima) was observed, indicating heavy precipitation, or in this case, heavy snowfall over Larimer and Weld counties. But what snowfall rates are being observed? This is where the Snowfall Rate (SFR) product can be utilized.

Collocated Snowfall Rate (SFR) product and Radar Observations at ~11Z, 2 March 2019.

The SFR product is derived from passive microwave observations via polar-orbiting satellites, where SFR observations are displayed in units of liquid equivalent ‘inches per hour’. The image below is a direct comparison of SFR in relation to the radar (albeit, offset by two minutes) at ~11Z, 2 March 2019. Notice the line of higher liquid equivalent snowfall rates (0.04-0.1 inches per hour) correspond well with the bright ‘snow band’ seen in the radar. Additionally, the SFR product has the ability to observe the snowfall rate areal extent, seen throughout Wyoming, Nebraska and South Dakota via 1 satellite swath (i.e. DMSP). In contrast, the Denver radar exhibits a limited range, where an adjacent radar needs to be utilized to see snowfall occurring in nearby domains (i.e. users would need to refer to the Cheyenne, WY or North Platte, NE radars).

Snowfall Analysis and snow totals (ending ~9AM MST, 2 March 2019).

Via NWS/NOAA snowfall analyses, note the snow totals from this event, ranging from 0-6 inches at low elevations, and 6-plus inches at higher elevations.

For interested readers, NASA-SPoRT has an additional product, denoted as the Merged SFR product (i.e. incorporates radar and SFR together) that can be accessed online via the following link.

Low cloud / fog over snow covered ground on 25 February 2019

During the overnight hours of 25 February 2019, low clouds and fog developed over portions of northwest Kansas, eastern Colorado and southwest Nebraska.  The low cloud and fog developed over a field of snow on the ground from a recent blizzard.  Low cloud and fog on top of snow on the ground can be difficult to detect in some satellite imagery, while in other satellite imagery it is easy to detect, for example see this 4 panel GOES-16 imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/25feb19/4panel&loop_speed_ms=100

The loop spans the nighttime to daytime hours.

Upper left is the GeoColor product.

Upper right is the Day Cloud Phase Distinction product.

Lower left is the nighttime microphysics product.

Lower right is the 10.3 micron (IR) band.

During daytime hours, note how difficult it is to discriminate between low cloud / fog versus snow on the ground in the GeoColor product, both features appear white.  However, during the daytime we can make the discrimination between low cloud / fog versus snow on the ground in the day cloud phase distinction RGB.  Snow on the ground appears green, why?  There is little contribution from Red (10.3), large contribution from Green (highly reflective at 0.64 microns) and small contribution from Blue (absorptive at 1.6 microns).  The low cloud and fog appears cyan since the contribution from Blue is larger (liquid water clouds reflect much more than snow on the ground at 1.6 microns).

The low cloud and fog during the nighttime hours is observed as bright green in the nighttime microphysics product and light blue in the GeoColor product.  It may be seen in the 10.3 micron band as well, but is much more difficult to detect due to the lack of contrast relative to the other 2 RGB products.  High clouds are also observed moving over the low cloud / fog region in northwest Kansas, these are observed as black or dark red colors in the nighttime microphysics product.  The high clouds acted to seed ice crystals into the low level clouds underneath, leading to snow flurries across the area.

Another product that shows all of this quite well is the CIRA Snow/Cloud-Layers product:

This loop spans daytime hours only since the discrimination between clouds versus snow on the ground can only be made during the daytime in this product.  Snow appears white in color, which may be more intuitive compared to other RGB products.  Bare ground is dark green, low clouds or fog are yellowish-green and high level clouds are pink.

Popocatépetl Volcanic Eruption

Popocatépetl Volcano erupted overnight, spewing volcanic ash emissions, from 0200-1600UTC, 15 February 2019. Geostationary and polar-orbiting satellites observed this atmospheric phenomenon from 00-16 UTC, 15 February 2019.

GOES-16 3.9um 

A hot spot (i.e. white, warm brightness temperature) is produced from the volcanic eruption, along with its ash plume (i.e. elongated, cooler, black, brightness temperatures). Notice how the ash plume advects to the southwest, due to moderate, surface-to-mid-level northeasterly winds.

 

GOES16 – Split Window Difference (SWD)

In the animation, SWD observes low-level atmospheric water vapor (i.e. moisture) and is derived from the 10.3um-12.3um channel difference. Once the volcano erupts, notice the elongated volcanic ash plume (i.e. seen in purple), that comprises of ash and cloud liquid droplets. In the animation, note how the plume is conspicuous compared to the 3.9um observations.

 

GOES-16 – Volcanic Ash Microphysics (EUMETSAT)

The EUMETSAT RGB shows the volcanic ash plume in red, where different shades of red, indicate varying levels of volcanic ash concentrations. Also, volcanic eruptions can produce varying levels of sulfur dioxide (SO2), which is bad for human health. In this animation, there was no SO2 detected; no yellow (i.e. indicates mixed ash and SO2), or shades of green colors (i.e. purely SO2) were observed near the volcanic eruption.

 

SNPP – Near-Constant Contrast (NCC) (Nighttime visible imagery)

SNPP, a polar-orbiter, also captured the volcanic ash plume, utilizing NCC nighttime visible imagery. NCC, a derived product of the Day/Night Band (DNB), utilizes a sun/moon reflectance model that illuminates atmospheric features, and senses emitted and reflected light sources during the nighttime. In the static SNPP NCC image below, notice the emitted light sources produced from cities and towns (i.e. Mexico City and Acapulco) and the reflected light sources (i.e. clouds and in this case, the volcanic ash plume).

VIIRS Captures Ice/Ocean Movements

By Lewis Grasso and Jorel Torres

VIIRS captures interesting imagery in the Arctic. From 0314 UTC to 1233 UTC, 13 February 2019, VIIRS, on-board NOAA-20 and S-NPP, imaged fascinating features in the Arctic. In particular, imagery from Band I4 (3.74um) with a sub-satellite footprint size of 375-meters captured several features: 1) oscillation of an ice field due to lunar tides, 2) the boundary where the ice sheet is melting due to the interaction of relatively warm waters associated with the Gulf Stream, and 3) the melt water and associated currents in the ice-free ocean. Animation below, shows a loop beginning at 0314 UTC and extending to 1233 UTC. Within the loop the dark-greenish color represents the very cold ice sheet, northeast of Greenland, which is located in the lower right corner of the loop. Incidentally, portions of Svalbard, Norway are located in the upper-left corner. The lightest grey color represents the relatively warm ice-free ocean. The rapidly moving cloud-field is evident over the ice free ocean by a black color. Interpretation is the following: periodic oscillations are associated with the lunar tide, note the back and forth motion of the ice sheet. As the boundary of the ice sheet melts, the cold melt water flows into the relatively warm ocean and appears relatively dark. The left most dark plume of melt water  is reminiscent of a drop of black ink falling downward in a tank of clear water. We invite the reader to think, when was the last time you have seen this type of view in the Arctic? Kudos to the JPSS Program.

4 February 2019 significant ice storm in the Upper Great Lakes

On 4 February a shortwave tracked across the Upper Great Lakes towards the east, ahead of the shortwave, anomalously high moisture at low to mid-levels existed which contributed to a historic ice storm for the region.

The NWS forecast office in Marquette, MI has a great web-page summarizing this event including pictures and the meteorological environment:

https://www.weather.gov/mqt/February4th2019IceStorm

This blog entry will focus on various satellite imagery and products, particularly those that highlight the anomalously high moisture for this event.

We lead off with a synoptic scale perspective of this event with the 3 Water Vapor bands from GOES-16 along with the Air Mass RGB:

The imagery clearly shows a shortwave in the North Dakota / Minnesota vicinity moving eastward. Ahead of this shortwave, precipitable water values were anomalously high, in fact record breaking TPW values were observed for this time of year in the Upper Great Lakes.  The anomalously high precipitable water values can be seen in the Advected Layer Precipitable Water product for the various layers.  Moisture plumes are observed with origins from the Gulf of Mexico in the SFC-850, 850-700 and 700-500 mb layers.  This shows that the moisture was relatively deep, particularly for this time of year in the Upper Great Lakes region.

An experimental product at CIRA that is still under development is the model minus ALPW PW for each layer.  For example, the HRRR minus ALPW PW for a given layer shows the difference between observations from ALPW versus different HRRR 3 hour forecasts (4 panel shows the same layer arrangement as the ALPW loop above). We primarily see positive values in the Upper Great Lakes region ahead of the shortwave, meaning that there’s more moisture in the HRRR 3 hour forecast compared to ALPW observations. A similar theme exists for the GFS (these are also 3 hour forecast fields).

Interestingly, the HRRR forecast a substantial ice storm, as seen in this set of forecasts:

The anomalously high precipitable water played a key role in contributing to a historic ice event for this region, which typically observes snow during precipitation events this time of year.

The ALPW product is available in AWIPS from CIRA via LDM, however the model minus ALPW difference fields are not available since these are still in an experimental stage.

Observing sea surface temperatures from GOES and JPSS

Observing sea surface temperatures (SSTs) from satellite is an important aspect in weather forecasting for a variety of applications. Applications consist of (but not limited to) forecasting hurricane intensity, sea fog, and convection over the oceans. But remember, oceans are vast, making up ~70% of the Earth’s surface, and more importantly, oceans are remote, where surface observations are scarce. This is where satellite observations come into play, and can complement other data observations (e.g. radar, surface) to address certain weather forecasting applications.

Below, is a comparison between GOES16 SST and GCOM AMSR-2 SST observing the Gulf Stream on 6 February 2019. The comparison will highlight the benefits and limitations of each product.

GOES16 – SST @1900Z, 6 February 2019

The geostationary product provides users hourly data (i.e. 15-minute data averaged into a 1-hour composite), at high spatial resolution of 2-kilometers. The product is derived from four, infrared, GOES16 ABI spectral channels: 8.4um, 10.3um, 11.2um, 12.3um. In the static GOES16 SST image below, notice the array of sea surface temperatures ranging from ~15°C to +30°C. However, also note the irregular, black colors, seen over the ocean. This irregularity is cloud cover and one of the limitations of GOES16 SST. Infrared satellite retrievals can only be produced in clear-sky environments.

 

GCOM AMSR2 – SST @ ~1843Z, 6 February 2019

Now in contrast to GOES16 SST, GCOM (a polar-orbiting satellite) AMSR2 SST is able to produce passive microwave SST retrievals in both clear-sky and cloudy environments. However, precipitating regions are problematic as well. In the imagery below, over the same domain, at approximately the same time (offset by 17 minutes), GCOM AMSR2 SSTs are observed. Notice there is also missing data (expressed in white colors over the ocean) in the imagery, however it is not due to clouds. The missing data is expressed via ‘data gap’, east of Florida. This is one of the limitations of polar-orbiters, in that polar-orbiters provide coverage up to ~2 times a day and GCOM AMSR2’s orbital swath does not overlap. Other limitations are GCOM AMSR2 SST’s coarser resolution (10 kilometers) and missing data over coastlines. Since microwave radiation is significantly different between land and ocean, SST retrievals that ‘do not’ have land in the field-of-view are considered.

 

GOES16 – SST Animation from 18Z, 6 February 2019 –> 16Z, 7 February 2019

For fun, here is an animation of the GOES SST, demonstrating its high temporal resolution, while observing the Gulf Stream moving near and around Florida: moving northward, up the southeastern coastline.

 

For interested readers, click on the following link to discover examples of polar-orbiting and geostationary data, combined together, to produce blended 5-km SST retrievals.

Arctic Blast in the Upper Midwest

A cold arctic air mass moved into the Upper Midwest the past two days (29-30 September 2019), providing extreme cold temperatures for several states, including North Dakota, Minnesota, Wisconsin, South Dakota, Iowa, and Illinois. Along with high winds, the calculated, respective, wind chills are even lower.

Satellite imagery, comprised of polar-orbiting and geostationary data, along with surface and upper air observations are used to demonstrate how cold it was for this portion of the United States. Images are provided between 29-30 January 2019.

VIIRS Snow Cloud Discriminator: 1902Z, 29 January 2019

The VIIRS Snow Cloud Discriminator product (below) differentiates between snow cover (white), bare ground (dark green),  and low (yellow) – to – mid (orange) – to – high (pink) level clouds. Additionally, state abbreviations seen in the imagery are as follows: SD – South Dakota, ND – North Dakota, MN – Minnesota, WI – Wisconsin, and IA – Iowa. The static image shows the large areal extent of snow cover that resides in several states, as the arctic air mass advects over the domain.  In addition to the cold arctic air mass, snow cover played a role in the extreme, cold air temperatures observed in the Upper Midwest from 29-30 January 2019. To re-enlighten readers, snow cover traps longwave radiation (i.e. in the form of heat) that the Earth emits, and as a result, during the nighttime, near-surface air temperatures are predominately colder, since there is limited heat exchange between the surface and the atmosphere.

GOES-16 10.3um: 2147Z 29 January 2019 –> 1607Z 30 January 2019 

The animation below, shows infrared geostationary data, displaying cold brightness temperatures (ranging from -30C to -50C, light-to-dark blue and green colors) moving over the Upper Midwest. Notice how the cold air advection moves slowly southward, and appears slightly different in texture than cloud cover; cold air appears more smooth and uniform, than the rough, defined features of cloud cover. Additionally, cloud cover also develops and dissipates quickly over time, albeit, shows similar brightness temperatures to near-surface cold air. Note how the cold air extends all the way south to the Missouri/Iowa border, then stars to recede northward, when daytime solar heating begins (~13Z, 30 January 2019). The cold air extent also matches approximately to the snow cover extent observed by the VIIRS Snow Cloud Discriminator.

 

Surface Observations: 2158Z 29 January 2019 –> 1643Z 30 January 2019 

Furthermore, surface observations (provided from RAP Real-Time Weather) show the cold air advection from the event. Notice the strong northwesterly winds move through the domain, throughout time, and note the surface air temperatures (-40F or below, observed by some locations).

RAOB Soundings: KABR and KINL –> 12Z, 30 January 2019

To get an idea of how cold the air was not only at the surface, but aloft, one can refer to RAOB temperature and moisture soundings. Soundings below are both at 12Z, 30 January 2019, where the first sounding is from Aberdeen, SD (KABR), and the second is from International Falls, MN (KINL). Notice, how deep the arctic air mass is from Aberdeen, SD; the air mass extends from the surface – to ~550mb thick!

For interested readers, a Minneapolis, MN social media post of the event can be seen at the following link.

WS Harper Impacts on Northeast US

As Winter Storm Harper passed through the northeast United States, the storm brought heavy precipitation in the forms of snow, rain, and freezing rain that produced significant ice accumulation on the ground. Storm total snowfall, rainfall and ice accumulations were observed in Connecticut, Massachusetts, and Rhode Island. Specifically snow and ice accumulations ranged from 1-11 inches, and a trace-to-0.3 inches of ice respectively. Both accumulations on the roads, along with localized flooding from heavy rain can be hazardous to travelers. To access the storm observation reports, refer to the following NWS ‘Public Information Statement’ link.

The snow and ice accumulations were also observed from geostationary and polar-orbiting satellites, taken from before the storm (17 January 2019), and after the storm (21-22 January 2019).

Before the Storm: GOES-16, Band 5 – 1.6um @ 13-21Z, 17 January 2019

Geostationary imagery (i.e. animation) is at 1-kilometer spatial resolution, where the 1.6um spectral band is used. 1.6um, also known as the ‘Snow/Ice Band’, discriminates between the refraction components of water and ice. In the imagery, users would see liquid water clouds appearing bright, while ice clouds and ice accumulation on the ground (i.e. via snow or ice accumulation produced from freezing rain) will appear significantly darker. This is due to that ice absorbs radiation in 1.6um, where liquid water clouds reflect radiation at 1.6um. Also in 1.6um, land surfaces and bodies of water (i.e. lakes and ocean) exhibit great contrast as well. The animation below is before the storm, where it is used as a reference for readers. Notice no dark swaths of ice are observed in the states of Massachusetts, Connecticut and Rhode Island.

 

After the Storm: GOES-16, Band 5 – 1.6um @ 13-21Z, 21 January 2019

After the remnants of Winter Storm Harper passes through the New England area, notice an elongated, horizontal, dark swath observed in the imagery. Notice the dark swath earlier in the day, before mid-to-high level clouds obscure the surface features. Refer to the imagery inside the yellow ellipse.

 

After the Storm: GOES-16, Band 5 – 1.6um @ 13-21Z, 22 January 2019

The very next day is also explored, since the day predominately encapsulates a cloud-free atmosphere, before high-level cirrus clouds move over the domain. The dark swath can be seen throughout portions of Connecticut, Massachusetts and Rhode Island.

 

After the Storm: NOAA-20, I-3 Band – 1.6um @ 1750Z, 22 January 2019

Need a better spatial resolution of the dark swath? Refer to polar-orbiting satellite imagery. NOAA-20 VIIRS imagery data is produced at 375-meter spatial resolution, and provides more detailed surface features, although the temporal resolution of polar-orbiters are infrequent.

Social media highlighting the ice accumulation can be seen via the following links: photo, and video.

Winter Storm Harper

A large low pressure system, slammed into the western United States (US), bringing heavy rain and snow, high winds, along with producing blizzards (for the Sierra Nevadas) and localized flooding for low-lying areas.

The areal extent of the system is seen via ‘Preliminary, Non-Operational‘ GOES-17 Day Cloud Phase Distinction RGB (below) at 1925 UTC, 16 January 2019, as the storm approached the Western US.  The RGB differentiates between liquid water clouds (blue), glaciated clouds (green) and mid-to-high level ice clouds (yellow-to-red clouds). Note the elongated rope cloud, associated with the cold front, in the southwest portion of the image, depicted in quasi-linear blue and green colors.

The winter storm can also be seen via Advected Layered Precipitable Water (ALPW) product, that is derived from polar-orbiting satellites and identifies areas of high moisture content and moisture transport that can lead to heavy precipitation and flooding. Animation below, shows the ALPW product from 06 UTC, 16 January 2019 –> 15 UTC 17 January 2019, highlighting Winter Storm Harper as it moves into California, Oregon and Washington. The colorbar is at the top of the animation depicting 0-32 mm (black to pink colors) precipitable water values. ALPW is different from Total Precipitable Water (TPW) products, in that ALPW identifies precipitable water from four atmospheric layers (surface-850mb, 850-700mb, 700-500mb, and 500-300mb), rather than specifying the total precipitable water values for the entire atmospheric column.

In this case, as Winter Storm Harper approaches land, notice high concentrations of precipitable water between the surface-to-500mb (blue/aqua colors), and significantly lower precipitable water concentrations in the upper atmosphere (i.e. 500-300mb, grey to black colors). Also note an elongated atmospheric river on the southeast side of the low pressure system. The atmospheric river moved into and impacted south-central and southern California, where the Sierra Nevadas experienced heavy snowfall rates (up to ~3 inches per hour) and high snow accumulations.

Speaking of the Sierras, earlier this morning there were reports of thundersnow in the Sierras. Reports were near Mammoth Mountain, CA, in which the ‘Preliminary, Non-Operational‘  GOES-17, Geostationary Lightning Mapper (GLM) detected lightning signatures near Mammoth Mountain. Animation below shows GOES-17 GeoColor imagery overlaid by GLM – Group Flash Count Density signatures. Time period observed is between 12-1445 UTC, 17 January 2019. Notice the lightning signatures observed in the Sierras that experienced thundersnow.

 

Winter Storm Harper plans to create more havoc throughout today and into the weekend, moving into and impacting the Rocky Mountains, Central Plains and Eastern United States.

Fog and Low Clouds over Snow Cover in the Midwest: A case from 10 December 2018

Areas of snow cover can often lead to areas of persistent fog and low cloudiness under the right conditions.  And determining the areas of fog and low clouds from snow cover can be tricky during the daytime hours.  Here we look at a case from 10 December 2018 over the Midwest the snow cover likely contributed to low cloudiness and fog that either was slow to clear or lasted through the day in some areas.  We’ll use a couple of CIRA products for this case.

We begin with a look at the extent of low clouds and fog around pre-dawn (1202 UTC on 10 Dec) using the CIRA GeoColor product from GOES-16 displayed using the CIRA SLIDER tool (available at http://rammb-slider.cira.colostate.edu).

GeoColor is not an operational product, but it is available for display on AWIPS and is widely used across the NWS (if you don’t have this product at your WFO and would like to get this product on AWIPS send us an email).  In the nighttime city lights are displayed in the background and low clouds (water clouds) or fog are colored blue, while higher (ice) clouds are white.   Observations at this time (shown below) suggest that much of the blue area in MN into WI is fog, with more low cloudiness in the area farther to the east.

During the daytime hours GeoColor uses the visible band 2 with a true color background.  A look at the GeoColor image at 1802 UTC on 10 Dec shows that it can be hard to distinguish snow from clouds or fog with the visible band during the daytime.

Here is the NOHRSC snow cover analysis for this day.

There are RGB products that can be used to help discriminate clouds from snow (such as the Day Snow/Fog product developed by EUMETSAT.  CIRA has developed a product that also discriminates clouds and fog from snow but retains white as the color for the snow cover.  The CIRA Snow/Cloud Layer Discriminator product for the same time as the image above is shown below.

The color scale is shown at the bottom of the image.  This product is also experimental but should soon be available for AWIPS as well.  In this version of the product there is a discrimination made between lower (water) clouds and higher (ice) clouds, adding additional information.  This product is for use during the daytime hours, with the image for a couple of hours later showing some breaking up of the low clouds and fog in areas without snow cover but otherwise the low clouds and fog persisting, in fact through the day as shown in the GeoColor image for 2202 UTC.

 

 

Advected Layer Precipitable Water (ALPW) comparison for events in December 2018 – January 2019

New Mexico Snowstorm

Due to an upper-level disturbance passing through the southwestern United States, abundant snowfall has fallen in New Mexico, southwestern Colorado and parts of Texas (see social media snowfall image here). Snow totals range from a few inches to 16 inches at higher elevations. Snowfall has been observed via surface observations and by satellite this morning, 28 December 2018 at ~16Z.  Surface observations and satellite images are offset by ~13 minutes.

RAP Real-Time Weather Data – Surface Observations @ 1558Z, 28 December 2018.

Snowfall is observed (i.e. weather station plots displaying purple, asterisk symbols) in parts of New Mexico, Texas, and southwestern Colorado. Multiple asterisks imply heavier snow rates observed by site. Notice, large areas of New Mexico, that do not have surface observations; this is where satellite and radar data are helpful in observing snowfall rates in ‘surface data’- sparse regions.

NESDIS Snowfall Rate Product (NASA-SPoRT) – Snowfall Rate Product @ 1611Z, 28 December 2018.

The NESDIS Snowfall Rate Product utilizes microwave snowfall rate data derived from polar-orbiting satellites (e.g. S-NPP, MetOp, DMSP; note more polar-orbiters are implemented into the algorithm). Snowfall rate is shown in ‘liquid equivalent’, displayed in inches per hour, and millimeters per hour. Product utility is in observing snowfall rates in data-sparse regions, and identifying areas of heaviest snow. The image below, highlights the snowfall rate product at approximately 13 minutes after surface observations were observed. Snowfall rate product imagery indicates a widespread distribution of snowfall rates in New Mexico, and the surrounding states, where areas of heaviest snow are indicated in southwestern Colorado, central New Mexico and northern Texas. Maximum values range from 1-1.5 mm/hr or 0.04-0.06 inches/hour.

COD Weather – Radar Imagery @ 1610Z, 28 December 2018.

Collocated in time with satellite observations, the Albuquerque, NM radar imagery, shows regions of precipitation (green-to-yellow colors), in this case, areas of snowfall. However, take note that Albuquerque, NM is at high elevation (i.e. ~5300 feet) and is surrounded by high terrain. The Albuquerque, NM radar will interact with mountain ranges that may produce anomalous features (e.g. high dBZ values in an areas where it is not snowing). Radar coverage can also be poor in some areas, due to high terrain blocking radar signals, obscuring ambient atmospheric features.

More snowfall is expected for Albuquerque, NM for the next 24 hours.

20 December 2018 Heavy Rain event in the Mid-Atlantic area

Advected Layer Precipitable Water (ALPW) for 14 December 2018 event

View the ALPW loop here:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/14dec18/alpw&loop_speed_ms=700

 

DNB and satellite identification

Day/Night Band (DNB) is apart of 22 spectral channels on the VIIRS instrument, in which VIIRS is on-board S-NPP and NOAA-20 satellites. DNB can assist users in monitoring atmospheric and surface features via emitted and reflected light sources, during the nighttime. Features identified by DNB (but not limited to), span from observing cloud cover, snow cover, lightning, auroras, sea ice, ship lights, fires, gas flares, lights from volcanoes and city/town lights. But what DNB can also observe are other low-earth orbiting satellites placed lower in altitude than S-NPP and NOAA-20.

Two prime examples are seen in the DNB imagery below. Images are taken on 4 December 2018 @ ~15Z, and 5 December 2018 @ ~10Z and are courtesy of RAMMB Slider.

Low-earth orbiting satellites observed by DNB exhibit a ‘line of bright dots’ that are sequentially produced in a direction via VIIRS’ scan lines. The dots have bright signatures via sunlight reflecting off the satellite, that DNB observes. Notice in the two examples below that emitted city lights of Alert, Nunavut, Canada can be seen, along with sea ice (i.e. located along the coast of Canada and Greenland) and an aurora.

DNB –> 4 December 2018, @ ~15Z

DNB –> 5 December 2018, @ ~10Z

21 November 2018 Snow Squall event in upstate New York

On 21 November 2018 a cold front moved through upstate New York with an associated band of heavier snow, as seen in this WSR-88D reflectivity image:

Note the narrow region of higher reflectivities extending from near Syracuse northeastward.  This snowband prompted a snow squall warning issued by the NWS office in Binghamton:

How about the perspective from GOES-16?  Here is a 4 panel display of visible (0.64 micron band) in the upper left, the day cloud phase distinction RGB (upper right), IR (10.3 micron band) with the default color curve (IR_Color_Clouds_Winter) in the lower left, and IR (10.3 micron band) with the GOES Snow Squall color curve in the lower right:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21nov18/&loop_speed_ms=60

The imagery on the top 2 panels relies on information from the visible band, therefore it’s only valid during daylight hours.  The familiar visible imagery in the upper left shows the snowband as more textured clouds along a narrow line, which is along the cold front as seen in the METARs.  The day cloud phase distinction RGB shows the snowband as green with surrounding clouds as light blue or cyan.  The surrounding clouds are clouds composed of liquid water, which show up as light blue or cyan in this RGB product.  The snowband is associated with more vertically developed clouds, therefore have glaciated tops associated with them.  Once glaciation occurs, the blue component of this RGB product (the 1.6 micron band) goes from a high contribution to a low contribution since ice clouds absorb radiation considerably more than liquid water clouds at this wavelength.  Lower contribution of blue means that the green contribution (0.64 micron) shows up quite well (since clouds reflect during the daytime).  How about the IR band shown in the bottom panels?  There is insufficient contrast to identify the snowband with the IR imagery in the default color curve in the lower left panel.  The lower right panel has a color curve designed for more contrast at temperature thresholds typically observed with snow squalls, however it’s still not as easy to identify compared with observing the glaciated cloud tops (in green) in the day cloud phase distinction RGB.  If this was a nighttime case, the day cloud phase distinction product would be unavailable, leaving you with options such as changing the range on the IR imagery or perhaps making manual adjustments to the color curve in the AWIPS color curve GUI.

Sea Ice Motion and VIIRS Spatial Resolutions

It’s nearing wintertime, and over the North and South Poles, sea ice accretion is occurring. Using RAMMB Slider, users can observe sea ice motion and sea ice breakup, via Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on-board the Suomi-National Polar-orbiting Partnership (S-NPP), and NOAA-20 satellites. For unaware users, VIIRS has 22 spectral channels, where 16 channels are at 750-m resolution (i.e. known as ‘Moderate (M) bands’), 5 are at 375-m resolution (i.e. known as ‘Imagery (I) bands’) and the other is the Day/Night Band (DNB) channel at 750-m.

Below is a VIIRS M15 – 10.76um animation, between 5-16 UTC, 20 November 2018, of sea ice motion between Fort Russ, Nunavut, Canada (bottom-left of the animation) and Baffin Island, Canada (upper-middle of the animation). Note, sea ice breakup and erratic sea ice motion are due to varying surface winds. In the imagery, colder brightness temperatures are represented in green and yellow colors, while relatively warmer brightness temperatures are seen in navy blue, aqua and grey colors. Additionally, notice the extremely cold brightness temperatures over Baffin Island (i.e. large areal extent of yellow colors).

 

Now if users want to use a higher spatial resolution to observe sea ice motion, below is a comparison between the coarser spatial resolution of VIIRS M15 – 10.76um at 750-m to VIIRS I5 – 11.45um at 375-m resolution. The comparison is taken at 1045 UTC, 20 November 2018. Note the sea ice fissures (i.e. crevices and cracks within the sea ice) that are more conspicuous in the I5 that the M15 band.

Alaskan Aurora

Early this morning, 5 November 2018, an aurora was visible over the state of Alaska. The aurora was large in areal extent and produced green hues over the Anchorage, AK night sky. A photo of the aurora can be seen, via the following link. Polar-orbiting satellite products observed the phenomenon; imagery is seen before (4 November 2018) and during the event (5 November 2018). Images courtesy of CIRA POLAR Slider.

VIIRS GeoColor @ 2327 UTC, 4 November 2018 (Before the Event)

GeoColor imagery combines 5 spectral channels, where three channels; the aerosol (~0.47um), visible (~0.64um) and vegetation (~0.87um) channels are used during the daytime to contrast between the ocean (i.e. blue, aqua colors), land (i.e. brown colors), overlying cloud cover (i.e. white smooth, thin, or clumpy textures) and snow (i.e. white colors resembling dendritic formations, especially over high, complex terrain).

Day/Night Band (DNB) @ 1349 UTC, 5 November 2018 (During the Event)

DNB product utilizes a sun/moon reflectance model that illuminates atmospheric features and in this case, senses emitted (i.e. auroras, city lights) and reflected light sources (i.e. clouds, snow) during the nighttime. Spatial resolution is at 750-meters and DNB imagery is taken during the new moon phase of the lunar cycle (i.e. moon below the horizon, no available moonlight). In this particular moon phase, without available moonlight, atmospheric features in the imagery are sensed via satellite from ‘atmospheric nightglow‘.

In this particular case, in the DNB imagery, the aurora produces an elongated, horizontal, white streak that dominates the scene. However, if one looks closely, the aurora was so bright (i.e. appearing to assist the atmospheric nightglow phenomenon in sensing features), it reflected light off the snowy surface, that the satellite sensed. Hint for readers: over Alaska, look to the north and to the south of the aurora, to find areas of snow. Ambient cloud cover is also noticed in the imagery.

Day/Night Band (DNB) @ 1349 UTC, 5 November 2018 – ‘Zoomed In’ (During the Event)

If the snow is not visually apparent to readers in the last image, refer to the following ‘zoomed in’ DNB imagery; where the domain is just south of the aurora. ‘Zoomed in’ DNB imagery highlights snow via dendritic formations (also seen in the VIIRS GeoColor, above) along the Alaskan coast. Notice, the emitted city lights from Kenai, AK and Nikiski, AK as well.

DNB: Ship Light Monitoring and Sea Ice Motion

The Day/Night Band (DNB) utilizes a sun/moon reflectance model that illuminates atmospheric features, and senses emitted and reflected light sources during the nighttime hours. The DNB is at 0.7um and is apart of 22 spectral channels on-board the Suomi National Polar-orbiting Partnership (S-NPP) satellite and the new NOAA-20 satellite.

In the DNB video below (using CIRA POLAR Slider) DNB is monitoring emitted ship lights in the East Siberian Sea, just northwest of Pevek, Russia. Refer to the yellow circle in the video. The ship appears to be moving to the east, traversing through the sea ice, over the course of four hours. Note, during this time period, the sea ice motion is moving to the north as well (i.e. refer to yellow arrow). Emitted town lights near Pevek, Russia and along the coast can be seen in the imagery as well.

 

For more information on DNB, refer to the following training material.

Hurricane Willa

Hurricane Willa is forecasted to make landfall, along the coast of Mexico, within the next few hours. Willa, once deemed a Category 5 hurricane, has been downgraded to a Category 3 hurricane within the last 24 hours, with max sustained winds at 125 mph, moving north-northeast at 6 mph according to the National Hurricane Center (NHC) at 9AM MDT. The majority of people that reside in Willa’s path have been ordered to evacuate, with high winds, copious precipitation, storm surge and flooding to be expected. Below, geostationary and polar-orbiting satellites observed the event, where satellite imagery can be inspected below.

Advected Layered Precipitable Water (ALPW): 1800-1200Z, 22-23 October 2018

ALPW measures Precipitable Water (PW) values within four layers of the atmosphere (i.e. surface-850mb, 850-700mb, 700-500mb, 500-300mb) to identify where the majority of PW is concentrated (i.e. usually within the first 3 kilometers above the surface). For simplicity, the ALPW surface-850mb layer video is shown below, showing the location of Hurricane Willa, as it approaches the Mexican coast. Note, ALPW utilizes GFS model winds in the algorithm, and is updated every 6 hours. The modeled product shows the hurricane’s forecasted track yesterday, through this morning, 23 October 2018. High PW values are shown in red, where black pixels within the hurricane, denote precipitating regions. Microwave products such as ALPW, cannot retrieve data samples within areas of precipitating regions (i.e. displayed areas of missing data), however retrievals can sample within cloudy regions.

 

GOES-16 10.3um: 1450-1600Z, 23 October 2018

Infrared imagery, at a high temporal resolution (i.e. in this case, every 5 minutes) shows the magnitude of the Willa as the eyewall begins to traverse the Islas Marias then move toward the Mexican coast. Deep convection (i.e. regions of heavy precipitation) denoted in dark red-to-black colors, is spotted predominately to the north and west of the eyewall.

 

For the latest updates on Hurricane Willa, click on the following link.

Santa Ana Winds and Dust Identification

It is that time of year again, to observe Santa Ana Wind events for Southern California. On 15 October 2018, an upper-level trough advected into the southwestern United States (i.e. see GOES-16, Upper Level Water Vapor imagery below), produced cold air advection aloft, and brought strong subsidence (i.e. sinking motion) to the surface. The strong subsidence, brought cold, dry, and fast, downsloping winds along the Sierras, towards the coastline. The downsloping winds produced compressional warming, a process that warms the surface air tens of degrees fahrenheit over a short period of time. The following satellite imagery and surface observations of the event are taken between 9-20Z, 15 October 2018.

GOES-16 -> Upper Level Water Vapor (6.2um) : 12-20Z, 15 October 2018

 

Surface Wind Observations: 10-20Z, 15 October 2018

Note the strong, northeasterly winds in Southern California, with wind gusts over 40 mph. Additionally, notice rapid warming of air temperatures along with a significant decrease in dewpoint temperatures, especially along the coast.

 

GOES-16 -> Low Level Water Vapor (7.3um): 17-20Z, 15 October 2018 

A rapid warming of Brightness Temperatures (BT), represented by pink and yellow colors, advects from northeast-to-southwest bringing warm, dry air to the Los Angeles Metropolitan area. Note, the wave-like patterns produced off of the Sierras create turbulence in the boundary layer, and are hazards for the aviation industry.

 

Microwave Sensors -> CIRA Advected Layered Precipitable Water (CIRA-ALPW): 9-18Z, 15 October 2018 

Another way to see how dry the surface air is, one can use the CIRA-ALPW product, comprised of microwave data. CIRA-ALPW estimates the precipitable water (i.e. moisture content) within a vertical column of the atmosphere. Unlike other precipitable water products, CIRA-ALPW produces precipitable water values within four layers of the atmosphere (i.e. surface-850mb, 850-700, 700-500 and 500-300mb) and utilizes GFS model winds in the algorithm. In the following CIRA-ALPW, surface-to-850mb video, see the moisture gradient, and dry air (i.e. low TPW values) shift southwestward, towards the coastline.

 

GOES-16 -> Split-Window Difference (10.3um-12.3um): 17-20Z, 15 October 2018  

The Split-Window Difference product shows areas of dust, produced by the high winds, seen within the black ellipses. Regions of low-level dust are denoted as negative values, and represented by dark brown signatures. The Split Window Difference product identifies these areas as dust, due to the 10.3um Brightness Temperature (BT) is colder than the 12.3um BT; where silicates in dust are absorbed in 10.3um.

 

GOES-16 -> GeoColor: 17-20Z, 15 October 2018  

In complement to the Split Window Difference product, one can use the GeoColor product to identify dust within the black ellipses. Play the following video. Notice dust over land may not be as discernible to dust advecting over bodies of water. If one is still having trouble identifying dust, focus on the following cities: Long Beach, Mission Viejo, Oceanside, Temecula, the Salton Sea, Ocotillo, and Barstow.

Hurricane Michael

Hurricane Michael has made landfall today, along the Florida Panhandle, between Tyndall Air Force Base, FL and Mexico Beach, Florida. Radar and satellite products observed Michael, as it approached the Florida Panhandle (seen below). Over the last 12 hours, Michael increased in maximum wind speed to 155-mph and had a pressure level of 919-mb.

Radar – Base Reflectivity (Tilt – 0.5 degrees) between 15-17Z, 10 October 2018 (via College of Dupage). Notice the heavy rain bands in the inner and outer core of Hurricane Michael, producing heavy precipitation, and very high wind speeds. 

 

Blended Total Precipitable Water (TPW) at 1456Z, 10 October 2018. A microwave product that estimates TPW throughout the entire column of the atmosphere. Purple and white colors express high moisture content (i.e. high TPW values, ~2.5-3 inches) that Hurricane Michael encapsulates, which can lead to heavy precipitation and flooding. 

Advected Layered Precipitable Water (ALPW) from 18Z, 9 October 2018 to 15Z, 10 October 2018. Different from the Blended TPW, ALPW estimates precipitable water values in 4 different layers (i.e. refer to 4-panel below: surface-850mb (top-left), 850-700mb (top-right), 700-500mb (bottom-left), and 500-300mb (bottom-right)), where the majority of moisture content is located in the lower layers of the atmosphere (i.e. within 3-km above the surface). GFS model winds are incorporated into the algorithm. Black pixels in the imagery represent precipitating regions, denoted as ‘missing data’. Although microwave retrievals can be made in cloudy regions, they cannot be made in precipitating regions. 

 

GOES-16, infrared band 13 (10.3 um) at 1634Z, 10 October 2018. Imagery shows Hurricane Michael, on the cusp of landfall, showing a well defined eyewall and cold brightness temperatures (i.e. red to dark-red colors) within the inner and outer core of the hurricane. 

NHC Forecast (below) for the next few days, as of 1 PM CDT update. Hurricane Michael will interact with an upper level trough, and move to the northeast, producing heavy precipitation in Florida, Georgia and the Carolinas. The system will be downgraded to a Tropical Storm by Thursday and will eventually move out to sea, by the weekend.

The Arizona Hurricane Rosa Heavy Rainfall Event for Late September to Early October 2018

This blog entry is by Sheldon Kusselson and in the format of a PDF document:

Hurricane Rosa Event_LateSept_EarlyOct2018

Hail damage swaths from severe storms over the High Plains as viewed from satellites during July 2018

By Louie Grasso, Dan Bikos, Jorel Torres and Ed Szoke

During the summer of 2018 over the High Plains, several significant severe storms occurred.  Several hailstorms moved southward over the Central High Plains and produced noticeable hail swaths and damage scars on the ground that were captured by GOES-16 ABI.  The purpose of this blog is to compare and contrast GOES-16 imagery before and after each event.  In addition, SNPP and NOAA-20 imagery will be shown that also contains damage scars. Supplemental observations of the tracks of the severe storms is provided the MRMS Mid-level rotation tracks along with SPC storm reports.

To begin with, a brief discussion of imagery before and after the hail swaths and damage scars is given below.  Before the events (left side image  in the figure below) this is how the GOES-16 ABI imagery appeared on 10 July 2018.  For those not familiar with the region displayed in the imagery, the dominant brownish color is typical due to the semi-arid climate of the western High Plains.  During the three weeks following 10 July a persistent northwesterly flow pattern existed that supported multiple severe weather episodes.  As a result, several surface features, indicated by black line segments are evident at the end of the period (right side image in the figure below).

Our first case study occurred on 26 July 2018.  Thunderstorms began developing during the afternoon of the 26th, a few of which are seen in the figure below.  Of particular interest are the two white ovals, one located along the Colorado/Wyoming border, the second located in western Kansas. At that time no hail swaths were evident in either oval.  A comparison of the figures above and below can be used to orient the reader about the 2 regions just discussed.

 

At 0230 UTC 27 July two hail producing storms were evident in imagery in the GOES-16 ABI 3.9 micron band and is displayed below.  Within the white ovals, darker line segments were evident in the wake of the storm paths as they moved towards the southeast.  In the color table used below, the dark line segments correspond to cooler brightness temperatures compared to the light gray region around each swath.  The colder and dark line segments represent hail swaths due to the hail be colder than surrounding ground.

On the next day, imagery near noon is used to indicate the hail scar due to the previous day’s storms.  Unluckily the swath along the Colorado/Wyoming was obscured by persistent clouds.  On the other hand the swath in western Kansas is evident within the oval: compare the figure below with the previous two figures.

An independent observing system that captured the storms is the MRMS mid-level rotation tracks.  As is seen in the figure below, the pairs of black arrows correspond to the location of rotating storms.  A comparison of the figure below with the previous three figures above provide additional evidence of convective activity where the hail swaths occurred.

Our second case study occurred on 28 July 2018.  Unlike the first case above, no thunderstorms have developed in the image below since it is late morning.  Of particular interest are the three white ovals, one located just south of the Colorado/Wyoming border, the second located in extreme northeast Colorado, while the third is located over east central Colorado. At that time no hail swaths were evident in either oval.  A comparison of the first figure (see beginning of blog) and the figure below can be used to orient the reader about the 3 regions just discussed.

At 0615 UTC 29 July the hail producing storm was evident over extreme northeast Colorado as seen in the GOES-16 ABI 3.9 micron band imagery as displayed below on the left.  Within the white oval in extreme northeast Colorado, two darker line segments were evident in the wake of the storm path as it moved towards the southeast over extreme southwest Nebraska.  Later on that day, severe storms developed and also produced a hail swath; the northern portion of the hail swath is seen in the northern portion of the white oval near the Colorado/Wyoming border.  The thunderstorm producing the hail swath covers the rest of the oval as is seen in the figure below on the right.

On 30 July 2018 at 1415 UTC imagery in the early morning is used to indicate the hail scars due to the previous two convective events (see figure below).  The hail damage scars from the first case are denoted by two black line segments.  Within each oval, hail damage scars are evident. A comparison of the figure below with the first figure of the blog can be used to help the reader identify hail damage scars.

As in the other case, the MRMS mid-level rotation tracks are used an additional source of information.  As is seen in the figures below, the pairs of black arrows correspond to the location of rotating storms.  A comparison of the figures below with the previous figures for this case provide additional evidence of convective activity where the hail swaths occurred.

All of the hail damage scars were also imaged by SNPP-VIIRS as seen in the image below on 31 July 2018 at 1917 UTC.  Black arrows point to scarring locations while the white arrow points to Cheyenne, WY.

We leave it to the interested reader to identify hail damage swaths in the NOAA-VIIRS Day Night Band image displayed below.  Also of interest a number of cities appear in the imagery.

Some Advected Layered Precipitable Water (ALPW) Comparisons Between Florence, Harvey (2017), Maria (2017) and Matthew (2016) with Respect to Rainfall and Severe Weather

This blog entry is by Sheldon Kusselson and in the format of a PDF document:

Florence 2018 Comparisons With Harvey and Matthew and More_Sep21,2018update

Hurricane Florence

The hurricane season in the Atlantic has been quite inactive throughout the majority of the summer, until this past week. Currently there are three active hurricanes located in the eastern, central and western Atlantic Ocean, named Hurricane Helene, Isaac, and Florence, respectively; all with varying magnitude, intensity, and storm tracks. However, with all the present activity, we will focus our sights on Hurricane Florence, since Florence can have major impacts (i.e. flooding, high winds, storm surge and potential power outages) along the southeastern United States. According to the latest forecast models and National Hurricane Center (NHC), Hurricane Florence appears to make landfall along the Carolinas, Thursday morning, 13 September 2018, although the storm track is subject to change, since it is only Monday, 10 September 2018. Refer to the NHC product below. Note that Florence is anticipated to become a Major Hurricane (i.e. hurricane greater than 110 mph) as it makes landfall later in the week.

The Suomi-National Polar-orbiting Partnership (S-NPP) satellite observed Hurricane Florence early this morning at 0505Z, 10 September 2018, using the Near-Constant Contrast (NCC) product. For unaware readers, NCC is a derived product of the Day/Night Band (DNB) that utilizes a sun/moon reflectance model that illuminates atmospheric features, and senses emitted and reflected light sources during the nighttime (i.e. a ‘nighttime visible’ product). Note the NCC imagery is taken during the new moon phase of the lunar cycle, where atmospheric features are still seen in the imagery due to an atmospheric phenomenon called ‘atmospheric nightglow’.

NCC captured the large areal extent of Hurricane Florence, along with the eyewall, circulating clouds and rain bands. Lightning was also observed on the southern side of the hurricane, denoted by the horizontal white streaks. Streaks are produced due to the time discontinuity between the satellite overpass (i.e. on the order of seconds) and the duration of the lightning strike (i.e. on the order of milliseconds).

Below, is a CIRA ALPW surface-850mb animation of the hurricane activity in the Atlantic Ocean between 18Z, 9 September 2018 to 15Z, 10 September 2018. The ALPW product highlights the precipitable water (i.e. a quantifiable measure of how much water vapor is in an atmospheric column) within four layers of the atmosphere (i.e. surface-850mb, 850-700mb, 700-500mb, and 500-300mb). Notice the high precipitable water values with each hurricane (i.e. indicating the horizontal moisture distribution and indicate areas that are favorable for heavy precipitation) as the hurricanes move westward through time.

For the latest updates on Hurricane Florence, click the following link.

Hurricane Lane

Hurricane Lane is making headlines this week, as the storm approaches the Hawaiian islands. As of the latest National Hurricane Center (NHC) update (8 am HST, 23 August 2018), Lane is moving quite slow, with a northwest movement at ~ 7 miles per hour. Within the last day or two, Lane has weakened, but is still a Category 4 hurricane that can bring heavy precipitation, high winds, storm surge and coastal flooding. Some parts of the Hawaiian islands have experienced heavy precipitation already, and more is expected, throughout the next few days. As seen in the NHC image below, the forecasted track shows the ‘probable path’ of Hurricane Lane, but does not indicate the magnitude of the storm. The forecasted track is subject to change.

The Suomi-National Polar-orbiting Partnership (S-NPP) satellite observed Hurricane Lane via Near-Constant Contrast (NCC) product, earlier this morning at 1228Z, 23 August 2018. The NCC is a derived product of the Day/Night Band (DNB) sensor, that illuminates atmospheric features and senses emitted and reflected light sources (i.e. cloud cover and cloud convective tops from Lane) during the nighttime. In the NCC image, notice the large areal extent of Hurricane Lane, as the northern/northeastern periphery of the storm has reached the southern shores of the Hawaiian islands, bringing heavy precipitation rates.

In complement to the NCC, the CIRA -Advected Layered Precipitable Water (ALPW) product (shown below), derived from microwave retrievals, shows high precipitable water values of Hurricane Lane, as the storm moves northwestward. The ALPW product is partitioned into 4 layers, to identify where most of the moisture lies within the atmosphere, and indicates the horizontal moisture distribution. In the animation below, ALPW highlights ‘surface-850mb layer’ precipitable water values, where high values are concentrated within and near Hurricane Lane (orange, dark orange colors), indicating areas that could experience heavy precipitation. Precipitating regions are indicated as ‘missing data’, represented by black colors in the imagery, since microwave retrievals are made in cloudy regions but not precipitating regions. Animation is from 18Z, 22 August 2018 to 15Z, 23 August 2018.

For the latest updates on Hurricane Lane, click on the following NHC link.

Hurricane Hector

Hurricane Hector has been a ‘Major Hurricane’ in the Central Pacific for the past week. At one point, the hurricane was nearly categorized as a Category 5 hurricane, however weakened, and is currently a Category 3 hurricane (i.e. ~120 mph winds as of 8AM Hawaiian Standard Time (HST), 9 August 2018). The storm is moving west at ~16 mph, and was ~350 miles southwest of Hawaii.

Polar-orbiting satellites observed Hector, earlier this morning, via the Near-Constant Contrast (NCC) and the Advected Layered Precipitable Water (ALPW) satellite products. The NCC, a derived product of the Day/Night Band (DNB) sensor, utilizes a sun/moon reflectance model to illuminate atmospheric features, and sense emitted (i.e. city lights) and reflected (i.e. clouds) lights during the nighttime.  NCC image is at 750-m spatial resolution and is taken at 1152Z  (0152 local time), 9 August 2018, during the new moon phase of the lunar cycle. Imagery shows Hector’s large areal extent, eye wall, and cloud cover, along with nearby emitted city lights from the Hawaiian islands.

In complement to NCC, the ALPW product, derived from polar-orbiting satellites,  highlights precipitable water values within 4 atmospheric layers, different from the Blended Total Precipitable Water (TPW) product. The ALPW animation below, shows one of the layers (surface to 850mb), where high TPW values are observed within Hector. High TPW values indicate large amounts of moisture within the surface-850mb layer that are then transported westward with respect to time (i.e. see animation). It is important to note, missing data values are observed in the imagery (i.e. black pixels), representing regions of precipitation. Animation is from 18Z, 8 August 2018 to 15Z, 9 August 2018, showing Hector’s westward movement as the hurricane skirts along the southern edge of the Hawaiian islands. So far, the islands have received some rainfall and high surf along its southern shores. ALPW product is at 16-km spatial resolution.

For more updates on Hurricane Hector, click on the following link.

Dust off the coast of Africa!

Earlier this morning, a large areal extent of dust advected off the coast of Africa. The new National Oceanic and Atmospheric Administration  – 20 (NOAA-20) satellite, launched into orbit back in November 2017, captured the dust via Day/Night Band (DNB) product at 0323Z, 1 August 2018. DNB illuminates atmospheric features, senses emitted and reflected light sources (i.e. dust and clouds in this case), during the nighttime. Notice in the DNB image below, the opaque, milky white features that resemble dust along the African coast and west of Africa. Corresponding cloud cover over the ocean and over land are also seen.

Although the DNB image above is only a static image, in animation of the dust can be seen later in the day, via geostationary data (higher temporal resolution). Clicking on the video below shows dust advecting west, towards the U.S., between 13-16Z, 1 August 2018. Products seen within animation are the GeoColor and Dust products, identifying dust in milky brown and pink colors, respectively.

Satellite observations of dust and forecasting dust can assist in assessing convective initiation, hurricane development/dissipation processes, along with determining visibility levels. As dust advects westward the next few days, Weather Forecast Offices (WFO’s) will determine how dust levels will impact their County Warning Areas (CWA’s), especially along the southeastern U.S. and Puerto Rico (U.S. territory).

Carr Fire

It has been a very active fire season in California. Another fire burning in the state, is the Carr Fire, located in Shasta County, just west of Redding, CA. Over 44,000+ acres have burned with only 3% containment as of 27 July 2018. According to sources, the mechanical failure of a vehicle is presumed to be the cause of the fire.

The Carr Fire was observed by the Near-Constant Contrast (NCC) product, derived from the Day/Night Band (DNB, 0.7um) sensor, apart of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on-board the Suomi-National Polar-orbiting Partnership (S-NPP) satellite. The NCC produces ‘nighttime visible’ satellite imagery, and in this case (see NCC image below, at 0909Z, 27 July 2018), observes emitted city lights, emitted lights from the fires, reflected light from fog and low stratus clouds, along with the reflected light from nearby smoke.

Additionally, a comparison between NCC (bottom-left) and the GOES-16 infrared imagery (3.9um, bottom-right) at ~1050Z, 27 July 2018, is seen below. The NCC highlights the emitted lights from cities and towns, emitted lights from the Carr Fire, along with atmospheric features described earlier.

However, how do users clearly discriminate between emitted lights produced from fire to emitted lights exhibited by cities and towns? This is where GOES-16 3.9um imagery complements NCC.  GOES-16 3.9um can be used to identify ‘fire hotspots’, that emanate high brightness temperature values, near the surface. In the infrared imagery, the ‘fire hotspots’ are clearly discerned, and are seen embedded within the black ellipse.

For more updates on the Carr Fire, click on the following link.

Typhoon Jongdari

By late weekend, Typhoon Jongdari is forecasted to make landfall along Japan’s southern islands. Typhoon Jongdari is expected to strengthen, with an initial northeast storm motion, then elicit a circuitous path, moving westward, within the next 24-hours. The typhoon is anticipated to be at Category 1 hurricane strength (74-95 miles per hour), just before landfall. Heavy rain, strong winds, storm surge and flooding is to be expected.

This morning, Near-Constant Contrast (NCC) data, derived from the Suomi National Polar-orbiting Partnership (S-NPP) satellite observed Typhoon Jongdari at 1617Z, 26 July 2018 (0117, 27 July 2018, local time in Japan). The NCC, a derived product of the Day/Night Band (DNB) utilizes a sun/moon reflectance model to illuminate and sense emitted (i.e. city lights) and reflected (i.e. clouds) light sources during the nighttime.

The first NCC image (below) is a large scale view of the storm, while the second NCC image, is a small scale perspective of Typhoon Jongdari, highlighting the convective cloud tops (areas of heavy precipitation), near and around Jongdari’s circulation. Notice the magnitude of Typhoon Jongdari, and how close the typhoon is to the country of Japan. Emitted lights from Japan, and ambient cloud cover can be seen in the following images.

Large Scale

Small Scale

For the latest updates on Typhoon Jongdari, click on the following link.

Ferguson Fire, CA

The Ferguson Fire erupted last Friday, 13 July 2018 at ~2030 local time.  The fire is near Yosemite National Park, burned 22,000+ acres, and is only 7% contained, as of 20 July 2018. The cause of the fire is unknown and under investigation, while several communities have been evacuated from the area, and one fatality has been confirmed. Expected 3-5 day forecast for Central California is to be hot, hazy, with light and variable winds, that could potentially enhance the fire.

The Suomi National Polar-orbiting Partnership (S-NPP) satellite and the Geostationary Operational Environmental Satellite – 16 (GOES-16) observed the wildfire event, identifying the areal extent of the fire (emitted lights from the fire, seen via Near-Constant Contrast (NCC)) and associated fire ‘hotspots’ (GOES-16 3.9um). NCC and GOES-16 3.9um observed the Ferguson Fire at 0941Z (0241 local time), 20 July 2018.

NCC 

Note the areal extent of the fire (embedded in the white ellipse) but additionally, emitted lights from nearby towns/cities can be seen via NCC.

To discern emitted lights that are from cities/towns to emitted lights from the fire, that is where GOES-16 3.9um complements NCC. GOES-16 3.9um, not only shows the fire location, but indicates the relatively cool brightness temperatures of the fire. See the brightness temperature (degrees Celsius), sampled, in the following satellite image.

GOES-16 3.9um

As the day progressed through late morning, fire temperatures increased significantly, due to increasingly hot, ambient temperatures and dry conditions. See the brightness temperature (degrees Celsius), sampled, in the following satellite image.

GOES-16 3.9um of Ferguson Fire at 1622Z (0922 local time), 20 July 2018. 

For more updates on the Ferguson Fire, click the following link.

Pawnee Fire, CA

In the late afternoon on 23 June 2018, the Pawnee Fire in Northern California initiated and has now spread to 11,000+ acres. As of this morning 26 June 2018, the fire is located a few miles north of Clearlake, CA and has forced thousands of people to evacuate the area, where 20+ structures have been destroyed. The fire is currently 5% contained and the cause of the fire is still under investigation. Additionally, Northern California is currently experiencing ‘D-0’ drought conditions (abnormally dry conditions), according to the US Drought Monitor. The latest California drought conditions can be seen below.

Polar-orbiting satellite imagery observed the Pawnee Fire, along with the Lions Fire, nearby. One of the polar-orbiting satellite products, the Near-Constant Contrast (NCC), illuminates atmospheric features and senses emitted and reflected light sources during the nighttime. The following two NCC images show the ‘Morning Before’ and the ‘Morning After’ the Pawnee Fire started. The first NCC image (shown below) is taken at 0948Z (0248 local time), 23 June 2018. Notice the emitted city lights along Northern California, and no discernible fires in the area.

Morning Before – 23 June 2018

The second NCC image is taken at 0928Z (0228 local time), 24 June 2018. Notice the emitted lights produced from the two fires, seen within the two red circles. The Pawnee Fire is located in the top-left part of the image, and the Lions Fire in the bottom-right part of the image. Areal extent of both fires can be inferred from the satellite imagery, wherein thousands of acres have been burned. Also notice the uniform, fog and low stratus clouds, along the California coast.

Morning After – 24 June 2018

The 3-5 day forecast for the area, is projected to be in the upper 80’s (air temperature), sunny, dry, with light to moderate winds, that could amplify the fire even further. More details on the Pawnee Fire can be accessed via the following link and via Inciweb website.

GOES-16 Split Window Difference Precursor to Convective Initiation

There are two GOES-16 products related to convective activity.  One is related to convective initiation; that is, this product will identify new cumulus that will further develop into mature thunderstorms.  The second product identifies which mature thunderstorms have a high probability of producing severe weather.  However, both GOES-16 products require active cumulus development.  We seek to fill a void by providing a product that aids in the identification of future convective initiation in a clear sky scene.  The purpose of this blog entry has 3 parts: 1) training, 2) two case studies, and 3) both color and gray-scale enhancement tables, applied to GOES-16 split window difference product.

A common goal of the both VISIT and SHyMet programs is to provide training to WFO, CWSU, and National Center forecasters.  One way to provide training information to these users is through the use of readily accessible information that appears on this training blog.  Previous blog training has focused on a variety of weather events; for example, winter weather, severe convection, tropical cyclones, aviation applications, and fire weather.  In this blog entry two case studies that focus on precursors to convective initiation are discussed.

Although there has been somewhat of a lack of severe convection during spring of 2018, two cases have been identified to highlight the use of the split window difference product in the identification of precursors to convective initiation.  They occurred on 15 and 29 May 2018  both in the Texas panhandle.  On 15 May 2018 a slowly moving outflow from previous convection interacted with a low-level convergence boundary.  In contrast, 29 May 2018 focuses on a dryline / cold front interaction event.  As a reminder to the reader, the split window difference product aids in the identification of where cumulus may first develop within clear sky conditions.  We urge the reader to exercise caution in that the use of this product is inappropriate for the identification of severe thunderstorms.  One significant challenge is the development of a satellite enhancement table to highlight features of interest.

To begin with, the 15 May 2018 case will be used to illustrate modification of satellite enhancement tables.  Even though a default color table exists for the split window difference product in AWIPS, we will demonstrate the usefulness of modifying satellite enhancement tables.  An example image is taken at 1607 UTC, see image below:

A black oval is used to denote the clear sky precursor signature in both images on the left side.  Within the gray-scale image, two arrows are used to denote the western and eastern edges of the signature.  An oval was not used due to variations in visual perception, i.e., the precursor may not be apparent to some people if a black oval is used.  Note in particular the lack of clouds within the oval in the visible image located in the top right panel.

To illustrate the utility of the 3 types of enhancements, the following GOES-16 animation is provided:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/swd/swd_4panel&loop_speed_ms=60

Top Left panel: Split Window Difference (10.3 – 12.3 micron) product in the range of -10 to +10 Celsius with the CIRA SLIDER enhancement (http://rammb-slider.cira.colostate.edu/?sat=goes-16&sec=conus&x=5000&y=5000&z=0&im=12&ts=1&st=0&et=0&speed=130&motion=loop&map=1&lat=0&p%5B0%5D=35&opacity%5B0%5D=1&hidden%5B0%5D=0&pause=0&slider=-1&hide_controls=0&mouse_draw=0&s=rammb-slider) overlaid with METARs.

Top right: Visible (0.64 micron)

Bottom left:  Split Window Difference (10.3 – 12.3 micron) product with the default AWIPS color table, in the range of -15 to +15 Celsius.

Bottom right: Split Window Difference (10.3 – 12.3 micron) product with the gray-scale linear enhancement on AWIPS in the range from -10 to 10 Celsius.

The point of the animation is to provide the reader an opportunity to view different enhancements at once.  Of importance are the variations in both the ranges and colors / gray-scale used for each split difference produce panel.  To some, the top left may be preferred while others may prefer the bottom right and yet some will show preference to the bottom left.  One key aspect to keep in mind is that the precursor signature corresponds to clear skies in the visible imagery prior to approximately 1700 UTC.

For completeness, a brief physical explanation of the split window difference signature now follows.  Water vapor in the boundary layer is an absorbing gas to energy at 10.3 and 12.3 microns that is emitted from the earth’s surface.  Water vapor absorbs more energy at 12.3 compared to 10.3 microns; therefore, when the temperature decreases with height the brightness temperature at 12.3 microns is less than the brightness temperature at 10.3 microns, hence the difference is positive.  Along convergence zones, water vapor is transported upwards, making the moist air relatively deeper compared to its surroundings and thus amplifying the channel difference.

Another example of a gray-scale enhancement highlights the usefulness of modifying the range of the split window difference product.  For clarity, the gray-scale loop on the bottom right in the previous animation is repeated along with a modified range (from 1.7 to 10 Celsius):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/swd/swd_2panel&loop_speed_ms=60

This example illustrates the use of changing the scale from -10 to 10 Celsius (top) to 1.7 to 10 Celsius (bottom) while preserving the same enhancement table (linear).  One consequence of decreasing the range of 1.7 to 10 C is to increase the contrast.  The motivation is to provide the reader with another example of how the data can be displayed.  That is, some may prefer the top loop while others may prefer the bottom.

Finally, a larger version of the upper left loop of the 4 panel animation above is shown here as another way to view the data:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/swd/swd_metar&loop_speed_ms=60

The 29 May 2018 case focuses on convective initiation in the northeast Texas panhandle as shown in this animation:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/swd/swd_29may_4panel&loop_speed_ms=60

Top Left panel: Split Window Difference (10.3 – 12.3 micron) product in the range of -10 to +10 Celsius with the CIRA SLIDER enhancement (http://rammb-slider.cira.colostate.edu/?sat=goes-16&sec=conus&x=5000&y=5000&z=0&im=12&ts=1&st=0&et=0&speed=130&motion=loop&map=1&lat=0&p%5B0%5D=35&opacity%5B0%5D=1&hidden%5B0%5D=0&pause=0&slider=-1&hide_controls=0&mouse_draw=0&s=rammb-slider) overlaid with METARs.

Top right: Visible (0.64 micron)

Bottom left:  Same as top left, except the range has been changed to -5.1 to +10 Celsius.

Bottom right: Split Window Difference (10.3 – 12.3 micron) product with the gray-scale linear enhancement on AWIPS, except the range has been modified to 0.9 to 10 Celsius.

In general, upper level cirrus clouds are seen stream from west to east from eastern New Mexico over the Texas panhandle into southwest Oklahoma.  The moisture gradient associated with the dryline is shown with red colors on the moist side and green/yellow/blue colors on the dry side (note the eastward movement of the moisture gradient just south of the cirrus in the southern Texas panhandle).  Followed by convective initiation in the northeast Texas panhandle around 2000 UTC.

The technique for modifying the range on the bottom 2 panels in AWIPS is as follows:

Hold down the right mouse button on the split window difference product, choose colormap.

A GUI will appear titled “Set Color Table Range”, scroll the Min bar until the contrast on the imagery is to your preference.

The above technique applies in general in modifying the enhancement and/or data range.

Further information:

Lindsey et al. (2014): Use of the GOES-R Split Window Difference to Diagnose Deepening Low-Level Water Vapor

Lindsey et al. (2018): Using the GOES-16 Split Window Difference to Detect a Boundary Prior to Cloud Formation

FDTD GOES-16 Applications webinar on the Split Window Difference

 

Hurricane Aletta

Hurricane Aletta has grown tremendously over the past 24 hours, and it is now determined as a Category 4 hurricane, as of this morning, 8 June 2018. Aletta is currently located southwest of Mexico, in the eastern Pacific Ocean. Aletta is moving northwestward, and is forecasted to drop in intensity by the weekend, as the system moves into higher wind shear, colder sea surface temperatures and decreasing ocean heat content. Aletta is quite large in areal extent, where preliminary reports had the ‘eye’ of the storm at approximately 20-miles in diameter. The latest National Hurricane Center (NHC) forecast track of Aletta is seen below, valid at 3pm MDT, 8 June 2018.

Satellite imagery observing Hurricane Aletta is also provided. Below is the Advected Layered Precipitable Water (ALPW) product at 18Z, 8 June 2018, highlighting the areal extent and the moisture profile of the hurricane. Product is derived from a Microwave Integrated Retrieval System (MIRS), derived from polar-orbiting satellites, and is at a 16-kilometer resolution.

The ALPW product identifies, where the moisture is predominately concentrated within four layers (i.e. thicknesses) of the atmosphere: surface-850mb, 850-700mb, 700-500mb, and 500-300mb. In the image below, the color bar is uniform across all layers, where 0-3 inch precipitable water values are seen. For this event, the majority of the precipitable water is found near the surface, where precipitable water values are ~1 inch (orange, red colors), and values decrease aloft. Black colors within Hurricane Aletta, indicate ‘missing data’, where microwave retrievals can be made within cloudy regions, however, not through areas of precipitation.

Additionally, here is the latest RAMMB-SLIDER satellite imagery of Hurricane Aletta, using GOES-16, 0.64um, visible satellite imagery near 21Z, 8 June 2018. Click on the following animation.

For the latest updates on Hurricane Aletta, click the following link.

Storm-relative animations for right-moving and left-moving storms

This blog entry will compare traditional satellite animations of right-moving and left-moving storms with storm-relative animations as observed by GOES-16 visible imagery in AWIPS with the Feature Following Zoom tool.  Also, comparisons will be made between different temporal resolutions, that is, AWIPS CONUS 5-minute versus mesoscale 1-minute sectors.

We will start with the traditional satellite animation which is for the 0.64 micron visible band CONUS sector (5-minute):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/texas/texas1_conus_non-sr&loop_speed_ms=60

In the eastern Texas panhandle, we observe a storm that turns right (towards the southeast) as it intensifies.  Meanwhile, in the northeast Texas panhandle into the Oklahoma panhandle we see a left-moving storm moving northeastward that appears to be moving faster than the right-moving storm.

Compare the animation with a storm-relative animation centered on the right-moving storm (CONUS sector):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/texas/texas1_conus_sr&loop_speed_ms=60

and also compare with a storm-relative animation centered on the left-moving storm (CONUS sector):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/texas/texas2_conus_sr&loop_speed_ms=90

How does the storm-relative animation affect your interpretation of the satellite imagery?  What can you see more effectively in the storm-relative animations?

Fortunately, on this day a Mesoscale sector covered the region of interest, allowing us to make further comparisons with 1-minute temporal resolution imagery.

First, the traditional satellite animation for the Mesoscale sector:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/texas/texas1_meso_non-sr&loop_speed_ms=20\

Compare the animation with a storm-relative animation centered on the right-moving storm (Mesoscale sector):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/texas/texas1_meso_sr&loop_speed_ms=20

and also compare with a storm-relative animation centered on the left-moving storm (Mesoscale sector):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29may18/texas/texas2_meso_sr&loop_speed_ms=20

How does the storm-relative animation affect your interpretation of the satellite imagery?  What can you see more effectively in the storm-relative animations?

Summary:

The storm-relative animations definitely allow the user to more effectively analyze features at cloud top (overshooting tops, cloud motions, gravity waves). Features in the vicinity of the storm may be viewed more efficiently as well (clouds moving into the storm, such as potential inflow feeder clouds).  Also, the storm motion relative to a boundary can be visualized more readily.  This can be particularly useful in situations where you are monitoring storms that may go more parallel or perpendicular to a boundary with obvious consequences on storm intensity.

Storm speed can be seen in a relative sense as well, in this case we adjusted the loop speeds to make them look more favorable, but you may change the loop speed so that they are all the same to prove to yourself how much faster the left-moving storm is moving relative the right-moving storm.  At the end of the URL, the loop_speed_ms= parameter can be edited to your preference, see what effects the same number (i.e., loop_speed_ms=20) has.

 

 

 

Volcan de Fuego, Guatemala erupts again!

Yesterday, Volcan de Fuego erupted again in southern Guatemala. The pyroclastic flow of Fuego surprised many, and as of this morning 4 June 2018, at least 25 people have died, while many others are injured. Locals near Fuego, are in the process of being evacuated from the area.

Fuego erupted around 18 UTC, 3 June 2018, ejecting hot gas, smoke and ash in the atmosphere, where geostationary and polar-orbiting satellites observed the phenomena. Below is a video of the volcanic eruption, utilizing the CIRA-GeoColor satellite imagery from RAMMB-SLIDER, between 18-21 UTC, June 3 2018. Notice the rapid volcanic plume (brownish cloud) that develops and is advected eastward, within the time-frame.

The Suomi-National Polar-orbiting Partnership (SNPP) satellite also observed the volcanic plume. The Visible Infrared Imaging Radiometer Suite (VIIRS) True Color and Imagery Band 5 (11.45um, brightness temperature) both show static images of the events at ~19 UTC, 3 June 2018.  Static images are taken approximately 1-hour after the volcanic eruption started. Both images are courtesy of the NASA Worldview data archive website.

VIIRS True Color Imagery

VIIRS Imagery Band 5 (11.45um, brightness temperatures)

For the latest updates on Fuego de Volcan, click the following link.

Ute Park Fire, New Mexico

The Ute Park Fire initiated and has erupted over the past 24 hours. As of this morning, 1 June 2018, the fire has burned over 8,000 acres and is at zero percent containment, forcing mandatory evacuations. The fire is located in Ute Park, NM and is east of Eagle Nest, NM. Several structures have already been burned, and the cause of the fire is under investigation.

The Ute Park Fire and the Buzzard Fire (that has been burning for several weeks) can be seen in an array of satellite products and imagery, below.

The Near-Constant Contrast (NCC), a derived product of the Day/Night Band (DNB), utilizes a sun/moon reflectance model to illuminate atmospheric features during the nighttime, such as emitted (i.e. wildfires, city lights) and reflected (i.e. clouds) light sources. The emitted light from both fires can be seen in the NCC product below, along with the emitted city lights. Product is at 750-m resolution and image is taken at 0818 UTC, 1 June 2018.

In complement to the NCC, the GOES-16 3.9um, infrared satellite imagery is used to identify the ‘hotspots’ of the fires. In the imagery, brightness temperature values are high, expressing over 90 degree Celsius temperatures for the Ute Park Fire. The Buzzard Fire expressed lower brightness temperature values at this timestamp. Product is at 2-km resolution and image is taken at 0817 UTC, 1 June 2018.

Both fires are seen in the CIRA-GeoColor Product as well, highlighting the smoke from both fires. Video animation is taken between 15-16 UTC, 1 June 2018.

Now where is the smoke from the fires going to go? Utilizing an experimental High-Resolution Rapid Refresh (HRRR) Smoke Model, two forecast products can be used to potentially determine where the smoke is going to go. Both forecast products are the Near-Surface Smoke (expressed in micrograms per meter-cubed) and the Vertically Integrated Smoke (expressed in milligrams per meter-squared). Both products, seen below, are utilizing the 12 UTC run, valid at 00UTC 2 June 2018.

Near-Surface Smoke (below) determines the fire emitted Particulate Matter (PM2.5, also known as ‘fire smoke’) concentrations at approximately 8 meters above the ground.

Vertically Integrated Smoke (below) simulates the total PM2.5 mass within vertical columns over each model grid cell. Vertical columns are approximately 25-km above the ground. Product incorporates the smoke within the boundary layer and aloft, displaying the integral effect of ‘fire smoke’ throughout the atmosphere.

For more updates on the Ute Park Fire, click the following link.

Alberto

As of 29 May 2018, subtropical depression Alberto has been advecting northward, through the southeastern United States. Alberto made landfall yesterday 28 May 2018, along the Gulf Coast, near the Florida Panhandle. Alberto produced heavy rainfall and has the potential for tornadoes, as it pushes north into the Ohio Valley within the next few days. Rain estimates are 3+ inches in several southeastern states.

Below are two static satellite images of Alberto. The first one is the Near-Constant Contrast (NCC), a derived product of the Day/Night Band (DNB). NCC utilizes a sun/moon reflectance model that illuminates atmospheric features, and senses emitted (i.e. city lights) and reflected light sources (i.e. clouds) during the nighttime.  NCC imagery is taken at 0735Z, 29 May 2018, during the full-moon stage of the lunar cycle. Notice the large areal extent of Alberto, engulfing a few southeastern states. Spatial resolution is at 750-meters.

The second image is of the GOES-16 infrared, low-level water vapor imagery (channel 10, 7.3um) at 0737Z, 29 May 2018. Imagery shows the convective, cold cloud tops (cold brightness temperatures, indicated in blue and green colors) of Alberto that can produce heavy precipitation and severe weather. Spatial resolution is at 2-kilometers.

More updates on Alberto can be seen by the following link.

Buzzard Fire, New Mexico

A fire has initiated in western New Mexico, denoted as the Buzzard Fire. Currently, the fire is burning within the Gila National Forest in Catron County, New Mexico. As of this morning, 24 May 2018, the fire has burned 4,500 plus acres. The cause of the fire is unknown and is under investigation. New Mexico has been experiencing very dry conditions and exhibits D1 to D4 (moderate to exceptional) drought conditions throughout the state. Refer to latest US Drought Monitor image of New Mexico below.

The Buzzard Fire has also been detected in the satellite imagery. In the GOES-16 3.9um infrared image (below), thermal anomalies (yellow to red colors) have been detected from the Buzzard Fire. Satellite image is taken at 0912Z, 24 May 2018 and is at a spatial resolution of 2 -kilometers.

Additionally, the fire has been seen via the Near-Constant Contrast (NCC) product, a derived product of the Day/Night Band (DNB), that illuminates atmospheric features, and senses emitted (i.e. lights from fires) and reflected light sources (i.e. clouds) during the nighttime. Product image was taken at 0910Z, 24 May 2018, and shows the emitted light produced from the Buzzard Fire, located west of Interstate – 25. Product spatial resolution is 750-meters.

For latest updates on the Buzzard Fire, refer to the ‘Active Fire Mapping Program’ website.

Fog event of 22 May 2018 in the midwest – focus on nighttime microphysics RGB

During the overnight and morning hours of May 22, 2018 there was widespread fog over the upper midwest region centered around Wisconsin.  We’ll start by looking at the familiar fog product (10.3 – 3.9 micron) overlaid with ceiling (top left: hundreds of feet AGL) and visibility (bottom in miles):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/22may18/fog&loop_speed_ms=180

Recall in the fog product, high clouds composed of ice are negative, while low cloud OR fog is positive with increasing confidence in magnitude (darker blue).  Note the positive values cover a rather large area, however it’s not uniform and there appear to be other cloud layers also present.  Note the higher cirrus (indicated in black colors) moving in from Nebraska into southwest Iowa (likely associated with prior convection).  Also note the regions of gray in central and northern Wisconsin, these can be interpreted as clouds that are less likely to be low clouds or fog, however keep in mind this cloud layer may be obscuring low clouds or fog underneath therefore there is a need to check surface observations.

In the GOES-R series, we now have additional RGB products that can provide more detailed and potentially additional information compared to the fog product.  For example, the nighttime microphysics RGB:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/22may18/large&loop_speed_ms=180

If you’re unfamiliar with this product, information can be found on the quick guide:

http://rammb.cira.colostate.edu/training/visit/quick_guides/QuickGuide_GOESR_NtMicroRGB_final.pdf

What additional information can be observed in the nighttime microphysics RGB that we were unable to observe in the fog product?  There are additional colors over regions that were displayed as various shades in blue (fog or low cloud) in the fog product, also, there are additional colors in regions that were depicted as gray in the fog product.  One of the primary advantages to the nighttime microphysics RGB is that it discriminates between low cloud and fog as observed by GOES.

Let’s first focus on eastern South Dakota to extreme southwest Minnesota into northwest Iowa.  The fog product showed positive brightness temperature difference values which would correspond to low cloud or fog.  The nighttime microphysics product over the same region is dull (darker) aqua.  Our assessment of dull aqua is based on a comparison to other aqua colored regions, for example, look further east in Minnesota where the aqua color is brighter which is more likely to be low cloud.  This agrees with surface observations which indicate 1/4 mile visibility during the period when the dull aqua increases.

Now let’s focus on central Wisconsin.  In the fog product, this was a mix of blue and gray colors, the gray represents less confidence in low cloud / fog while the blue is more confidence in low cloud / fog.  In the nighttime microphysics RGB we see a mixture of shades of green, along with some purple and localized red.  This provides information on the different cloud top heights.  Observations indicated occasional light rain in these regions, corresponding to what you may expect with thicker clouds.

Next, let’s focus in on the region indicated by the red box below:

During the loop, note a small region of green passes through the observation site in the red box, after passage we observe a dull aqua color.  The various shades of green correspond to low to mid level clouds composed of water droplets.  After the passage of this cloud we observe dull aqua (fog) and the observation indicates that fog has formed.  Presumably after the cloud goes by, radiation fog formed in this case.

Finally, we will conclude with a challenging scene for interpretation in northern Illinois, southern Wisconsin and the eastern half of Iowa.  The most obvious feature is a region of high clouds as indicated by near black or dark blue colors in the RGB product advecting eastward.  It’s important to note that fog may exist but simply be obscured by high clouds above it and indeed we observe this over our region of interest.  Now let’s focus in on north central Illinois near the Wisconsin border where interpretation is particularly challenging, there is a variety of colors that are changing and advecting through during the loop.  At the start of the loop, observations indicate widespread fog with low visibility, however the dull aqua may not appear as widespread as you may expect relative to the observations.  There is quite a bit of aqua and light green at this time.  This would indicate low clouds are present so that the fog is being obscured by a cloud deck over the fog.  A bit later in the loop, after the passage of the small patch of high clouds, we observe more regions of dull aqua (fog) before later becoming obscured by more widespread high clouds.  Note that during the loop, the observations do not change appreciably in northern Illinois / southern Wisconsin meaning that most of this area remains in fog despite the changes we see in the nighttime microphysics RGB.

In summary, we analyzed a challenging scene where fog was widespread but so were different types of clouds.  The nighttime microphysics RGB showed its utility in identifying some areas of fog but it also showed us that fog can be obscured by cloud layers above it.  When this is the case, using both surface observations and satellite imagery in tandem is critical to understanding the event.  In other words, where we see the dull aqua in the nighttime microphysics RGB our confidence in fog should be high, but the absence of this color does not necessarily mean fog is not present.

Hail swath observed by GOES-16

On 14 May 2018, a severe thunderstorm near Denver, Colorado resulted in accumulations of hail (reports of 2 to 6 inches in depth locally).

The hail swath left by the thunderstorm can easily be observed in GOES-16 imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/14may18/&loop_speed_ms=60

After the passage of the thunderstorms.  The hail swatch can be seen as a line, see annotated image below for the hail swath:

The ground is white where hail accumulations occurred, much like snow cover.  This is quite reflective in the visible (0.64 micron band) shown in the upper left, therefore it is white.  At 1.6 microns (upper right), the hail absorbs radiation readily, therefore it is dark at this wavelength (black).  In the lower left is the 10.3 micron band, the hail swath is colder than the surrounding ground, therefore it will have lower brightness temperatures than the surrounding ground.  In this color table, yellow is colder than orange.  Finally, the Day Cloud Phase Distinction RGB in the lower right.  The Red component is the 10.3 micron band, the Green component is the 0.64 micron band and the Blue component is the 1.6 micron band.  The hail swath shows up as green.  Why?  There is a strong contribution from the visible band (green) since the hail swath is highly reflective.  There is little contribution from 1.6 microns since the ice absorbs efficiently (dark) and also from 10.3 microns since it is only slightly colder than the surrounding ground. Snow cover typically displays as green in this RGB for these reasons, in fact snow cover can be observed over the western portions of this scene along the mountains.

 

Colorado Dust Storm

Yesterday, 17 April 2018, there was a large dust storm that occurred in the state of Colorado. The dust storm initiated due to dry and and very windy conditions, originating from the Great Sand Dunes National Park and Preserve, near Alamosa, Colorado. Throughout the afternoon, visibility was quite low, in portions of the state.

Check out the surface observations (via RAP Real-Time Weather), between 16-22Z, 17 April 2018. Over time, notice the dollar sign and infinity weather symbols, depicting dust and haze in central and eastern Colorado.

The dust storm was also seen via satellite by the Suomi-National Polar-orbiting Partnership (S-NPP) at 1843Z and 2026Z, 17 April 2018. Below are two satellite products observing the atmospheric phenomena.

VIIRS True Color Product (via RAMSDIS Online) from S-NPP.

Notice the ‘light, milky brown’ feature, emanate from south-central Colorado, where the Great American Sand Dunes reside. The feature elongates all the way to the Colorado/Kansas border, at the time of the overpasses.

VIIRS Blue Light Dust Enhancement Product (via RAMSDIS Online) from S-NPP.

See the bright pink feature, implying the areal extent of dust, advected eastward, seen from the two S-NPP overpasses.

For interested viewers, refer to the following social media link to check out a video of the dust event.

Oklahoma Wildfires

Over the weekend, there have been several wildfires in western Oklahoma. The US Drought Monitor , as of 12 April 2018, has Oklahoma experiencing extreme and exceptional drought conditions. Refer to the US drought monitor image of Oklahoma, below.

 

The fires in Oklahoma have burned over 300,000 acres as of 16 April 2018. Using the Suomi-National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites, one can see the evolution of the fires during the nighttime. Below, is the Near-Constant Contrast (NCC), a derived product of the Day/Night Band (DNB), that utilizes a sun/moon reflectance model that illuminates atmospheric features, and senses emitted and reflected light sources during the nighttime. S-NPP and NOAA-20 are approximately 50-minutes apart, and produced satellite imagery between 7-10Z, 14 April 2018. The NCC imagery is at 750-m spatial resolution, and imagery is produced during no moonlight conditions (during new moon phase of the lunar cycle). Refer to the NCC video below.

*Note the NOAA-20 data is currently non-operational as the data is going through operational testing and evaluation.*

Notice the emitted city lights from Oklahoma City, OK and the variance an emitted lights produced by the fires. But the question becomes, how can one tell what is emitted light from the fire and what is emitted light from the cities and towns? This is where the infrared imagery band (I-4, 3.74um) comes into play. The I-4 band senses anomalous hot spots from fires, compared to the ambient environment. I-4 band data is displayed in brightness temperatures, in degrees Kelvin and is at 375-m spatial resolution. Refer to the infrared video below, from 7-10Z, 14 April 2018.

Notice, the I-4 band can clearly see where the fires are located (red and black colors). The relatively warm bodies of water (orange colors), and nearby cloud cover (blue and white colors) can also be seen by the imagery band.

To receive more updates on the Oklahoma wildfires, refer to the following InciWeb site.

Atmospheric River: Northern and Central California

The state of California, is about to experience an atmospheric river this weekend. For readers that are not familiar with atmospheric rivers, they are long moisture plumes that originate from the tropical/subtropical regions that advect to higher latitudes. Atmospheric rivers are capable of producing large amounts of precipitation, in the forms of rain and snow and can lead to flooding in low-lying areas.

For central and northern California, precipitation will start today, 5 April 2018, and last through Saturday, 7 April 2018. Precipitation will vary depending on location, and forecasts can be seen here.

Satellite imagery products, highlighting the atmospheric river can be seen below. They consist of the Blended Total Precipitable Water (TPW), Advected Layered Precipitable Water (ALPW) and the Near-Constant Contrast (NCC) products.

NCC

At 1109Z, this morning’s SNPP overpass highlights the NCC product (at 750-m resolution) that utilizes a sun/moon reflectance model that illuminates atmospheric features, and senses emitted and reflected light sources during the nighttime. Notice the large low-pressure system, embedded with the atmospheric river, located west of the United States.

Blended TPW

The Blended TPW product is derived from several satellite sources, and is at a spatial resolution of 16 kilometers and temporal resolution from 1-3 hours. The product is useful in identifying rich moisture plumes such as the one recognized this morning, seen below, at 0341Z, 5 April 2018. A limitation of the Blended TPW is that it determines the ‘total’ TPW throughout the atmosphere, however, does not differentiate how much TPW exists in specified layers in the atmosphere (e.g. surface-850mb).

Advected Layered Precipitable Water (ALPW) 

The ALPW product, unlike its predecessor, Blended TPW, incorporates model data, in this case, the Global Forecast System (GFS) wind data. ALPW addresses where the TPW is predominately located within the atmosphere, separated by 4 layers, the surface-850mb, 850-700, 700-500mb and 500-300mb. The video below shows the atmospheric river advecting form the East Pacific to north and central California, between 4-5 April 2018.

Colorado Fog

Fog engulfed northeastern Colorado this morning. Thick fog persisted over several hours, along the northern, I-25 corridor and eastern Colorado plains. Real-time surface observations below, can point out the foggy areas, indicated by the horizontal, parallel, pink lines. Surface observations (05-14Z, 26 March 2018) are over Colorado, and the neighboring states.

In complement to surface observations, fog can be seen via satellite imagery. The following two satellite images show the Near-Constant Contrast (NCC) product, that illuminates atmospheric features (e.g. liquid and ice clouds) and senses emitted lights (i.e. city lights) during the nighttime, in comparison to the GOES-16 satellite fog product. For both images, the domain of interest is highlighted by the large box over northeastern Colorado and the time is approximately 0915Z (0315 local time), 26 March 2018.

The NCC, below, shows cloud cover over the majority of the state along with emitted city lights. An interesting feature to point out in the imagery, is the difference in cloud texture. The clouds within the large box are grey and smooth, in comparison to the clouds southeast of the box, that are rather apparent and reflective . If one had not looked at surface observations, how could one tell where the fog is located? That is, how could one tell where the liquid water clouds (i.e. fog) are located, in comparison to the ice clouds? The GOES-16 Fog product assists in addressing the question.

NCC

The GOES-16 Fog Product (below) is a difference channel product between the 3.9um and 11.3um spectral channels, where negative values (white colors), indicate liquid water clouds and positive values (magenta) indicate ice clouds. The GOES-16 Fog Product can differentiate between the two types of clouds easily, where liquid water clouds are predominately located within the domain of interest. Additionally since fog was reported by surface observations in this domain, we can be confident that low-lying liquid water clouds were present this morning, in northeastern Colorado.

GOES-16 Fog Product (3.9um – 11.3um)

Faka-Union Fire (Southwest Florida)

The Faka-Union Fire, located in southwest Florida has burned over 9,000 acres with only 50% containment. The fire is located near the Picayune Strand State Forest. The fire started out as a ‘prescribed burn’ last weekend, but due to erratic weather conditions, started to burn out of control. The smoke and fires have caused temporary road closures in southwestern Florida, however, as of 9 March 2018, no structures have been burned. For additional information on the Faka-Union Fire, click the following link.

The latest CIRA – GeoColor loop of the fire via the CIRA-RAMMB Slider between 15-18 UTC, 9 March 2018 (shown below). Notice the elongated trail of grey/white smoke, emanating from the fire.

 

Below, is the latest Near-Constant Contrast (NCC) satellite imagery of the Faka-Union fire from this past week, 2-8 March 2018. NCC imagery is also known as ‘nighttime visible’ imagery, that can identify atmospheric features, and sense emitted and reflected light sources during the nighttime. All times are between 6-8 UTC. Notice the change in emitted lights from the fire (embedded in the yellow circle). The fire is close in proximity to the emitted city lights of Naples, Florida.

 

To get an idea of where the smoke from the fire will disperse, one can utilize the ‘experimental’ High Resolution Rapid Refresh (HRRR) Smoke Model. The model had been developed to simulate emissions and the transport of smoke from wildfires. The model is at 3 kilometer spatial resolution and is initialized everyday, at 00, 06, 12, and 18 UTC, and the model produces 36-hour forecasts.

For the Faka-Union Fire, click on the following HRRR Smoke link, to see where the smoke from the fire is forecasted to disperse. The model animation was initialized at 12 UTC, 9 March 2018.

Synthetic imagery from the NSSL WRF-ARW for 7 March 2018 event

A nor’easter occurred on 7 March 2018 which resulted in heavy snow, strong winds and rain across portions of the Northeast U.S.  In this blog entry we’ll examine the performance of the NSSL WRF-ARW via synthetic water vapor imagery in relation to the cyclogenesis aspects.

The following 4 panel display:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/7mar18/synthetic_goes_compare/&loop_speed_ms=60

shows the following:

Upper left:  GOES-16 7.34 micron imagery (5 minutes)

Lower left: NSSL WRF-ARW synthetic 7.34 micron imagery from the 00Z 7 March run (hourly)

Upper right:  GOES-16 6.95 micron imagery (5 minutes)

Lower right: NSSL WRF-ARW synthetic 6.95 micron imagery from the 00Z 7 March run (hourly)

Early in the loop we observe colder cloud tops offshore, adjacent to a dry slot just west of that region, followed by colder cloud tops  associated with proximity to the upper low.  Soon thereafter, we see the rapid development of clouds in proximity to the upper low (the red oval on the image below):

The warm conveyor belt is denoted by the red “WCB” at this time.  Up to this point, this may be considered a cold air type of cyclogenesis event.  However what happens afterwards in the yellow oval above would transition this to an (more intense) instant occlusions type of cyclogenesis.  In the yellow oval, watch the development of convection.  This signifies the development of a secondary warm conveyor belt which peels cyclonically from the warm conveyor belt back towards the position of the upper low.  Once occlusion begins, this is generally referred to as the TROWAL airstream.  The significance of this is that the TROWAL advects air from the warm sector, back towards the low, allowing more baroclinic energy for the cyclone to act upon.  This generally results in rapid pressure falls, and associated hazardous weather (heavy precipitation, strong winds etc.).  A conceptual diagram with these features can be viewed on slide 3 of this training module: http://rammb.cira.colostate.edu/training/visit/training_sessions/goes_r_cyclogenesis_life_cycle/video/

The NSSL WRF-ARW output, as viewed via the synthetic imagery generally did a good job capturing the development of the various components of cyclogenesis discussed above, albeit the position was slightly off.  The synthetic imagery provides an efficient visual comparison between model output and observations (GOES), allowing for a rapid assessment of how the model is performing.  This assessment in model forecast confidence allows the forecaster to have more or less confidence in future forecast hours.  The region of rapidly cooling cloud tops denoted by the red oval shows a more uniform extent of colder cloud tops compared to the NSSL WRF-ARW synthetic imagery.  This is primarily due to a known weakness in the WSM6 microphysics scheme used in the NSSL WRF-ARW.  Due to this known weakness, it’s the location and timing of the colder cloud tops that matters, not so much the areal extent of colder cloud tops.

The GOES-16 1-minute visible imagery provided a spectacular view of the development of the convection associated with the TROWAL:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/7mar18/vis/&loop_speed_ms=60

An alternate view of the same scene is the Day Cloud Phase Distinction RGB, which provides different colors to various growth stages of the convection:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/7mar18/day_cloud_phase/&loop_speed_ms=60

 

Tropical Cyclone Dumazile

A tropical cyclone has been present in the Indian Ocean, the past few days. Yesterday, on 4 March 2018, tropical cyclone Dumazile skirted along the coasts of Madagascar and La Reunion bringing high winds, heavy rain and storm surge along the way. To persons that are not familiar, Madagascar is located on the southeast side of Africa, a remote island, embedded in the western Indian Ocean.

A Day/Night Band (DNB) image below shows the location of Dumazile, just east of Madagascar at 2137Z , 4 March 2018 (0037Z, 5 March 2018, local time). The nighttime satellite image, highlights the tight-circulation, the cloud convected tops and the large areal extent of Dumazile. Convected cloud tops are indicative of heavy precipitation.  As of early this morning, 5 March 2018, Dumazile had maximum sustained winds of 120+ miles per hour (mph) and is forecasted to move south, southeast (i.e. away from Madagascar) at approximately 13 mph, according to the Joint Typhoon Warning Center (JTWC).

For more information on Dumazile, click on the following web-link.

East Coast Weather….

Winter Storm Riley inundated the northeast United States with strong winds, storm surge and high amounts of rain and snow. Current snowfall totals over the northeast can be seen via the National Weather Service – Snowfall Reports web-link.  The screenshot below, shows the snowfall distribution over the northeastern United States, with current snowfall observations (as of 2230 UTC, 2 March 2018) ranging from just a few inches (blue colors) to over 20 (red, maroon colors) in some areas!

A Near-Constant Contrast (NCC) image (below) taken at 0645 UTC, 2 March 2018, shows the large, areal extent of Riley, as Riley produced damaging winds and power outages across the northeast. Corresponding cloud cover (reflected light sources) and city lights (emitted light sources) can be also be seen in the imagery.

Another feature to highlight in the NCC imagery is the cold front across the state of Florida. Using the same image from above and zooming in to the state of Florida, notice the ambient cloud cover and elongated line of clouds, expressed horizontally.  Although this is a static image at 0645 UTC, 2 March 2018, one can verify the elongated line of clouds is a cold front passing through the state, via surface observations.

Using surface observations at a similar time stamp (0658 UTC, 2 March 2018), there is a dramatic shift in winds, from north-northwesterly in northern Florida, to west-southwesterly winds in southern Florida (see red circle). The rapid change in wind direction is an indication of a front moving through the area, in this case, a cold front.

To see the cold front advect south, through the state of Florida, see the following animation. Notice the change in air temperatures (values in red), from low-to-mid 70’s to the upper 50’s, and lower 60’s. Animation is from 0058-1458 UTC, 2 March 2018. (Images courtesy of RAP Real Time Weather data)

Mississippi and Ohio River Flooding

More flooding has occurred over the past weekend, due to a series of storms passing through the Lower Mississippi River Valley and along the Ohio River. Storm totals across the area were in the range of 4-8 inches of liquid precipitation. In the image below, National Weather Service (NWS) – Paducah, Kentucky shows total precipitation values along the Mississippi and Ohio Rivers, and displays the states of Missouri, Illinois, Tennessee, Indiana and Kentucky on 25 February 2018. For a zoomed-in image click on the following NWS-Paducah social media link.

In complement to these totals, one can also see the magnitude of the flooding via the VIIRS Flood Detection Map product, that can be seen via the RealEarth website. There are 2 subsequent images below. The first image shows a google map, in this case, covering where the Lower Mississippi River Valley and the Ohio River intersect, along with the neighboring states.

The second image shows the VIIRS Flood Detection Map product over the same domain. The product highlights the areas of inundation (yellow, orange and red colors) in relation to the open water (blue) and land (brown). In the southeast portion of the image (bottom-right corner) one can see ‘grey colors’ that are an indication of cloud cover in the area. The images were taken at 1932 UTC, 26 February 2018 and are at a spatial resolution of 375 meters. Notice, the inundation areas are along the rivers, but there are significant amounts of flooding just west of the Mississippi River, and north and south of the Ohio River. What makes matters worse, is more rain is expected within the next 3-5 days, most likely exacerbating the situation.

Flooding across the CONUS

Within the last few days, extensive flooding has occurred due to heavy precipitation from Texas all the way to Michigan. Just look at the NOAA NWS River Forecast map, that encompasses river gauge data across the CONtinental United States (CONUS) on 23 February 2018. Each of the data points exhibit the magnitude of flooding. Notice the range of colors, depicting river gauges that are experiencing no flooding (green), near flood stage (yellow), minor flooding (orange), moderate flooding (red) and major flooding (purple).

If  one zooms in a little bit closer at the state of Michigan, one can see the range of flooding across the state.

In complement to river gauge data, one could also see the magnitude of flooding, utilizing the Visible Infrared Imaging Radiometer Suite (VIIRS) Flood Detection Map product via the RealEarth data portal. This product is at a spatial resolution of 375 – m spatial resolution and the product’s algorithm calculates the floodwater fraction percentage of a pixel (i.e. how much of a pixel is flooded, expressed in percent).

The example below shows southern Michigan and all the nearby cities (bottom-left) and the VIIRS Flood Detection product (bottom-right), highlighting the areas of floodwater (yellow, orange, and red colors) in the center of the image, and around the state. The images were taken at 2055 UTC, on 22 February 2018. The product also discriminates between different scene types, such as: ice, open water, land, clouds, cloud shadows, mixed ice and water, and snow. Notice the vast areas of cloud cover (grey), cloud shadows (dark grey) to the west and the mixed ice and water (purple) and open water (blue) over Lake Huron.

There is more rain and snow expected in the forecast this weekend for Michigan, potentially leading to more flooding.

Vortices off the California coast on 5 February 2018

GOES-16 captured some amazing imagery on the development of multiple vortices off the coast of southern California on 5 February 2018:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/5feb18/&loop_speed_ms=60

 

Fuego de Volcan, Guatemala

A volcanic eruption took place on 1 February 2018 over the country of Guatemala. If you don’t know where exactly Guatemala is, it is located in central America, bordering the countries of Mexico, Belize, El Salvador and Honduras. Guatemala has over 30+ volcanoes within the country, however only three (Pacaya, Fuego and Santiaguito) are currently active. The volcano that erupted was ‘Fuego de Volcan‘ (i.e. ‘Fire Volcano’ in English), located in southern Guatemala. The volcanic eruption sent multitudes of ash and smoke into the atmosphere and over the communities nearby.

To monitor Fuego de Volcan, one can utilize polar-orbiting satellite data, such as the Day/Night Band (DNB, 0.7 um) sensor, that illuminates atmospheric features and senses reflected and emitted light sources during the nighttime (i.e. emitted lights from fires, volcanic eruptions). In complement to the DNB, is the infrared, imagery band (I-4, 3.74 um) to identify hotspots. Here are animations from the past few days, 31 January 2018 – 2 February 2018, of both the DNB and I-4 satellite imagery of Fuego de Volcan. Note that all satellite images were taken during the nighttime, between 7-8Z, or 1-2 am local time.

DNB

Click on the animation. Notice within the red box, the continuously changing emitted lights that are produced from Fuego de Volcan, and one can see how close this volcano is in proximity to the neighboring cities just to the east and northeast, and Lago de Atitlan (Lake Atitlan, in English) to the northwest.

I-4

Click on the animation. In the infrared imagery, notice the hotspots, areas significantly hotter than their surrounding environment, produced by Fuego de Volcan (seen in bright white colors, exhibiting brightness temperatures of 300K and higher).

Mayon Volcano Eruption

The Pacific Rim, has been quite volatile throughout the years, ranging from volcanic eruptions to earthquakes to tsunami’s, that are produced along the Rim or nearby. Yesterday, 22 January 2018, around noon local time, the Mayon Volcano in the Philippines erupted where fragments of lava, ash and steam ejected into the sky. The volcano is located in the Mayon Volcano Natural Park, approximately 300 miles southeast of the Manila capital of the Philippines. Several thousands of people have been evacuated from the area and have been told to stay clear of the area by local authorities.

Below, is a complementary imagery band (I-4), infrared image, of the ‘hotspots’ of the volcano, several hours after the eruption occurred at 1733Z, 22 January 2018. Note that at this time-stamp, the image is taken during the nighttime hours, that is, the local time is 0133, 23 January 2018. The spatial resolution of the image is at 375 meters.

In the imagery, one can see the hotspots of the volcano, within the white circle, seen in the yellow, orange and red colors, expressed in 310K + brightness temperatures values. What is also noticed, are the surrounding clouds and convection (seen in navy blue colors) within the domain. These features are low-lying clouds and convection due to the significantly cooler temperatures these features express, near 270K, in comparison to the green-yellow colors that exhibit the land/ocean temperatures, ~290K.

What cannot be seen in the imagery are what areas are clouds and what areas are volcanic ash, within the white circle? This question is answered utilizing the EUMETSAT Ash product via the CIRA/RAMMB slider, below. Look closely at the video link below, between 15-19Z, 22 January 2018, which highlights the evolution of the volcanic ash plume (seen in pink) from the Mayon Volcano within the white rectangle. The ash can be easily distinguished from the ambient low-level clouds, seen in light-green colors.

2018 Nor’Easter and NCC!

To kick of the new year, 2018, we start off with a Nor’Easter that has developed over the East Coast and has brought freezing rain, sleet, snow, and high winds to the coastal areas, ranging from northeast Florida, the Carolinas to the New England areas. This particular storm experienced rapid cyclogenesis: a significant decrease in pressure of the low-pressure system (i.e. nor’easter) within a short period of time, producing high amounts of precipitation and high winds for the states along the East Coast.

The latest Near-Constant Contrast (NCC) imagery (shown below) highlights the magnitude of this storm as of this morning, 4 January 2018 @ 0614Z (~0114 local time). The NCC imagery product, is a derived product of the Day/Night Band (DNB) which utilizes a sun/moon reflectance model that illuminates atmospheric features, senses reflected and emitted light sources and monitors the distribution of clouds during the nighttime. At the time the satellite image was taken, the center of the storm appears to be located just east of the state of North Carolina, as it makes its way north to the New England states. What one can also see is the corresponding snow fields that were produced after the storm passed, seen in the red rectangle. The snow fields stretch from southern Georgia all the way to the Carolinas in this image. In complement to the imagery, in the top-right corner, is the moon percent visibility and moon elevation angle, implying the moon provided adequate moonlight and was above the horizon when the satellite image was taken.

NCC @ 0614Z, 4 January 2018

4 January 2018 explosive cyclogenesis event

GOES-16 imagery captured the spectacular explosive cyclogenesis event on the eastern seaboard on 4 January.  First, we’ll look at the 3 GOES-16 water vapor channels along with the air mass RGB product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/4jan18/4panel_wv&loop_speed_ms=60

During this loop we see an instant occlusion type of cyclogenesis.  We also see the development of a sting jet, annotated on the 0852 UTC image:

Next, we’ll look at 1-minute imagery of the visible band (0.64 um), Day Cloud Phase Distinction RGB product, IR (10.3 um) and True Color RGB (only available at 5 minute intervals):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/4jan18/4panel_vis&loop_speed_ms=30

The loop is zoomed in over the surface low.  The Day Cloud Phase Distinction product adds additional value in terms of variable cloud heights, with colder clouds being glaciated which shows up from the 1.6 um band contribution and even colder clouds from the 10.3 um band contribution.

High cloud obscuration during a lake-effect snow event

When analyzing satellite imagery during lake-effect snow events, one is interested in looking at the low-level clouds associated with the snowbands.  However, sometimes high clouds obscure the low-level clouds making analysis from a satellite imagery perspective more challenging.  An example of high cloud obscuration can be seen in this GOES-16 loop from 2 January 2018:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/2jan18&loop_speed_ms=60

Upper left panel: Day Cloud Phase Distinction RGB product (R: 10.35 um, G: 0.64 um, B: 1.61 um)

Upper right panel: Visible (0.64 um)

Lower left panel: IR (10.35 um) with default color table

Lower right panel: IR (10.35 um) with alternate color table for winter

In the first half of the loop, there exists considerable high cloud cover which shows up quite well in the IR imagery as the colder cloud tops, and in the visible imagery as cirrus clouds moving in a different direction than the low-clouds over the lakes.  In the later half of the loop, there is less high cloud obscuration over Lake Erie where the lake-effect snow band can be easily seen.  Over Lake Ontario, there is much more high cloud cover to obscure the band, however by late in the loop the high cloud coverage is less over the western and south central portions of Lake Ontario.  This can be most easily seen in the Day Cloud Phase Distinction RGB product since high clouds stand out as red (colder, thus more red gun (10.35 um) contribution).  The low clouds associated with the lake-effect snowbands show up well in the visible band, thus appear light color in combination with the 1.6 um band.  The RGB product gave indications of the presence of the lake-effect snowband, which can be confirmed with this radar reflectivity image which shows a significant lake-effect snowband:

At times during lake-effect snow events, the clouds of most interest can be obscured with high clouds, therefore it is important to know what other satellite products are available and may be of use in determining where lake-effect snowbands exist.  Also, be aware of alternative techniques for viewing the imagery, on the animation above, click on rock and turn up the loop animation speed.  Can you delineate the low-clouds associated with the lake-effect snowband more easily?

Snow in the southeastern US!

Due to strong, cold, upper-level low that swept through the southeastern United States last weekend (8-9 December 2017), there were variable snow totals that accumulated from southeastern Louisiana, all the way to the Appalachian Mountains.  Snow totals varied from a trace of snow to 10 inches plus in some areas.  The local NWS-Atlanta, GA, has some updated snow totals from this uncommon December snowstorm.

Even more fascinating, the large swaths/fields of snow that impacted the southeastern United States can be seen via satellite. The Suomi-National Polar-orbiting Satellite (SNPP), in which, the Visible Infrared Imaging Radiometer Suite (VIIRS), an instrument on-board SNPP is utilized here. VIIRS has 22 spectral channels, and the following satellite imagery is produced from one of those spectral channels; the Imagery Band (I-1) (0.64um) visible channel. This channel is at a high spatial resolution (375-m) and can see the snow swaths extending from Louisiana, Mississippi, Alabama, Georgia to the Carolinas, during the daytime (i.e. afternoon) hours.

Three separate, daily, visible images (9-11 December 2017) are provided showing the areal extent of the snow, and how the snow diminishes, due to solar heating, throughout the following days. It is important to note, that for 9 December 2017, the snowstorm just passed through the area hours before the satellite image was taken.

9 December 2017 @ 1906Z

10 December 2017 @ 1843Z

11 December 2017 @ 1826Z

For more information on the December snowstorm, click the flowing NWS -Peachtree City, GA link.

Thomas Fire

Last week, more California wildfires had been initiated northwest of the Los Angeles Metropolitan area. Out of the fires that have spurred up in the last week, the Thomas Fire is the largest one. Over 230,000+ acres have been burned by the Thomas Fire, with approximately 15% of fire being contained so far. The cause of the fire is still under investigation and over 700+ structures have been burned as of 11 December 2017. Additionally, several thousands of people have been evacuated from the area. The latest updates on the Thomas Fire can be seen via the ‘Inciweb’ weblink.

Here is the latest Day/Night Band (DNB) satellite imagery, highlighting the evolution of the fire during the nighttime hours throughout the past week. Satellite imagery before the fire (4 December 2017), the day after the fire started (5 December 2017) and a week after the fire started (11 December 2017) are shown in the following images below. In the satellite imagery, emitted lights of the Thomas Fire and corresponding smoke can be seen, along with the nearby, emitted city lights of the Los Angeles metropolitan area. The Thomas Fire, initially developed near Santa Paula, California, and has migrated to the north, northwest of Los Angeles, throughout the past week.

4 December 2017 – Before the Thomas Fire initiated.

5 December 2017 –  The Day after.

11 December 2017 –  Approximately a week after fire initiated.

An animation of the fire from 4-11 December 2017 can also be seen, showing the evolution of the fire via the following link.

December Wildfires in California

More wildfires are ravaging the California landscape again, as hot, dry and windy conditions persist over the west coast. Fires this time, have initiated and developed to the north and northwest of Los Angeles, California. Several fires have been identified and named such as the ‘Thomas’, ‘Creek’ and ‘Rye’ Fires. The Thomas fire started in the evening hours on Monday, 4 December 2017, while the others initiated thereafter. As of 6 December 2017, the fires have spread rapidly due the existing Santa Ana winds, destroying infrastructure and buildings along their paths. No percent of fire containment has been declared from firefighters and local emergency management officials.

Here is quick look at a satellite imagery product that highlights the magnitude of these fires. The use of the Near-Constant Contrast (NCC) product monitors atmospheric phenomena, senses emitted and reflected light sources and assists with cloud monitoring during the nighttime. Below is a comparison between two different days provided from two static NCC images.

The first image, is of 3 December 2017 @ 0935z (0135 local time), showing the Los Angeles metropolitan area located in southwestern California. This particular image is taken before the fires initiated on 4 December 2017. One can see the emitted city lights from Los Angeles and all the neighboring suburbs, along with the existing cloud cover, located to the south and east of the metropolitan area. In complement to the image, in the top-right corner, is the moon percent visibility and moon elevation angle, implying that the moon provided adequate moonlight and the moon was above the horizon when the satellite image was taken.

The next image, shown below, shows a few days later, on 6 December 2017 @ 1020Z (0220 local time) the emitted city lights from the metropolitan areas along with the location of the fires and and areas of smoke, which appear to be moving offshore in the south, southwest directions. These fires are ongoing and we’ll have updates on these fires in the near-future. Social media images and videos can be seen via the hyperlinks provided.

Orographic cirrus / lee wave clouds as observed from GOES-16

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

On 22 November 2017, lee wave clouds (also referred to as orographic cirrus) developed downwind of various mountain ranges in Montana and Wyoming.  GOES-16 10.3 micron imagery does an excellent job of capturing these lee wave clouds as regions of exceptionally cold brightness temperatures.  The following 4 panel display of GOES-16 imagery depicts:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/22nov17/4panel&loop_speed_ms=60

Upper left: 10.3 micron (band 13)

Upper right: 3.9 micron (band 7), same color table as band 13

Lower left: Nighttime microphysics RGB (Red: 12.3 – 10.3 microns; Green: 10.3 – 3.9 microns; Blue: 10.3 microns)

Lower right: Fog product (10.3 – 3.9 micron)

This loop spans 1122 to 1757 UTC, therefore the appearance of the imagery transitions between nighttime and daytime, which is very important to note for any imagery/products that contains the 3.9 micron band.  One prominent feature of the 3.9 micron band is that all clouds, both liquid and ice, will exhibit an approximate 20 degrees Celsius increase in temperature when the sun rises over them during the early morning hours. That is, during the daytime, a significant solar reflected component increases 3.9 micron brightness temperatures.  The terminator can be seen best in the fog product centered around 1417 UTC.

There are a mix of orographically induced lee wave clouds (easy to spot since they are locked to the terrain) along with what may be referred to as “synoptic” scale cirrus that is advecting along and is not locked to the terrain, an example of the different types of cirrus is shown here annotated on the 10.3 micron image at 12:27 UTC; the other 3 images are there for reference.

Prior to sunrise,  lee wave clouds in Montana exhibited their initial development.  Lee wave clouds in Montana began to develop around 11:47 UTC.  By 12:27 UTC, brightness temperatures at 10.3 microns were very cold, around -70 degrees Celsius, as the lee wave clouds expanded (see top left image above).  The non-orographic cirrus, annotated in 10.3 microns, exhibit cold brightness temperatures, around -45 degrees Celsius (see top left image above).  Prior to sunrise, the solar contribution to 3.9 micron brightness temperatures is missing; consequently, brightness temperatures at 3.9 microns (top right image above) are similar to those at 10.3 microns.  In sharp contrast, the appearance of the lee wave clouds, in Montana, in both the nighttime microphysics and fog products appear  contrary to clouds composed of ice.  As indicated above, green in the nighttime microphysics RGB comes from the 10.3 minus 3.9 micron temperature difference, which is the fog product.  Therefore, green in the nighttime microphysics RGB (bottom left) and light blue in the fog product (bottom right) is a liquid cloud signature.  However, brightness temperatures are between -45 and -70 degrees Celsius; temperatures that are much colder than the homogeneous freezing temperature.  An apparent dilemma exists: both 10.3 and 3.9 micron brightness temperatures suggest ice clouds while both nighttime microphysics RGB and fog products suggest liquid clouds.  How could this be?

Unfortunately, both the greenish color in the nighttime microphysics RGB and the bluish color in the fog product over Montana are “false signals” (i.e., anomalous from what is expected).  There are 2 possible explanations for the “false signal” in each product.  First, temperatures at 3.9 microns are cold enough to allow noise to appear in imagery.  Secondly, the existence of relatively small ice particles.  Appearances change however when the terminator passes by and the sun shines on the scene.

At 16:47 UTC, the terminator passed by and the sun is shining on the scene shown in all 4 panels in the figure below.

Due to solar reflection off of all types of clouds, brightness temperatures at 3.9 microns have increased approximately 20 degrees Celsius.  Consequently, brightness temperatures at 10.3 microns (upper left) are much colder than brightness temperatures at 3.9 microns (upper right); therefore, the appearance of 10.3 and 3.9 microns are quite different during the daytime compared to nighttime.  In particular, the false signature disappears as a consequence of brightness temperatures increasing at 3.9 microns relative to 10.3 microns (lower 2 panels).  Hence, ice clouds appear black in the fog product (lower right).  There is a difference, however, in the brightness temperatures of the lee wave clouds in Montana and Wyoming compared to all other ice clouds at 3.9 microns.  Research has shown that smaller ice particle sizes reflect more solar energy than larger ice particle sizes.  Thus, the existence of relatively small ice particles can explain the warmer brightness temperatures at 3.9 microns of the lee wave compared to all other ice clouds (including the non-orographic cirrus).  GOES-16 allows us to observe these characteristics of lee wave clouds, which are important forecast considerations in temperature forecasting.  When very cold clouds exist at night, use 10.3 microns rather than 3.9 micron imagery (or product that uses the 3.9 micron band) due to significant noise in the 3.9 micron band.

 

Tropical Storm Rina

Ever since Hurricane Ophelia, it has been rather quiet in the Atlantic Ocean in regards to tropical cyclone activity, but now we have Tropical Storm Rina in the midst. As of early morning, 7 November 2017, Rina is positioned in the central Atlantic Ocean and is projected to move north, then northeast, and is not expected to hit land. Rina’s magnitude will stay as a tropical storm, not becoming a hurricane during the remainder of its life cycle. The latest updates on Rina can be seen via the National Hurricane Center link, shown here.

Below is a Day/Night Band (DNB) image of Tropical Storm Rina located in the middle of the Atlantic Ocean taken at 0421Z, 7 November 2017. For readers that are not familiar, the DNB satellite product utilizes a sun/moon reflectance model that illuminates atmospheric features, senses emitted and reflected light sources and assists with cloud monitoring during the nighttime. In the image, notice the well-defined circulation of Rina, located just west of the convective clouds and bands. The identification of the circulations are crucial in tropical cyclone forecasting, specifically, in finding the strongest/intense part (s) of the cyclones, which can dramatically affect coastal areas with torrential rainfall, flooding, high winds and storm surge. Additionally, in the top-right corner of the image, the moon percent visibility and the moon elevation angle values are provided. The values imply that the the moon is above the horizon and provided adequate moonlight when the image was taken via satellite.

Early Morning Fog in Colorado

Early this morning, 3 November 2017, the Colorado Front Range was inundated with fog and low stratus clouds. Fog persisted for several hours this morning. Below is an animation of the fog along the Front Range of Colorado via the ‘RAP Real-Time’ surface observations website, between 08-15Z. As a quick reminder, foggy areas are identified via pink, parallel, horizontal lines, sandwiched in-between the air temperature (red) and corresponding dew-point (green) values.

In complement to the surface observations, static satellite observations can be used to identify the fog/low stratus clouds via the Day/Night Band (DNB) product and the 10.7um -3.7um brightness temperature difference product. Static satellite observations were taken during the early morning hours, 3 November 2017, @ 0903Z (0303Z local time).

Day/Night Band (DNB) (0.7um)

The DNB provides the illumination of atmospheric features, senses emitted and reflected light sources and assists with cloud monitoring during the nighttime. The DNB image below, taken during the full moon stage of the lunar cycle, shows features over the state of Colorado. Dendritic snow patterns via reflected moonlight are seen in Central Colorado highlighting the location of the Rocky Mountains, with clouds to the east and west. Emitted city lights are also seen under the cloud cover, seen via bright clusters along the Front Range.

M15 (10.7um) – M12 (3.74um) brightness temperature difference 

In complement to the DNB, one can utilize the brightness temperature difference between the VIIRS M15-M12, which can assist in identifying which clouds are liquid water clouds, (in this case, fog/low stratus clouds seen as positive, blue values) in comparison to ice clouds (seen as negative, black values). The range of temperature values are displayed from -10 to +10 degrees Kelvin. The validation of the liquid water clouds along the Front Range, complements the surface observations that observed fog/low stratus clouds at this particular point in time (~9Z).

GOES-16 perspective of Leeside cold front and associated gravity waves – 30 October 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

On 30 October 2017 GOES-16 observed a leeside cold front with associated gravity waves in the vicinity of eastern New Mexico and the Texas panhandle moving southward.  GOES-16 water vapor bands along with 3.9 micron band are shown here:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30oct17/wv&loop_speed_ms=60

The 3.9 micron band highlights the colder temperatures behind the cold front, while the 3 water vapor bands highlight the gravity waves associated with the leeside cold front.  For further reading on leeside cold fronts and associated gravity waves you may read the following articles:

https://doi.org/10.1175/1520-0469(1999)056<2986:DTGWCB>2.0.CO;2

https://doi.org/10.1175/1520-0493(2001)129<2633:OONFPA>2.0.CO;2

How about a comparison between numerical simulation and GOES-16 observations?

The 4-km NSSL WRF-ARW model output from the 00Z 30 October run is shown below:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/30oct17/compare&loop_speed_ms=200

Top left) WRF synthetic 6.2 micron imagery

Top right) GOES-16 6.2 micron imagery (time matched with WRF)

Bottom left) WRF synthetic 6.95 micron imagery

Bottom right) GOES-16 6.95 micron imagery (time matched with WRF)

How well did the model capture the gravity waves associated with the leeside cold front?

Early season lake-effect showers on 25 October 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

Cold advection over the relatively warm waters of the Great Lakes resulted in bands of showers across the Great Lakes as seen in the GOES-16 Daytime cloud phase distinction RGB:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/25oct17/rgb_daycloud&loop_speed_ms=60

This RGB product makes use of the 1.6 micron band which will highlight glaciation, as well as the 10.3 micron band which highlights cloud top temperature.  Note how well it depicts the lake-effect band over Lake Erie since it can discriminate between glaciated clouds versus non-glaciated clouds, as well as the cloud top temperature differences from the 10.3 micron band contribution.  In this case, temperatures are too warm for snow therefore the precipitation was in the form of rain.  Note the appearance of the band from the Buffalo WSR-88D:

Hurricane Ophelia and Wildfires in Spain and Portugal

Ever since last week, Hurricane Ophelia was meandering in the eastern Atlantic Ocean, not knowing where to go. Ophelia gradually became a tropical storm, then hurricane, as it moved northeastward and past through the Azores. As of this past weekend (14-15 October 2017), Ophelia inched closer and closer to the country of Ireland. Before Ophelia made landfall, the strength of Ophelia decreased slightly and had maximum sustained winds of 85 mph. Hurricane Ophelia made landfall along Ireland’s coast in the early morning hours on 16 October 2017. Ophelia brought heavy rain, flooding and storm surge to Ireland, and with how large the areal extent of Ophelia, it advected smoke from the wildfires that were blazing in Spain and Portugal. Even local European airlines, re-routing around Ophelia, noticed the odor of the fires in their airplanes that day.

Static polar-orbiting satellite images taken of the event are seen below via the Day/Night Band (DNB), and the Imagery Band (I-4) (3.74um) at 0248Z, 16 October 2017. More detailed information about each satellite image is described below.

DNB 

The DNB utilizes a sun/moon reflectance model that illuminates atmospheric features, senses emitted and reflected light sources, and assists with cloud monitoring during the nighttime. The DNB image below shows Hurricane Ophelia before it made landfall along the coast of Ireland and its approximate location to a few European countries such as the United Kingdom, France, Spain, and Portugal. The emitted city lights from these countries can be seen as well. Additionally, this image was taken during the ‘new moon’ phase of the lunar cycle, and where the moon is physically below the horizon, not providing adequate moonlight to see atmospheric features via satellite. Due to the lack of moonlight, the satellite imagery can still see the atmospheric features primarily by a phenomena called ‘airglow’. ‘Airglow’ are produced via photochemical reactions during the daytime, that produce a faint ‘glow’ during the nighttime, wherein the satellite can sense the ‘airglow’ and the ambient atmospheric features. Lastly, notice the orange ellipse in Spain and Portugal, this is the general location of where the wildfires were occurring. We’ll refer to this area in the next image.

Imagery Band (I-4), (3.74um), Inverse Gray Scale

The I-4 band is at a spatial resolution of 375-meters, and is used in complement of the DNB to identify wildfire ‘hotspots’, areas that are significantly hotter than their surroundings. If you look within the orange ellipse, you will see ‘hotspots’, one hotspot in Western Spain, and a few in Northern Portugal, highlighted by the darker, black colors. Within the ellipse, the brighter white colors are combination of clouds and smoke, although one cannot clearly differentiate between the two. It is inferred that the smoke and clouds are advecting to the north/northeast as Hurricane Ophelia approaches Ireland.

Imagery Band (I-4), (3.74um), New Color Scale

Utilizing the same I-4 band image from above, and implementing a new color scale to it, to identify the locations of the wildfire ‘hotpsots’ (hotter brightness temperatures sensed by satellite) in Spain and Portugal, are shown in red.

California Wildfires

It is finally October, where the fall season has hit its stride, but unfortunately wildfires are still a-brewing in the Golden State. California, in recent days, has been subjected to more wildfires along the Northern side of the state. The majority of the fires are north of San Francisco and San Pablo Bays. The cause of these fires are unknown and under investigation as of 10 October 2017.

Supplemental satellite images are provided highlighting the relative location of the fires via the Near-Constant Contrast (NCC) that illuminates atmospheric features, senses emitted and reflected lights and assists with cloud monitoring during the nighttime and the GOES-16 infrared band (Band 7, 3.9um) that observes ‘hotspots’ over Earth’s surface.

If you look at both static images (below) at 0947Z, 10 October 2017, there are collocated ellipses (orange and white) that encircle the general areas of the fires. The confusion becomes, how can one differentiate between the emitted city lights and the emitted light from the wildfires that are shown in the NCC? That is where geostationary data comes into play, specifically the infrared band 7 (a.k.a. 3.9um channel).

The 3.9um channel is notorious for identifying ‘hotspots’ or surface areas that are significantly warmer than their surroundings and are represented by the black pixels. The differentiation between the emitted city lights and emitted lights from the wildfires becomes easier when the NCC and GOES-16 infrared image are used in complement with one another to identify and infer the areal extent of wildfires. In addition, if you look closely on the southernmost ellipse in the infrared image, you can see that one black pixel senses over 60+ degrees Celsius at the surface, indicating hot fires sensed within that pixel!

NCC @ 0947Z, 10 October 2017

GOES-16, IR, Band 7 – 3.9um @ 0947Z, 10 October 2017

Here’s the latest article covering the fires in Northern California via CNBC.

Advected Layer Precipitable Water product for Hurricane Harvey

The CIRA advected layer precipitable water (ALPW) product for Hurricane Harvey is quite interesting:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/28aug17&loop_speed_ms=600

The loop spans from 12Z 23 August to 12Z 29 August.  During that time period, we see the development of Hurricane Harvey in the western Gulf of Mexico.  There are clear indications of moisture convergence in the vertical during the early stages which would add confidence to a forecast of a strengthening tropical cyclone.  In the later periods of the loop, the system slows down along the coast of Texas.  It is clear that abundant moisture exists over a deep layer in the vertical in the vicinity of the slow-moving circulation.  High precipitation efficiency can be inferred from this imagery.  Abundant moisture over a deep layer in the vertical, very slow movement and high precipitation efficiency came together and contributed to the significant flooding in the vicinity of the circulation over coastal Texas.

Hurricane Harvey

Just a few days after Hurricane Harvey made landfall in southeast Texas, Harvey has downgraded to a Tropical Storm, however it is still bringing torrential rainfall and massive flooding to southeast Texas and Louisiana. To recap, Hurricane Harvey made late-night landfall on 25 August 2017 as a Category 4 Hurricane.

Some of the latest precipitation totals are as high as 40+ inches as of late Monday evening, 28 August 2017. Flooding is widespread and power outages have affected many cities along the Gulf Coast, especially in Houston, Texas.  Below is an example of the number of river gauges observing major flooding along the Gulf Coast via the National Weather Service – Advanced Hydrologic Prediction Service website. The red and purple dots represent river gauges that are experiencing moderate to major flooding in and around the Houston, TX area.

If we look a little bit closer, at one of the purple gauges that represent major flooding, below is an image of the West Fork San Jacinto River Gauge near Conroe, TX, where the gauge is experiencing record flooding so far, peaking at a flood stage of 127.3 feet!

More precipitation and flooding are expected throughout the week, where recovery efforts will last for the next few months. Below is an image of the Imagery Band 5 (11.45um), an infrared band on the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on-board the Suomi-National Polar Orbiting Partnership satellite, showing the relative location of the storm along the Gulf Coast at 0816Z, 28 August 2017. The brightness temperature values are expressed from a range of colors; 180K (brighter colors representing temperatures of cold clouds/convective cloud tops) to 320K (darker colors, land and ocean temperatures). The spatial resolution of the imaging band is at 375 meters.

Interesting features along the west coast on 15 August 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

The following loop is the GOES-16 visible (0.64 micron) band centered along the California / Oregon border on the morning of 15 August 2017:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/15aug17/ncal&loop_speed_ms=60

Low clouds exist along the coastline.  Offshore, vortex generation is occuring as the vortices move southward.  Inland, point sources of smoke with plumes drifting west are observed.  Mount Shasta can be seen in the southeast corner of the image with snow at higher elevations.  The shadow associated with the rising sun is readily seen on the western slope of the mountain.

Further south along the coast:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/15aug17/scal&loop_speed_ms=60

We observe a general decrease in the coverage of low clouds as the sun is rising.  Numerous vortices are observed associated with island effects.  The relative motion of low clouds can provide information on regions that experience gap flow.  Identify regions over the land where gap flow appears to exist.  Consider how useful this imagery could be towards visibility and ceiling monitoring and short-term forecasting.

Fires in Pacific Northwest

The Pacific Northwest (PNW), that is, the states of Washington, Oregon, Idaho and Western Montana are experiencing a significant heat wave right now. Air temperatures are scorching hot, with temperatures in the high 90’s and low 100’s. An amplified upper-level ridge has been quite persistent over the PNW, bringing the high temperatures and dry air to the area.

Due to the abnormally dry conditions, fires have been initiated and are scattered within the PNW. Fires can be seen below via two polar products: the Day/Night Band (DNB, 0.7um) and the Imagery Band 4 (I-4, 3.74um). The two products work in complement with one another. The DNB assists in utilizing a sun/moon reflectance model that illuminates atmospheric features, senses emitted lights, and assists with cloud monitoring during the nighttime, while the I-4 band senses the ‘fire hotspots’, areas that exude high brightness temperatures in comparison to the nearby environment. The I-4 band also assists the DNB in differentiating between what emitted lights are city lights, and what lights are the emitted lights from the fires.

It is important to note that the DNB and I-4 band are at two different spatial resolutions; DNB at 750 meters an the I-4 band at 375 meters. The static images can be seen below at 0924Z (0224 local time), 3 August 2017.

DNB

DNB_modified

I-4 Band

I4_modified

Quasi-Null case for lots of TPW over eastern Colorado on 29 July 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

We’ve shown blog entries that highlight the value of the CIRA advected layer precipitable water (ALPW) product for heavy rain cases.  Here we show a case of very high LPW during the monsoon circulation over western North America that affected eastern Colorado but this time failed to produce excessive rain.

The 1200 UTC Denver sounding on 29 July 2017 was extremely moist:

DNR_12z

This was a near record TPW value for this day of 1.21″ as depicted on sounding climatology for Denver available from SPC:

pw_climo

The ALPW loop covering from near 00 UTC 28 July through 1800 UTC 30 July shows the peak of the monsoon surge of moisture over northeastern Colorado is on 29 July:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29jul17/lpw&loop_speed_ms=600

While the lower levels are too low for the elevated terrain of most of Colorado, the upper two layers show that the moisture was very deep.  All of this pointed to a threat for potential flash flooding or at least heavy rain for the lower elevations of northeast Colorado on 29 July.

So what actually happened?

Here is a radar loop that spans the mid-day through evening hours:

myanimation

Substantial rain did occur over portions of the mountains west of the Urban Corridor of northeastern Colorado, but these moved very slowly and only light showers managed to move eastward on to the lower elevations.  We can see that heavier showers did develop farther to the east but most of the populated areas along the Urban Corridor failed to receive much rain at all as seen in the CoCoRaHS rainfall reports:

cocorahs

Why didn’t these areas receive heavier rainfall in this abundantly moist atmosphere?  One major factor is that there was so much moisture that the day began under considerable cloud cover.  As the GOES-16 visible (0.64 micron) loop shows, the clouds never cleared over a large portion of northeastern Colorado:

 http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29jul17/vis&loop_speed_ms=60

Notice that where there was more sunshine further east and south, convection did develop with some flooding issues.  Also, the convection in southeast Wyoming and far northeast Colorado produced what appear to be gravity waves on top of the lower-level cloud cover.

Sometimes, we can still get heavy rain along the Urban Corridor despite cloud cover if upslope is strong and deep, and/or there is a upper level shortwave.  In the absence of these 2 possibilities, another scenario would be storms developing in the adjacent foothills moving eastward, but in this case it appears that the steering flow is very weak.  Looking at the 00Z Denver sounding from 30 July:

DNR_00z

There was drying in the lower levels, and we see there is one westerly wind barb.  It’s possible that the weakening mountain storms sent a weak but fairly large scale outflow (perhaps just above the surface) onto the adjacent plains actually drying the lower levels, since this would be downslope flow.  This stabilization along with the cool temperatures from the persistent cloud cover may have combined to prevent any heavy rains across the Urban Corridor on what appeared to be a day with high heavy rain potential.

Layer PW product for flooding on 27 July 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

During the overnight hours of 26-27 July 2017, a large area in the vicinity of Kansas City received greater than 5″ of rainfall, with maximum amounts around 8.5″ which led to flooding:

precip

GOES-16 10.35 micron imagery during the overnight hours showed a series of convective clusters moving over the same region (northeast Kansas into west-central Missouri):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/27jul17/B13&loop_speed_ms=60

The CIRA layer precipitable water (LPW) product depicted the moisture plumes at different levels that contributed to this flash flood event:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/27jul17/lpw&loop_speed_ms=600

Upper left panel Sfc – 850 mb LPW

Upper right panel 850 – 700 mb LPW

Lower left panel 700 – 500 mb LPW

Lower right panel 500 – 300 mb LPW

At the lowest level (sfc – 850 mb), we observe abundant moisture with origins from the Gulf of Mexico advecting in from the south, along with enhanced moisture convergence along a frontal boundary.

At mid to upper levels, we observe abundant moisture with origins from the tropical eastern Pacific resulting from the North American monsoon circulation.

Abundant moisture throughout a deep layer in the vertical is one of the key ingredients for excessive rainfall that can lead to flooding.  The LPW product provides context for the origins of moisture plumes and allows one to see them “coming together” from an observational perspective.

Gap flow in the Strait of Juan de Fuca

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

On the morning of 18 July 2017, low-level clouds moved eastward through the Strait of Juan de Fuca.  This can be seen in the GOES-16 Nighttime Microphysics RGB product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/18jul17/nmp&loop_speed_ms=60

Recall in this RGB product, low clouds are depicted in the aqua color (before sunrise), which is moving eastward.  After sunrise, there is a reflected component to the 3.9 micron band, therefore its recommended to only make use of this product at night.  After sunrise we may make use of the visible imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/18jul17/vis&loop_speed_ms=60

Note the speed of the eastward moving low-level clouds appears to be faster than other low-level clouds in the region.  This is due to gap flow through the Strait of Juan de Fuca, a well known topic of research.

One method to make for a smooth transition in your loop between nighttime and daytime is to make use of the image combination in AWIPS and make use of visible imagery for the daylight hours.  Here is a different example of gap flow through the Strait of Juan de Fuca that makes use of the fog product at night:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/26jul17&loop_speed_ms=60

Like the nighttime microphysics product, this product also makes use of the 3.9 micron band.  Once sunlight is introduced with any product that involves the 3.9 micron band, it may change the interpretation of the product which is why it is recommended for nighttime use only.  In this loop, the visible imagery becomes clear after sunrise but we still see a bit of the fog product which you may adjust with the alpha setting in your color table editor on AWIPS.

Tropical Activity in the East Pacific

It is that time of year again! The time of year where tropical storms, hurricanes initiate and permeate through the Atlantic and the East Pacific Oceans. Currently, a few to note are tropical depression Greg, Tropical Storm Irwin and Hurricane Hilary. A screenshot of the National Hurricane Center’s  (NHC) ‘Active Storm’ Map showing the relative locations of the storms is seen below as of 26 July 2017.

nhc

You can check out the CIRA GeoColor product loop of the tropical activity via the new RAMMB/CIRA Slider, a user interface where users can see and analyze satellite products over the entire globe. The loop is from 26 July 2017 between 14-17Z.

According to NHC, it is important to note that Hilary is a Category 2 Hurricane with maximum sustained winds of 105 mph and is forecast to move west/northwest at 13 mph as of 9AM MDT, 26 July 2017.

Here is the latest visible image of Hurricane Hilary seen below via MODIS and AVHRR at 1 kilometer resolution via the CIRA ‘TC Real Time’ website. MODIS and AVHRR are instruments on-board a few polar-orbiting satellites such as the NOAA-18 satellite.

2017EP09_1KMVSIMG_201707261437

Furthermore, what is fascinating is some of the model output such as the GFS and ECMWF models have Tropical Storm Irwin and Hurricane Hilary experiencing a Fujiwhara Effect in the next few days: where both storms rotate relatively close to one another (counter-clockwise, where both storms are approximately less than 900 miles apart from each other). According to the models in this particular case, Tropical Storm Irwin will become engulfed into Hurricane Hilary in the later days. We will see what happens! Check out the GFS model run animation of this phenomena, initialized at 12Z, 26 July 2017 via the Tropical Tidbits website below.

Webp.net-gifmaker (1)

California (Alamo Fire)

The state of California had sufficient moisture over the winter where the Sierra Nevada Mountains tabulated record amounts of snowfall. However, as we have transitioned into the summer months of 2017, fires have been initiated in the southern and southwestern parts of California.

One fire to note is the Alamo Fire which is located just east of Santa Maria, California. For users that are not familiar with the area, Santa Maria is located approximately 170 miles northwest of Los Angeles.

As of 11 July 2017, the cause of the fire is still unknown and the fire has burned more than 28,000 acres. On a positive note, 45% of the fire has been contained so far.

Below is an animation of the Alamo Fire from 6-10 July 2017 via the Day/Night Band (DNB), that utilizes a sun/moon reflectance model that illuminates atmospheric features, senses emitted lights and assists with cloud monitoring during the nighttime. This is also a perfect time to check out DNB imagery since the moon just passed the full moon stage of the lunar cycle.  In the animation, the moon percent visibility and moon elevation angle are also provided. Click the image below and the animation will begin.

alamo_fire_animation

Also, here is an additional link using the CIRA RAMMB Slider which shows the Alamo Fire on 8 July 2017 via the GeoColor Product.

For further updates on the Alamo Fire keep tabs with the ‘Cal Fire’ state webpage.

Onset of southwest monsoon as depicted by the CIRA advected LPW product

Between the first and second week of July, moisture associated with the southwest monsoon surged into the Mojave dessert.  Prior to the arrival of this airmass, very hot temperatures and low dewpoint temperatures existed throughout the southwest.  After the arrival of this airmass, temperatures were not quite as high, and dewpoint temperatures were considerably higher.  The CIRA advected layer precipitable water product at 01Z on 5 July 2017 was representative of before the arrival of this moist airmass across the southwest:

4panel-20170705_011004

For example, in the 850-700 mb layer (upper right panel) the blue colors represent a much drier airmass in southern California and Arizona, with a moist airmass just south of the Mexico border (in the green shades).

Soon afterwards, we see in this animation the advection of moisture from Mexico into the southwest US:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10jul17/lpw&loop_speed_ms=120

this more moist airmass at mid/upper levels goes around the strong 500 mb ridge to bring mid/upper level moisture into much of the intermountain west.  The greater moisture introduces better chances for rain, particularly for regions that had been very hot and dry.

Advantage of 1-minute imagery for the evolution of rapidly evolving low clouds

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

We begin by looking at the GOES-16 visible (0.64 micron) loop on the morning of 11 July 2017 at 5 minute temporal resolution:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11jul17/vis_conus&loop_speed_ms=60

Notice the low-level clouds in southern Georgia extending northeastward towards coastal South Carolina that appear to flicker since they are rapidly evolving.

The GOES-16 IR (10.35 micron) loop shows a similar evolution:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11jul17/B13&loop_speed_ms=60

A portion of this scene was captured by the mesoscale sector so that we may view the imagery at 1 minute temporal resolution:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11jul17/vis_meso&loop_speed_ms=40

Rather than the flickering effect we saw at 5 minute temporal resolution, we instead see rather rapid evolution of clouds developing / dissipating much more smoothly.

The environment is characterized by very light winds throughout the troposphere, along with high relative humidity through much of the low to mid levels:

FFC

 

How deep do these clouds extend in the vertical?  We can help answer this question by looking at the 3 GOES-16 water vapor bands (at 5 minute temporal resolution), and we will also include the Air Mass RGB product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11jul17/4panel&loop_speed_ms=60

The rapidly evolving low-level clouds are evident in the low-level water vapor band (7.34 micron).  At 6.9 microns (mid-level water vapor band) we still see some of those features, but not as clearly.  Note that we do see numerous gravity waves, which may be playing a role in the rapidly evolving low-level clouds.  As expected, the low-level clouds do not appear in the 6.19 micron (upper-level water vapor band), however gravity waves are still observed.

Undular bore in the Split Window Difference Product

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

By Louie Grasso and Dan Lindsey

On the morning of 30 June 2017, an undular bore is observed in north Texas.  The clouds associated with the undular bore can be seen on the GOES-16 visible (0.64 micron) band.  The GOES-16 Split Window Difference (10.3 – 12.3 micron) product adds additional information.  The undular bore is observed to exist further west of where clouds associated with the undular bore (in clear skies) are located.  We observe this feature in the split window difference due to local maxima in water vapor depth along the “crests” of the undular bore.

undular_bore_30jun17_vis_swd

Island / coastal effects in the northeast on 19 June 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

A loop of GOES-16 visible (0.64 micron) imagery on 19 June 2017 over the northeast:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/19jun17&loop_speed_ms=80

illustrates the effects of various islands and coastalines.  The surface winds are southerly with warm/moist air advecting northwards.  Low clouds (and perhaps fog) develop over the ocean as the warm/moist air advects over the relatively cooler ocean surface.  These low clouds converge along the coastlines as well as various islands.  The islands also act as a barrier to the flow with various convergence / divergence patterns around the islands.

Colorado: Dead Dog Wildfire

Another fire is catching headlines in Colorado the past few days, it is called the Dead Dog Wildfire located within Dead Dog Gulch. The fire is located approximately 10 miles north-west of Rangely, Colorado. Hot, windy and dry conditions have persisted in northwest Colorado over the past few days, where the fire initiated over the past weekend. According to news outlets, the fire has burned approximately 18,000+ acres so far and will continue to burn more acreage.

To visualize and emphasize the magnitude of the wildfire, one can use the Day/Night Band (DNB) which utilizes a sun/moon reflectance model to illuminate atmospheric features, sense emitted lights and assist in cloud monitoring during the nighttime. A DNB satellite image is shown below, where the fire is located within the highlighted red circle. To complement the DNB, one can also use the I-4 band (3.74um), also provided below, which senses fire ‘hotspots’: areas where the satellite sensor recognizes ‘hot’ regions, expressed in brightness temperatures (i.e. orange, yellow and red colors) on the Earth’s surface.

DNB image taken at 0849Z, 13 June 2017

DNB_one

I-4 Band (3.74um)  image taken at 0849Z, 13 June 2017

IR_one

The latest updates of the fire can be seen via the following link.

Mesoscale Convective Vortex (MCV) over Texas on 9 June 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

GOES-16 IR band 13 at 10.35 microns depicts an MCS across Texas:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8jun17/B13&loop_speed_ms=100

The MCS decays during the loop.  Can you identify a Mesoscale Convective Vortex (MCV) in this loop?

Next, look at the GOES-16 visible (0.64 micron) loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8jun17/B02&loop_speed_ms=100 

Now can you identify a Mesoscale Convective Vortex (MCV)?  On the northwest flank of the decaying convection, a circulation can be readily seen in the visible imagery associated with a MCV.  Note that it can be detected in the IR loop as well, although it was more subtle.  In fact, due west of the decaying convection we can see another circulation, likely an MCV associated with convection that took place prior to the start of the IR loop.

For MCV identification, using both IR and visible imagery in tandem is ideal in that the IR imagery can trace the origins of the MCV from the MCS while the visible imagery generally provides a clear indication of the circulation associated with the MCV after the decay of the MCS.

Tropical Storm Beatriz

Yesterday evening, Thursday, 1 June 2017, Tropical Storm Beatriz made landfall in southern Mexico state of Oaxaca. The tropical storm brought heavy rains, flooding and mudslides to the area, resulting in 2 deaths as of Friday, 2 June 2017. Once deemed a tropical storm, turned back into a tropical depression soon after making landfall and is currently a remnant low. Flights were cancelled across the region where the city of Puerto Angel received 9+ inches of rain so far. The storm is moving slowly over southern Mexico, and high amounts of precipitation is expected across the region over the next few days.

Looking at the latest overpass from the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite, Beatriz is highlighted by one of the 22 spectral channels (i.e. Imagery Band 5 (I-5) (11.45um)) apart of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on-board Suomi-NPP. The I-5 band is an infrared band that detects the emissivities of the atmospheric phenomena and land/ocean surfaces (i.e. the amount of thermal energy produced by atmospheric phenomena and surface features, detected by satellite).

The image below describes the thermal energy detected by satellite via brightness temperatures in degrees Kelvin over the southern Mexico and Central America domain. The range of brightness temperatures are from 180K (-132 degrees F) to 320K (116 degrees F). The low to high clouds are seen via cooler colors (i.e. blue, light blue), while the land and ocean are seen via warmer colors (i.e. orange). One can see the location of tropical storm Beatriz as it made landfall in southern Mexico at 0711Z, 2 June 2017. There are also small storms located east of Beatriz along the Yucatan Peninsula.

modified_I5_06022017

*Sources: National Hurricane Center and The Weather Channel.

1-minute GOES-16 applications for the 8 May 2017 Colorado hail event

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

By Ed Szoke and Dan Bikos

On 8 May 2017 severe thunderstorms hit northeast Colorado with a devastating hail storm across the Denver metro vicinity, causing more than $1.4 billion in damage.  This blog entry will look at GOES-16 imagery to show the synoptic setup, followed by the pre-storm environment, and then applications of the 1-minute imagery during the nowcast time period.

The 3 water vapor bands and 0.64 micron visible imagery from GOES-16:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8may17/colorado/4panel_early&loop_speed_ms=140

depict a closed low over far northern Baja.  The loop shows southerly flow in all 3 WV channels  bringing moisture northward across Colorado.  We can see this relatively deep moisture in the 12Z Denver sounding:

DEN_12z_sounding

Note the southerly flow extends to the surface.  Mid-level (700-500 mb) lapse rates are quite steep (8.1 degrees C / km).  Despite the fairly deep moisture, the dewpoint just above the surface was still fairly low (upper 30s F).  But this changed dramatically during the morning as a cold front surged southward across northeastern Colorado as shown in this visible loop with METARs:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8may17/colorado/conus_vis_metar&loop_speed_ms=140

The visible imagery shows low-level cumulus developing along portions of the Front Range behind the cold front, meanwhile at the end of the loop a thunderstorm develops at the leading edge of the cold front.  By 1800 UTC, the dewpoint at Denver had risen to 46 F with slightly higher values to the north.  Although 73 / 46 do not seem like very high values to support the severe hailstorm that would later occur, given the steep lapse rates, this temperature / dewpoint is actually quite unstable at this elevation.

What was the origin of the Denver hailstorm?  At the end of the last loop, we noticed a storm forming along the cold front southeast of Denver.  In the next loop, we look at several hours of 5 minute visible imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8may17/colorado/conus_vis&loop_speed_ms=140

Notice the lines of south-north oriented convection southwest of the Denver area.   The origin of these lines appears to be collocated with regions of higher terrain.  The moist southerly flow impinging on these terrain features forms these lines of convection.  In the loop we can see that the Denver storm appears to develop at the north end of the mountain range indicated by the yellow arrowstatic_labeled

The animation also shows the first storm mentioned earlier that forms east of Denver around 1740 UTC and for a while looks fairly strong (although no severe reports were observed with this storm), but it dissipates quickly shortly after 1900 UTC.  We speculate that the air mass behind the cold front was still too cool and stable to sustain the storm.

Next, let’s focus on 1-minute visible imagery starting at 1900 UTC going through 2200 UTC:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8may17/colorado/meso_vis&loop_speed_ms=60

Early in the loop, extensive cumulus growth takes place over the stalled cold front, leading to convective initiation.  These storms remained southeast of Denver but do become severe (hail observed up to 2″ in diameter and funnel cloud).  We’ll discuss these storms in more detail later, however now we will focus on the Denver hailstorm.

Recall that the Denver storm develops on a cloud line with origins from the northern edge of a mountain range.  We can see this nicely in the 1-minute animation.  Following this particular storm, we notice that it rapidly develops an overshooting top around 2016 UTC as denoted in this image below:

annotated_denverstorm

This overshooting top expands rapidly and soon thereafter (2040 UTC) we get the first severe hail report west of Denver.  Shortly after this first hail report we see continued expansion of the overshooting top and what appears like anti-cyclonic rotation at cloud top.  This may however just be a strong divergence signature and not necessarily a circulation. Also during this time period, the storm motion changes from heading north-northeast to more east-northeast (turn to the right of the mean flow) coincident with numerous hail reports above 2″ in diameter and up to 2.75″ (baseball) in diameter.  Since this is a densely populated area that included numerous automobile dealerships, widespread damage occurred (latest estimates of at least $1.4 billion).  After passing through the Denver area, the storm motion reverts back to north-northeast once again, suggesting a downward trend in intensity (indeed, the frequency and size of the hail reports went down).

Early in the loop, at the southern end of our scene a thunderstorm produces anvil cirrus that moves to the north-northeast.  As that anvil cirrus approaches the cluster of thunderstorms southeast of Denver that we discussed earlier, they clearly act as an obstacle in the flow to the anvil cirrus coming up from the south (particularly evident as a ring of clear sky between the thunderstorm and approaching cirrus from 2015 – 2100 UTC).

Focusing our attention back on the cluster of thunderstorms southeast of Denver.  We observe a series of quasi-stationary waves oriented east-west just north of the overshooting tops, these are particularly evident in the 2030-2130 UTC range.  Are these waves induced by the thunderstorms or are they pre-existing?

To help address this question, we consider the 3 water vapor channels along with the 10.35 micron IR animation:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8may17/colorado/4panel_gw&loop_speed_ms=100

In the water vapor bands, notice the east-west oriented quasi-stationary waves do exist prior to convective initiation.  These waves are most evident in the 6.95 micron (mid-level) water vapor band, somewhat evident in the 7.34 micron (lower-level) water vapor band and quite subtle in the 6.2 micron (upper-level) water vapor band.  It appears that once the anvil cirrus develops, the waves become visible within the anvil shield.  Perhaps these are gravity waves that are forming as the mid/upper level southerly flow is lifted over the stalled cold front.

One final feature to point out is in the 10.35 micron IR imagery in the lower right panel.  The overshooting top with the Denver hailstorm is quite vivid in the IR imagery, in particular at 2016 UTC in comparison with the visible imagery discussed earlier.  We can also easily follow the deviant storm motion and what appears to be the anti-cyclonic motion at cloud top around the time of the most significant hail reports.  The most pronounced enhanced-V signature is found in the storm further north of Denver where severe hail also caused damage.

For more information about the Denver hailstorm of 8 May 2017, see this link:

http://www.thedenverchannel.com/news/local-news/14b-may-hailstorm-on-track-to-be-colorados-most-expensive-ever

 

 

Southern Georgia: West Mims Fire

As the spring season accelerates into summer, it is that time of year again for fires to occur all around the United States. A large fire that is a-brewing is the West Mims Fire located in southern Georgia, embedded in the Okefenokee National Wildlife Refuge. As of 11 May 2017, the fire, initially started by lightning, has burnt over 140,000 acres and is still raging on. To add insult to injury, southern Georgia has not received much precipitation over the past few months where the Okefenokee National Wildlife Refuge is currently in a D3, Extreme Drought as of 9 may 2017. A category D3 Extreme Drought is classified as an area that is susceptible to the listed impacts: major crop losses and potential widespread water shortages or restrictions, according to the US Drought Monitor. The latest updates of the West Mims Fire can be seen via the InciWeb link.

Here are the latest Day/Night Band (DNB) (0.7 um) and Imagery Band (I-4) (3.74um) animations from the Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the Suomi-National Polar-orbiting Partnership satellite. For reference, the DNB utilizes a sun/moon reflectance model that illuminates atmospheric features, senses emitted lights, and assists in cloud monitoring the nighttime, while the I-4 band shows the locations of the hottest wildfires, known as ‘hotspots‘. The DNB is at 750 meter resolution while the I-4 band is at 375 meter spatial resolution. The animations are from 5-12 May 2017.

DNB Animation 

Animation highlights the evolution of the wildfires in southern Georgia denoted by the large white circle. Some of the features that are seen are the emitted city lights and the emitted lights from the fires, corresponding smoke, clouds and one can infer the location of the burn scar extent. Additionally, in the top-right hand corner shows the moon percent visibility and the moon elevation angle. A high moon percent visibility and a positive moon elevation angle imply the moon is above the horizon and adequate moonlight is present to see the atmospheric features via satellite.

DNB_animation

Imagery Band (I-4 ) Animation

Over the same domain, the IR animation shows the brightness temperatures of the fires from a range of 180-400 degrees Kelvin (K), where yellow and red colors imply the hottest parts of the fires. In contrast, the white, grey and black colors imply cold low-to-high clouds in the area. The evolution of the fire can be seen at a high spatial resolution at 375 meters.

IR_animation

Tropical Cyclone Donna

Have you ever been to the Solomon Islands or the Republic of Vanuatu? They are both remote islands located in the southwestern Pacific Ocean, relatively close in proximity to Australia and Papau New Guinea. There is a tropical cyclone that is a-brewing in this area of the world….her name is tropical cyclone ‘Donna’. As of Friday morning, 5 May 2017, Donna is a Category 2 hurricane and is expected to reach a Category 3 on the Saffir-Simpson Scale.

Donna is unique in that it is considered an ‘out of season’ tropical cyclone where tropical cyclones are normally produced between the months of November and May in the Southern Hemisphere. Flooding, high winds and heavy rains are expected for islands that are in the path of Donna; a tropical cyclone warning has been issued for the effected areas.

To track and monitor Donna, a forecaster or user can utilize the Day/Night Band (DNB) which is a sensor, and is one of 22 channels on the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument which is on-board the Suomi-National Polar-orbiting Partnership (S-NPP) satellite. The DNB uses a sun/moon reflectance model that illuminates atmospheric features, senses emitted natural and anthropogenic lights, and assists with cloud monitoring during the night-time hours; consider it as a night-time visible channel. A DNB image of tropical cyclone Donna, provided from the CIRA TC Real-Time web-page at 1432Z, 5 May 2017, is seen below.

2017SH18_SRSNPPDV_201705051432

Users can see the high-resolution (750-meters) and the detailed cloud structures that the DNB provides during the night-time as Donna moves through the area. Users can also assess the magnitude of Donna, inferring how many islands are or will be impacted by the storm.

For more updates and current status on Tropical Cyclone Donna click the following link.

16 April 2017: 1.37 micron band (“Cirrus band”) features other than cirrus clouds

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

By Dan Bikos, Louie Grasso, and Ed Szoke

For this blog entry, we are going to focus in on the state of Durango in Mexico during the mid-day hours of 16 April 2017.  Conditions during that time were warm and very dry:

TPW_metars

The sky cover was mostly clear throughout the period of interest (mid-day hours):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/mexico/B02&loop_speed_ms=80

A topographic map of the region reveals that the elevation (given below in thousands of feet) is quite high:

terrain

 

If we analyze the GOES-16 1.38 micron (“Cirrus band”):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/mexico/B04_default&loop_speed_ms=80

There are features that are moving that are approximately oriented southwest-northeast (ignoring the cirrus clouds later in the loop in the northern regions and also the low-level cumulus to the south).  These features are not clouds since we did not see them in the visible channel shown above.

Let’s look at the GOES-16 7.34 micron (“low-level water vapor”) band:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/mexico/B10&loop_speed_ms=80

Features similar to those that were observed in the 1.38 micron band appear at 7.34 microns.  The 1.38 micron band can be displayed with a different color table to increase the contrast, thus bringing more clarity to the features that we observe:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/mexico/B04_enh&loop_speed_ms=80

Recall at this wavelength, 1.38 microns, water vapor is the primary absorber.  If there is sufficient moisture to absorb incoming radiation, cirrus clouds show up rather clearly due to the large contrast between bright cirrus clouds and a dark background, hence the band being named the “Cirrus band”.  In the case discussed here, moisture is limited, particularly at higher elevations where we see the southwest-northeast oriented banded  features.  In fact, here is a comparison of the corresponding features at 1.38 and 7.34 micron band.

annotated1

We note that each feature labeled above has the following characteristics:

1) 1.38 micron band darker corresponds to 7.34 micron band cooler brightness temperature and

2) 1.38 micron band lighter corresponds to 7.34 micron band warmer brightness temperature.

Why?

In this relatively dry, higher elevation environment, the surface is not completely obscured by the intervening (and highly absorbing) atmospheric water vapor when viewed at 1.38 microns.  In this near-infrared band, regions that are darker correspond to more column-integrated water vapor (and a lower surface reflectance contribution), while regions that are brighter correspond to less column-integrated water vapor (and a  higher surface reflectance contribution).

The alignment of these features most likely associated with water vapor are oriented with the terrain:

annotated2

Note that the lower valleys at locations 5 and 6 can be seen as darker regions at 1.38 microns (recall, more water vapor is associated with darker regions at 1.38 microns).

Can we rule out that these features are associated with dust or smoke?  This will now be investigated.

The split window difference product (11.2 micron minus 12.3 micron band difference):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/mexico/SWD&loop_speed_ms=80

would have negative values (brown in this color table) if lofted dust was present, since the values are positive, we can rule out lofted dust.

For assessing smoke, we look at the GOES-16 0.47 micron (“Blue”) visible band:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/mexico/B01_midday&loop_speed_ms=80

There are no obvious smoke plumes during this time period.  However, if we look later in the afternoon when fires tend to be more pronounced and show up more clearly due to  favorable scattering associated with the view angle:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/mexico/B01_later&loop_speed_ms=80

We do observe a few smoke plumes.  However, the orientation of the smoke plumes does not match with what we observed in the 1.38 or 7.34 micron bands and does not cover such a large area in bands that are oriented with the terrain.

In conclusion, the GOES-16 1.38 micron (“Cirrus”) band can observe features other than cirrus clouds and plumes of water vapor may be observed under specific circumstances.

1-minute applications for severe thunderstorms from 15-16 April 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

During the afternoon hours of 15 April, one of the GOES-16 mesoscale sectors captured severe thunderstorms in the Iowa / Nebraska region:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/15apr17/vis&loop_speed_ms=80

Often times we like to point out the use of 1-minute imagery for the evolution of severe thunderstorms.  However, the imagery also has utility to identify regions where the potential for thunderstorm development is suppressed which can be an important operational application.  In this particular case, notice the highlighted yellow regions at 2209 UTC:

annotated_20170415

Focus on those regions in the animation above.  Note that thunderstorm development is  suppressed in these regions.  The 1-minute imagery actually helps you identify these regions with more clarity than current GOES due to the increased temporal and spatial resolution.  We might not always be able to understand why suppression is occurring.  In this case, just considering the surface observations:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/15apr17/metars&loop_speed_ms=80   

for the southern region of interest, although there is cloud cover, the temperature and moisture does not appear unfavorable for thunderstorm development, however the imagery shows that any attempts at convective initiation are being suppressed by something in the environment.  Meanwhile, in the northern region of interest there is nothing apparent in the METARs that would suggest suppression, however GOES-16 imagery clearly indicates that something in the environment is unfavorable for convective development.  In operations, GOES-16 imagery could be integrated with model analyses, surface observations and other observational data to try to understand why the imagery shows what it does.

On the following day, another round of severe thunderstorms took place this time in Oklahoma and the Texas panhandle.  We’ll start with a loop of the IR band at 10.35 microns at 5 minute temporal resolution:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/ir&loop_speed_ms=80

We observe a number of thunderstorms across the scene;  the storm in southern Oklahoma has an enhanced-V signature as well as multiple gravity waves and is backbuilding southwestward for a while.  We see other thunderstorms in the scene as well, however lets focus in on the eastern Texas panhandle activity.  We see a westward moving boundary with low-clouds to its east and clear to the west.  The northern part of this boundary leads to a triple point with an outflow boundary oriented east-west.  It’s interesting to note that convective initiation occurs first south of the triple point and then later new convection develops at the triple point.  The initial storm that developed along the westward moving boundary south of the triple point develops an enhanced-V signature and in fact severe hail was observed with this storm.  Also note that this storm is moving southwestward in time as it backbuilds along the boundary against the mean west-southwest flow aloft shown in the 0000 UTC Amarillo, TX sounding:

AMA

 

Are there are any indications in the GOES-16 imagery to explain why the storm of interest along the boundary developed?

A 4 panel animation of the visible along with the 3 water vapor bands:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/4panel&loop_speed_ms=80

The visible band indicates what might be a gravity wave advancing east-southeast across the northeastern portion of the Texas panhandle (also shown in a subtle sense in the earlier IR loop).  The storm seems to initiate coincident with the passage of this feature across the boundary.  We include the 3 water vapor channels in an attempt to further identify this feature or any other feature that would not appear in the visible band.  The most obvious feature (at least in the mid-level WV band) appears later in the loop and is a north-south oriented brightness temperature gradient moving eastward, perhaps this is an approaching shortwave.  However, this comes after convective initiation for the storm noted above.  For this case, it’s difficult to definitely attribute convective initiation to any signature from the water vapor imagery, although a 4 panel like this may help in other cases.

Shortly thereafter, this region was in a mesoscale sector with 1-minute imagery, here is a loop with METARs included:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/16apr17/vis&loop_speed_ms=80

One point to note is that this was not a classic dryline with very dry westerlies behind it, rather the winds here are easterly and the moisture gradient is more subtle.  We do eventually see a storm initiate along the triple point discussed earlier north of the storm of interest.

Snow on the ground as depicted in the GOES-16 1.6 micron band

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

The GOES-16 visible (0.64 micron) loop shows what appears to be pretty straightforward – recent snow cover melting during daytime heating in southern Colorado:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/5apr17/B02&loop_speed_ms=140

Now let’s look at the GOES-16 1.6 micron loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/5apr17/B05&loop_speed_ms=140

At this wavelength, snow and ice surfaces are strongly absorbing, thus darker in this imagery.  However, all darker regions are not equally dark, see the highlighted region here:

annotated

Note the darker, almost black regions within the highlighted yellow ovals.

On the previous day, the highlighted regions did receive rain mixed with the snow following all snow that accumulated earlier in the day.  This likely led to a more slushy type of snow in the darker regions highlighted above.

It turns out, at this wavelength, the size of the ice particles on the surface matters.  Where there was rain on snow, the ice particles are larger relative to regions that received all snow.  As the melting began on 5 April, the snow cover in the darker regions highlighted were relatively wetter / “slushier” compared to other regions that received just snow.

For a much more detailed explanation of this effect in the 1.6 micron band, see this blog by Curtis Seaman:

http://rammb.cira.colostate.edu/projects/npp/blog/index.php/uncategorized/on-the-disappearance-of-lake-mille-lacs/

A potential application of this principle regarding the 1.6 micron band is identification of hail swaths left behind thunderstorms (assuming clear skies).  The hail swath should appear relatively dark in this channel, similar to the wet / slushy snow signature shown above.

 

Low-level moisture as observed from the GOES-16 split window difference product

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

The split window difference product is the difference between GOES-16 bands 13 and 15, that is 10.35 minus 12.3 microns.  The high spatial resolution of GOES-16 allows for detection of small-scale gradients in water vapor.  Lindsey et al. (2014) demonstrates the utility in this product for identification of local deepening of low-level moisture in a cloud-free environment.  The following schematic illustrates the concept that larger positive values of SWD correspond to deepening low-level moisture (in the absence of clouds):

 

diagram

In this example with simulated data, a cross section is taken across a dryline in Texas in clear sky conditions.  The cross section on the right of specific humidity (shaded) illustrates the local deepening (i.e., moisture pooling) along the dryline.  Split window difference values are maximized in the vicinity of the localized deeper moisture.

For an example, lets look at the GOES-16 visible (0.64 micron) animation:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/14apr17/B02&loop_speed_ms=80

Note the line of cumulus that develops oriented approximately east-west from Denver eastwards to just south of the Kansas / Nebraska border.  Before the line of cumulus develops, skies were clear.  Now let’s consider the split window difference product during the same time period:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/14apr17/SWD&loop_speed_ms=80

In this color table, positive brightness temperature difference values are green, followed by yellow then red for increasing magnitudes.  The low-level convergence boundary where the cumulus later develops is now readily identified.  Also, the magnitude of the brightness temperature difference increases (becomes more positive, towards the red values) prior to the development of the cumulus.  This is the signal of deepening moisture along the convergence boundary.  Supporting evidence from METARs at 1800 confirm the presence of the low-level convergence boundary with higher dewpoints north of the east-west segment of the boundary:

metar_18z

This product can be utilized to identify regions (in clear sky conditions) where localized moisture deepening occurs prior to the development of convective clouds (and potential convective initiation).  One caveat to this signal is that we tend to see this where moisture is relatively shallow, not in regions of deep low-level moisture.  More research is needed to understand the limits of how deep the moisture must be to not see this signal in the split window difference product.

Note that in this example, the split window difference product is band 14 (11.2 micron) minus band 15 (12.3 micron).  It should now be band 13 (10.35 microns) minus band 15 (12.3 microns), this is a small change that would not affect your interpretation of the imagery but it should make the signal a bit stronger (easier to identify).

Another example may be seen in Himawari over Bangladesh / eastern India, where the dryline commonly develops in April.  First, here is the visible loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/13apr17/bangla/B02&loop_speed_ms=120

Initially, skies are clear which is an important prerequisite to seeing this signal in the split window difference product.  Later, thunderstorms develop.

The split window difference product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/13apr17/bangla/SWD&loop_speed_ms=120

Shows increasing values of brightness temperature difference (orange/red in this color table) in clear skies before cumulus develops, followed by thunderstorms.  Again, the signal is increasing depth of moisture along the convergence line (slowly eastward moving dryline in this case).  Moisture depth in the pre-storm environment can be assessed with the nearest sounding from Calcutta:

2017041300.42809.skewt.parc

 

like the Colorado case, the moisture depth is shallow.  This seems to be one of the caveats for being able to identify this signal in the split window difference product.

References:

Lindsey, D.T., L. Grasso, J.F. Dostalek, and J. Kerkmann, 2014: Use of the GOES-R Split-Window Difference to Diagnose Deepening Low-Level Water Vapor. J. Appl. Meteor. Climatol., 53, 2005–2016, doi: 10.1175/JAMC-D-14-0010.1.

NCC, GOES-16 and the Pacific Northwest

Another round of storms are headed for the Pacific Northwest, bringing high winds and precipitation. Updates on the storms can be seen via the following link.

An observer on the ground can see the current storm via satellite, utilizing polar-orbiting data. A data product that comes from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on-board the Suomi-National Polar-orbiting Partnership (Suomi-NPP) satellite is the Near-Constant Contrast (NCC). The NCC is a derived product of the Day/Night Band (DNB) sensor which utilizes a sun/moon reflectance model to illuminate atmospheric features, sense emitted lights and assists with cloud monitoring during the nighttime. Figure 1 below shows the NCC image in AWIPS-II displaying the synoptic-scale storm over the western part of the United States at 0935Z, 7 April 2017. In this case, the NCC can show the location of the storm, recognize clouds and snow via reflected moonlight, sense the emitted city lights and highlight the gas flares in western North Dakota. In the top-right hand corner of Figure 1, the moon percent visibility and moon elevation angle are provided where a positive moon elevation angle implies that the moon is above the horizon, which in turn, provides crisp, distinct satellite imagery.

                                                             Figure 1

Picture1

Although the NCC does express limitations, with one of them in that users can only receive two satellite images per day (one during the day, one during the nighttime), users can use NCC in complement to the new GOES-16 data that just arrived within the past month in tracking storms. In Animation 1 below, utilizing the same domain in AWIPS-II, GOES-16 visible imagery (0.64um) was loaded and users can see the most-recent updates of the evolution of the storm, as it moves northward throughout the state of Washington. GOES-16 data ranges from 13-19Z, 7 April 2017.

                                                             Animation 1

animation

Disclaimer: The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

Lake-effect showers off Salt Lake and Seasonal Lakes

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

By Darren Van Cleave (NWS Salt Lake City)

On the morning of April 4, lake-effect showers existed off Salt Lake of Utah, as seen in the GOES-16 0.64 micron “Red” visible band:

lakeEffectRedVis

 

This temporal resolution (every 5 minutes) essentially offered information at the same rate as radar scans on the band’s development and movement. The spatial resolution also reveals interesting convective details.

For a comparison with current GOES 15 imagery, there are up to 4 visible images available (depending on system legacy):

new_goes_vis_loop

 

This comparison illustrates how valuable the temporal resolution can be, regardless of GOES-16 becoming GOES-East or GOES-West.

For overnight feature identification, we can also compare improvements between current GOES-IR:

goes15_IR

and the GOES-16 IR band:

goes16_IR

Several seasonal “lakes” in western Utah have standing water currently (including the relatively rate Sevier “Lake”), this can be easily seen in the 0.86 micron “Veggie” band on GOES-16 since this band offers significant contrast between water and land surfaces as water surfaces are quite dark:

seasonalLakesVeggie

 

compare this to the 0.64 micron “Red” visible band:

seasonalLakesRedVis

the same features are more difficult to discern.

This illustrates that the water/land differences of the 0.86 micron “Veggie” band are useful beyond just coastal regions.  As shown here, it even has utility in the Great Basin.

23 March 2017 Convection and Dust in Texas / New Mexico

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

On the afternoon of 23 March 2017, an upper level trough in the western US moving eastward was responsible for a strong lee cyclogenesis event.  The mesoscale sector for GOES-16 observed convection along the dryline with blowing dust on either side of it:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/23mar17/vis_meso&loop_speed_ms=80

The dust is lighter in color.  To the east of the dryline, it’s oriented in boundary layer rolls oriented NNW-SSE while west of the dryline where the boundary layer is much deeper in a hot / dry air mass, the dust is much more widespread.  What additional details can you see in the convection that you normally would not see with GOES-13/15?

In a GOES-16 4 panel loop showing all 3 water vapor bands in addition to the RGB Air Mass Product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/23mar17/wv_amrgb&loop_speed_ms=80

What additional features do you see?  Do you see the blowing dust in any of the water vapor bands?  Why would the dust be observed in these band(s)?  Hint, see the weighting function profile for the 3 GOES-16 water vapor bands, based on the sounding from Amarillo, TX at 0000 UTC 24 March:

ama_wf

 

Remember that the weighting function profile is valid for clear skies only

24 March 2017 fog / low stratus in the West

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

On the morning of 24 March 2017, there were some interesting fog / low stratus events in the West.  In northeast Montana, we can see fog surging up the Milk River Valley in the GOES-16 visible imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/24mar17/MT&loop_speed_ms=80

Saturated soil (due to snowmelt runoff flooding) from the Big Muddy Creek contributed to the fog event:

Meanwhile, at the same time in Arizona we see fog / low stratus sloshing back and forth (most likely in a canyon or valley) south of Sedona:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/24mar17/AZ&loop_speed_ms=80

 

22 March 2017 undular bore

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

An undular bore occurred on the morning of 22 March in the southern states and can be seen in this GOES-16 visible loop from southern Mississippi, northwestward across Louisiana into northeast Texas:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/22mar17/B02

Compare and contrast with the 1.6 micron loop over the same time period:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/22mar17/B05

 

14 March 2017 Blizzard

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

The 13-14 March 2017 blizzard that affected portions of the northeast can be viewed from a moisture perspective via the CIRA advected layer precipitable water (LPW) product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/14mar17/alpw

The loop shows the rapid advection of moisture northward from the Gulf of Mexico and off the southeast US coast.  Once the low started to deepen rapidly, moisture convergence also contributed to increasing moisture through a deep layer.

An overlay of winds (ASCAT surface winds over the sfc – 850 mb layer, and RAP model winds for their respective layers in the other panels):

alpw_winds_z3

gives an idea of the wind at different levels to readily identify moisture source regions.

A closer look at the ASCAT wind pass overlaid with the RGB airmass product and surface observations clearly shows the circulation around the surface low:

rgbam_ascat_16z_zout

Finally, GOES-16 1-minute visible imagery over the Washington DC vicinity provides a spectacular view of gravity waves that exist at many spatial/temporal scales:

http://rammb.cira.colostate.edu/ramsdis/online/loop.asp?data_folder=loop_of_the_day/goes-16/20170314000000&number_of_images_to_display=100&loop_speed_ms=40

 

GOES-16 7.34 micron band applications for the 6 March 2017 event

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

Authors: Dan Bikos, Chris Gitro, Ed Szoke and Chad Gravelle.

On 6 March 2017, a developing cyclone moved eastward across the central US, causing a number of hazards including severe thunderstorms, strong winds and wildfires.  This blog entry will focus on GOES-16 applications of the 3 water vapor bands, in particular, the 7.34 micron band.  The 3 water vapor bands may be viewed in this animation:

http://rammb.cira.colostate.edu/training/visit/loops/6mar17/loop.gif

Upper left image is the 7.34 micron (low-level water vapor) band, upper right image is the 6.95 micron (mid-level water vapor) band, lower left image is the 6.19 micron (upper-level water vapor) band, and lower right are surface observations with surface fronts annotated.  Analyzing the 3 water vapor bands in tandem provides a three-dimensional perspective since the bands “see” different levels in the vertical, where it “sees” can be identified from the weighting function profile of each band.  The GOES-16  weighting function profiles for clear sky conditions based on the 12Z sounding at Dodge City, KS for the three water vapor bands:

ddc_wf

Indicates the relative contribution in the vertical for each band.  All 3 bands show two peaks in weighting function profiles, near 400 mb and between 550 and 600 mb, however note the 7.34 micron band (blue line) has a more significant contribution as we look lower in altitude.  The 7.34 micron band has a peak weighting function around 600 mb, and generally is looking lower in the atmosphere than the other two water vapor bands.  This is the level we typically find important meteorological phenomena such as the elevated mixed layer (EML) or elevated cold front (ECF) therefore this band is better suited to identify these features compared to the other 2 water vapor bands.

The EML may be tracked in the GOES-16 7.34 micron band once it is positively identified as being associated with an EML with other datasets such as soundings.  The brightness temperatures with an EML tend to be relatively warm, however there are a number of reasons why relatively warm brightness temperatures may be observed (i.e., subsidence).  Morning soundings from Omaha and Dodge City within regions of warmer brightness temperatures confirm the presence of an EML:

OAX_12z

 

DDC_12z

 

The EML can be tracked eastward if there are no clouds to obscure the layer where the EML exists.  Note that this technique does not indicate if a low-level moist layer exists or not, since the weighting function response at low-levels is insufficient (it peaks above the moist layer usually).

In this case, the EML tracked well to the east, including regions where cloud cover exist to obscure the eastern edge of the EML:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/6mar17/B10&loop_speed_ms=80

Afternoon thunderstorms developed along the surface cold front, within the EML, which provided multiple factors favorable for thunderstorms to be severe.  In this case there were many severe reports.

Another feature that may be tracked in the GOES-16 7.34 micron band is the elevated cold front (ECF).  The ECF can be identified as a line of relatively warmer brightness temperatures, ahead of the surface cold front as seen in the earlier loop above:

http://rammb.cira.colostate.edu/training/visit/loops/6mar17/loop.gif

This annotated image from 16Z highlights the ECF:

Slide1

 

Note the ECF is ahead of the cold front (and dryline – hatched).

Additional confirmation of the ECF can be viewed via model output (RAP in this case), in this case a cross section oriented East-West from Denver to St. Louis:

Slide2

The contours of theta-e indicate the position of the surface cold front – tight gradient near the surface and sloping to near vertical with height – and the elevated cold front, at the nose of the sloping isentropes east of the surface cold front.  A loop of the cross section may be viewed here: 6Mar17_Ruc_Xsect

Additional supporting evidence of the ECF may be found in HRRR output of the 700 mb heights, temperature and wind:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/6mar17/hrrr

The loop shows a bulge of colder temperatures at 700 mb moving into eastern Nebraska / western Iowa that is ahead of the surface cold front.  Also, notice the gradient tightens in time.  This seems to agree with the line of warmer brightness temperatures we noted above in the 7.34 micron imagery.

A loop of HRRR 700 mb analyses between 15 and 23 Z demonstrates that the model forecast above appears to have captured the ECF:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/6mar17/hrrr/analysis

A RAP model cross section from eastern Colorado to just east of Kansas City of temperature advection, specific humidity and winds:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/6mar17/rap

Depicts cold advection aloft ahead of the surface cold front at 15Z, by 18Z downslope warming / diabatic heating decrease the cold advection signature west of the ECF, however by 21Z we see deep cold advection associated with the surface cold front beginning to catch up with the ECF.  Why does the ECF show up as a line of relatively warmer brightness temperatures (at least in the annotated image shown above at 1602 UTC and more subtly later in the afternoon)?  The cold advection is  accompanied by lower specific humidity (and lower RH as shown above) values, and in this drier air (assuming clear skies) the weighting function profile would “see” lower in the atmosphere, thus warmer brightness temperatures.

What effect does the ECF have on severe thunderstorm development?

Note the thunderstorms that develop ahead of the line of thunderstorms associated with the surface cold front in southwest Iowa:

Slide3

 

These storms appear to be in association with the ECF since no distinct surface convergence in that region is apparent in the METARs (lower right panel above) and this is where the ECF would be expected if we closely analyze the loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/6mar17/B10&loop_speed_ms=80

Let’s zoom in on the storms of interest in Iowa by analyzing the radar loop:

radar_warnings

Note the storms ahead of the main squall line associated with the surface cold front were difficult to discern in the GOES-16 loop (anvil cirrus from the squall line), however here these storms can be clearly seen and produced multiple severe hail reports.  By the end of the loop the surface cold front likely catches up with the ECF as noted by the squall line overtaking the isolated storms ahead of it.

Reference:

Parker, D.J., 1999: Passage of a tracer through frontal zones: A model for the formation of forward-sloping cold fronts. Q.J.R. Meteorol. Soc., 125, 1785-1800.

Madagascar: Tropical Cyclone Enawo

Madagascar! A small country located in Africa, just east of Mozambique was hit by Tropical Cyclone Enawo making landfall today, 7 March 2017. Right before landfall the tropical cyclone was near ‘Category 4 status’ with winds approximately 145 mph. It was the strongest landfall in 13 years. The storm will bring heavy precipitation and flooding to the country.

To aid in monitoring this storm, one can use the Day/Night Band (DNB) that utilizes a sun/moon reflectance model to monitor tropical storms, observe atmospheric features, sense emitted lights and assist with cloud monitoring during the nighttime. A static Day/Night Band (DNB) image of the cyclone at 2146Z, 6 March 2017 can be seen below in Figure 1.

DNB_2146Z_6_March_2017_modifiedFigure 1: The DNB highlighting the location of the Tropical Cyclone Enawo at 2146Z, 6 March 2017, just northeast of the country of Madagascar. The DNB can observe atmospheric features such as lightning, clouds and the eyewall of Tropical Cyclone Enawo. DNB can also sense anthropogenic lights (i.e. emitted city lights) in Madagascar and from neighboring islands east of Madagascar. In the top-right corner of the image, the moon percent visibility and moon elevation angle are also provided.

For the latest updates on Tropical Cyclone Enawo click here.

Comparison of GOES-16 with GOES-13

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

One of the 1-minute mesoscale sectors for GOES-16  captured a series of polar low-like circulations over northeastern Lake Ontario moving into New York:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3mar17/B02&loop_speed_ms=80

Notice how much more clearly these circulations appear in GOES-16 as compared to current GOES-13:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3mar17/GOES13

 

GOES-16 Mountain Wave clouds on 3 March 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

Mountain wave (orographic cirrus) clouds were observed in the Rocky Mountain region on the morning of 3 March 2017 as observed in this 10.35 micron image:

annotation_20170303

 

Let’s examine a series of loops of different channels available from GOES-16.  The loops will span through sunrise (i.e., start in darkness and transition to daytime).

The familiar IR channel at 10.35 microns has always been a good channel for identifying mountain wave clouds and will continue to be an ideal channel for identification:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3mar17/B13&loop_speed_ms=80

The mountain wave clouds in western Montana develop during the loop while the mountain wave clouds downwind of the Bighorn range in north central Wyoming dissipate during the loop.  Mountain wave clouds are easily identified since brightness temperatures are particularly cold, making them stand out when compared, for example, to the clouds associated with the disturbance passing from eastern Montana into western North Dakota.  Mountain wave clouds in north central Colorado exist at the beginning of the loop and expand in time.

One of the new channels on GOES-R is the 1.38 micron channel (“Cirrus band”), which is useful for identifying mountain wave clouds since cirrus clouds stand out while low-level level clouds do not:

 http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3mar17/B04&loop_speed_ms=80

Next we will look at the 3 water vapor channels available on GOES-R.

First, the upper-level water vapor band at 6.2 microns:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3mar17/B08&loop_speed_ms=80

Next, the mid-level water vapor band at 6.9 microns:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3mar17/B09&loop_speed_ms=80

Finally, the low-level water vapor band at 7.3 microns:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3mar17/B10&loop_speed_ms=80

What additional information do the water vapor bands show?

The water vapor bands typically have a subsidence signature (relatively warmer brightness temperatures) slightly upwind (i.e., west in this case) of the mountain wave clouds.  This subsidence signature can be seen on all 3 water vapor channels, but it’s more subtle in the upper-level water vapor imagery.  The depth of the subsidence can be assessed by looking at all 3 channels in tandem.  It may be worthwhile noting trends in the subsidence signature for the mountains in your forecast area during these mountain wave cloud events.

Our last loop will be a channel difference product, the 3.9 minus 11.2 micron loop, commonly known as the fog / low-stratus product since it’s been around for a long time with current GOES channels.  Since this product involves the 3.9 micron channel, we have to be aware of the solar reflected component during the daytime hours.  This can easily be tracked before and after sunset in this loop:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3mar17/fog&loop_speed_ms=80

Recall from this training session that mountain wave clouds tend to be composed of relatively small ice crystals which are highly reflective.  By subtracting out the emitted component we are left with the solar reflected component during the daytime, this helps make the mountain wave clouds stand out from other clouds.

Advected LPW product in identifying circulations in the vertical

On the evening of 21 February 2017, a well defined mid-level circulation moved into the San Francisco Bay region.  This circulation was responsible for a region of convection in the vicinity, mostly showers but a few thunderstorms with some low-level rotation.

The mid-level circulation can be readily identified in the 700-500 mb layer of the CIRA advected layer precipitable water (LPW) product (lower left panel) :

mid-level_circulation

The circulation also shows up, to a lesser extent, in the 850 – 700 mb layer (upper right panel).

A loop of the product is shown here:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21feb17_alpw

This product consists of moisture information from microwave sensors on various polar orbiting satellites, which is then advected by a model for a very short-term forecast.  Mid-level circulations that are sufficiently large enough to be resolved can be readily observed, and also indicate where they are in the vertical, providing information about the depth of the circulation.

This product is new, building on the previous product termed the layered precipitable water product which 1) made use of lower resolution data (i.e., more blocky) and 2) did not include advection from a model.  A loop of the LPW product for this case:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21feb17_lpw

still shows the mid-level circulation, but not nearly as well due to the more coarse resolution of the data (and perhaps due to advection not being included also).

7-8 February 2017, New Orleans Power Outage

After a series of tornadoes ripped through southeastern Louisiana and southern Mississippi on 7 February 2017, a power outage occurred in eastern New Orleans affecting 10,000 plus people. Below is a screenshot of the affected areas: the red lines imply the power lines are off. Additionally, there are a different colored triangles on the map: the blue, yellow and red colors show 1-50, 51-250, and 251-1000 customers that were affected, respectively, and out of power.

modified_screenshot

Figure 1: Shows the areas in New Orleans affected by the power outage caused by the tornadoes that occurred on 7 February 2017.

To verify these power outages, one can use the Day/Night Band (DNB) that utilizes a sun/moon reflectance model that illuminates atmospheric features, senses emitted lights and monitors clouds during the night-time. Figure 2  shows the city of New Orleans before the power outage on 7 February 2017 at 0807Z and Figure 3 shows the magnitude of the power outages in Eastern New Orleans at 0749Z. Refer to the red circle in both images.

DNB_020717_0807Z_New_Orleans

Figure 2: A DNB image of the city of New Orleans before the power outage occurred. The red circle is the area of interest to compare to Figure 3. The moon percent visibility and the moon elevation angle are also provided in the top-right hand corner, implying that the moon is above the horizon during this time period.

DNB_020817_0749Z_New_Orleans

Figure 3: A DNB image of the city of New Orleans after the power outage occurred. The red circle is the area of interest to compare to Figure 2. Approximately 10,000 plus people were affected by the power outage.

A supplemental animation can be seen here.

More Precipitation for the West Coast, 3 February 2017

Who would have thought that the West Coast would get more precipitation? In a series of atmospheric river events that pummeled the West Coast a few weeks ago, another synoptic-scale precipitation event approaches….yet again. As of this morning, parts of Washington, Oregon and California are expected to have 3-5 inches of rain and from 1-3 feet of snow in the Sierra Nevada and Cascade Mountains.

Figure 1 below, shows the latest Near-Constant Contrast (NCC) satellite imagery of the storm, as of this morning at 1057Z, 3 February 2017. If not familiar with the NCC, it is a derived product of the Day/Night Band (DNB) which utilizes a sun/moon reflectance model that illuminates atmospheric features, senses emitted lights and assists with cloud monitoring during the night-time. The DNB is a sensor and is apart of the 22 spectral channels on the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on-board the Suomi-National Polar-orbiting Partnership (Suomi-NPP) satellite.

NCC_modifiedFigure 1: NCC imagery taken at 1057Z, 3 February 2017 of the synoptic-scale system moving towards the West Coast of the United States. Although with no moon present, the NCC is able to detect the storm, the clouds and the emitted city lights along the coast. 

What’s unique about this precipitation event is that the moon is still below the horizon (i.e. no moon present to assist in sensing atmospheric features), but the clouds are still recognized. This unique situation is due to an atmospheric phenomenon called ‘atmospheric nightglow’. Nightglow is caused by photochemical reactions that occur during the day-time and illuminate a faint glow during the night-time. The DNB sensor recognizes the nightglow and senses the ambient atmospheric features.

In complement to NCC in analyzing the storm, the NOAA Unique Combined Processing System (NUCAPS), is an operational Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) physical retrieval algorithm that display vertical temperature and moisture soundings, spaced apart at ~50 km (30 miles). NUCAPS also contains data quality flags (Figure 2) that are assigned to each colored sounding: good soundings are in green, use caution with yellow soundings, and bad soundings are in red.

NUCAPS_modifiedFigure 2: NUCAPS soundings overlayed onto NCC imagery of the storm moving towards the West Coast. The data quality flags are partitioned into 3 colors: green, yellow and red.

The NUCAPS soundings can be investigated more in depth via the animation link shown here. In the animation (embedded with Skew-T plots), one can see the instability, and the moist and dry layers of the atmosphere within the storm.

Furthermore, the last bit of eye candy highlighting the storm is the ATMS Total Precipitable Water (TPW) Product (Figure 3), which shows the total amount of water vapor (expressed in inches) within a vertical column of the atmosphere. The color scale range goes from 0-2.5 inches, where the storm is apparent and has TPW values of ~0.7 inches.

ATMS_TPW_020317Figure 3: The ATMS Total Precipitable Water product at 1033Z, 3 February 2017, which shows the amount of water vapor within a vertical column of the atmosphere.

17-22 January Significant Precipitation for the West Coast

The West Coast of the US experienced multiple storms between 17-22 January 2017 that resulted in substantial precipitation:

ahps_precip

Early in the period, an Atmospheric River with origins from the central tropical Pacific Ocean advected towards Washington, Oregon and northern California:

Slide1

This product is the CIRA Layer Precipitable Water (LPW) product (displayed here in inches) which denotes water vapor distribution in various layers.  Upper left is the surface to 850 mb layer, upper right is 850 to 700 mb layer, lower left is 700 to 500 mb layer and lower right is 500 to 300 mb layer.  The red oval generally indicates the position of the atmospheric river of interest.  This product not only indicates the position of the atmospheric river, but also the magnitude of the moisture between the various layers.  The vertical distribution of moisture can be compared between different storms to assess relative impact.  Generally speaking, higher PW amounts at higher levels in the vertical promote a higher likelihood of warm-rain processes and higher precipitation efficiency.  These factors are important in evaluating flood potential.

A loop of the LPW product for the period 17-23 January:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/19jan17_lpw

Depicts the evolution of the various atmospheric rivers throughout the week, leading to multiple heavy precipitation events along the West Coast.  By the end of the loop, we note an atmospheric river affecting southern California:

Slide2

Post Atmospheric River Events in California, 011317

As we all know there were two atmospheric river events that occurred in California in the past week and a half. Both atmospheric river events dumped large amounts of precipitation and snow to the state of California. There was a plethora of reports indicating mudslides, flooding and several feet of snow (10+ feet of snow) in the Sierra Nevada Mountains which also includes Lake Tahoe.

After these events concluded, approximately 12 January 2017, I was curious to see what the Near-Constant Contrast (NCC) satellite imagery features were picked up during the night-time hours. For readers that are not familiar with NCC, it is a derived product of the Day/Night Band (DNB), which utilizes a sun/moon reflectance model that illuminates atmospheric features, emitted lights and assists with cloud monitoring during the night-time hours.

The below AWIPS-II screenshot, shows the NCC satellite imagery hovered over the North-Central portion of California at 0910Z (0110 local time) on 13 January 2017. In the bottom-left corner is the moon percent visibility and the corresponding moon elevation angle above the horizon (expressed in degrees). Due to the fact that this observation was taken near the full moon stage of the lunar cycle, the atmospheric features can be easily detected. In the satellite image one can see clouds seen off the coast of California, the emitted city lights of San Francisco and Sacramento. Additional features that can be seen are the low clouds and fog that are located in the north-western part of the state, the snow over the Sierra Nevada mountains, and some high-level clouds hovering over the city of Reno, Nevada.

states2

The high level clouds are not as discernible over the mountains, since both the clouds and snow in the mountains both reflect the color white. By inference, one can differentiate between the two by the texture difference between the clouds (broad, uniform, white swaths) and snow (white dendritic formations, over the mountains). The Cooperative Institute for Research in the Atmosphere (CIRA), has been working on products that could help discriminate between snow and mid-to-upper level clouds, which hopefully one day will be implemented into AWIPS-II.

Atmospheric River event of 7-9 January 2017

A significant atmospheric river affected the west coast on 7-9 January, 2017.  The GOES water vapor imagery shows the development of clouds associated with an approaching trough off the west coast, these clouds advected into the west coast:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8jan17_wv&loop_speed_ms=80

and resulted in significant precipitation across California:

ahps_precip

 

at times, we can infer moisture from the water vapor imagery.  In this case we can infer abundant moisture where the clouds develop and advect onshore, however the water vapor channel is not the best way to assess moisture.  Instead, making use of a satellite derived moisture product (i.e., precipitable water) is a much better approach.  The CIRA layered precipitable water (PW) product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8jan17_lpw

Provides not only a plan view of the moisture, but also a 3-dimensional perspective since it observes moisture at different layers in the vertical.  The lowest layer (upper left) is the PW (units are inches) in the surface to 850 mb layer, the upper right is the PW in the 850-700 mb layer, the lower left is the PW in the 700-500 mb layer, and the lower right is the PW in the 500-300 mb layer.  Typically the greatest magnitude of moisture is found in the lowest layer, however what the mid/upper level layers provide can be very important for significant precipitation events.  Usually, significant precipitation events are characterized by relatively high precipitation efficiency, which tends to exist in an environment that is saturated through a deep layer.  Therefore, if the PW is relatively high in the lower, mid and upper levels, the precipitation efficiency is near 100%.  As you may suspect, the PW values in all 3 layers for this case are quite high.  Keep in mind, this product utilizes data from polar orbiting microwave instruments which means they can see through (most) cloud cover, unlike the GOES Imager or Sounder instruments.

The Orographic Rain Index (ORI) is a product developed by CIRA made specifically for these type of Atmospheric River (AR) events.  The product is based on research by Alan White and others from NOAA/PSD that investigated water vapor transport into terrain for AR events.  ORI produces an index related to this water vapor transport using satellite derived TPW combined with GFS winds and high resolution terrain data.  The idea is to highlight regions of terrain induced short-term flooding potential.   For this particular case, the ORI product:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8jan17_ori

shows higher values oriented along various ridgelines in central California that would be most susceptible to flooding for this storm.  A more detailed description of ORI can be found at http://rammb.cira.colostate.edu/research/goes-r/proving_ground/cira_product_list/orographic_rain_index.asp

Real-time data is available at:

http://rammb.cira.colostate.edu/ramsdis/online/goes-r_proving_ground.asp#Orographic_Rain_Index_(ORI)

Atmospheric Rivers typically have relatively strong winds in the low to mid layer to help advect moisture rapidly.  The winds in this layer were particularly strong for this event, with wind gusts as high as 173 mph observed:

squaw_summit_obs

Link to post from Twitter

Lake Effect Snow: Buffalo and Rochester, NY, 121416-121516

Lake Effect Snow, what is it? The general public may not be familiar with this term, because this meteorological phenomenon does not occur in the Rocky Mountain region of the United States. However, lake effect snow is a common occurrence in the Great Lakes Region of the United States.

So what is lake effect snow anyway? In short, lake effect snow is produced from cold, dry air passing over warm waters, where snow falls on the lee side of the lake. Significant convective snow bands can be generated from this type of event and can lead towards extensive snow totals.

In Buffalo, NY, which lies on the east side of Lake Erie, has had lake effect snow events that occurred these past few days. Rochester, NY also has experienced lake effect snow since the city lies on the southern edge of Lake Ontario.

To monitor such events not only during the day, but especially during the night-time one can use polar-orbiting data. The Near-Constant Contrast (NCC), a derived product of the Day/Night Band (DNB), utilizes a sun/moon reflectance model that illuminates atmospheric features and clouds and senses emitted lights during the night-time hours. The NCC is a product which can help NWS forecasters assess the state of the atmosphere in complement with products that forecasters already have, such as infrared imagery (IR).

The NCC is shown in Figures 1 and 2 below, showing static images of western New York from 14-15 December 2016. The images highlight the emitted lights from the cities of Buffalo and Rochester along with the surrounding towns. The cloud cover and convective snow bands off of Lake Ontario and Lake Erie are apparent on 15 December 2016 at 0633Z (Figure 2). In the bottom left corner of both figures show the approximate moon percent visibility and the corresponding moon elevation angle presented in degrees above the horizon.

image1Figure 1: NCC image on 14 December 2016 at 0652Z, one can see the emitted light from the cities of Rochester and Buffalo, NY and neighboring towns. Lake Erie and Lake Ontario are shown as well. One can also see the high percentage of cloud cover over Western New York.

image2

Figure 2: NCC image on 15 December 2016 at 0633Z, one can see the convective snow bands that were produced over Lake Erie and Lake Ontario as they head towards Western New York. The cloud cover is still persistent over the area as well.

For more information, the latest forecasts for the area can be seen via the National Weather Service (NWS), Weather Forecast Office (WFO), Buffalo, NY and Rochestor, NY.

Nantahala National Forest Wildfires 11-11-16

Above average temperatures have been persisting over a great portion of the United States, bringing drought-like conditions. One of the areas that have been experiencing the lack in precipitation is the Nantahala National Forest located in Western North Carolina. In Figure 1, the U.S. Drought Monitor shows the severity of the drought, highlighting areas of the Nantahala National Forest under Extreme (red) and Exceptional (purple) drought. More statistics of the drought can be seen and evaluated through the U.S. Drought Monitor website.

NC_Drought_Monitor_III

Figure 1: The U.S. Drought Monitor of the state of North Carolina and the criteria for drought intensity (right). Currently, one can see that Eastern North Carolina is experiencing no drought, however, Western North Carolina increases in drought intensity, seen by the black circle in the figure.

The extreme drought in Western North Carolina has prompted several wildfires in the Nantahala National Forest. To monitor these wildfires not only during the day but during the night-time hours one can utilize the Near-Constant Contrast (NCC) which is a derived product of the Day/Night Band (DNB) sensor on-board Suomi-NPP, a polar orbiting satellite. The NCC utilizes a sun/moon reflectance model that helps illuminate atmospheric features (i.e., clouds, lightning) and recognizes emitted lights sources (i.e., wildfires and city lights) around the globe.

To infer the current locations of the wildfires in Western North Carolina, an NCC image of a clear-sky atmosphere, during the full moon stage of the lunar cycle is utilized. Figure 2 below highlights a static image of the emitted city lights in Western North Carolina on 17 October 2016 at 0640Z.

image1

Figure 2: NCC image of the emitted city lights located in Western North Carolina on 17 October 2016. In the top-left corner of the figure is the approximate percent visibility of the moon (~full moon) and the corresponding moon elevation angle (in degrees) above the horizon.

Figure 2 will now be compared to Figure 3 (below). Figure 3 consists of the current locations of the wildfires in North Carolina, denoted by the white circles, as of 0710Z on 11 November 2016.

image2

Figure 3: NCC image of the emitted city lights and the wildfires in Western North Carolina on 11 November 2016. In the top-left corner of the figure is the approximate percent visibility of the moon (~full moon) and the corresponding moon elevation angle (in degrees) above the horizon.

An additional tool to complement the NCC is the GOES Infrared (IR) 3.9 um satellite imagery (Figure 4) that can dictate hotspots; areas within the imagery that are very hot, such as wildfires. One can use the same domain, that has been utilized in Figure 2 and 3 and overlay it with the IR imagery. One can see some of the hotspots, expressed in brightness temperature (dark grey to black colors), are located in the same white circles that were seen in Figure 3, verifying the location of the wildfires.

image3

Figure 4: A corresponding GOES IR 3.9 um satellite imagery at 0710Z, 11 November 2016, showing the brightness temperatures (in degrees Celsius) of the hotspots. The same white circles that were used in Figure 3 were overlayed in this figure to complement and verify that the wildfires are located in these specific areas. 

For more information on the wildfires click the link, and to see the animation of the fires discussed above, click Figure 5 below.

animation2

Figure 5: An animated composite of Figures 2, 3 , and 4, please click on this figure.

11 November 2016 Himawari imagery around Japan

In this blog entry, we look at imagery from the AHI instrument on Himawari-8 with  anticipation for GOES-R over the USA as the ABI instrument is very similar to AHI.

We start with the 0.64 micron (visible) band during the daytime hours of 11 November:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11nov16_b03

North of Japan, we observe a circulation (as depicted below) associated with a surface low with convection to the east and southeast of it along a frontal boundary.

annotated_vis

 

Also note the islands that appear to be blocking the flow (as depicted above).

Next, we look at the 1.6 micron band, which will be one of the new GOES-R bands:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11nov16_b05

The 1.6 micron band is useful for cloud top phase discrimination.  Ice clouds are relatively absorbing at 1.6 microns, therefore glaciated clouds appear darker.  As the deeper convective clouds in the vicinity of the circulation and east of it along the front grow in vertical extent, they become darker, which indicates cloud top glaciation associated with vertical growth.  This information is in addition to many of the features we already viewed in the visible imagery at 0.64 microns.

Next we’ll look at 2 of the 3 water vapor bands that will be available on GOES, the 7.3 micron low-level tropospheric water vapor band:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11nov16_b10

and the 7.0 micron mid-level tropospheric water vapor band:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/11nov16_b09

Looking at multiple water vapor bands with GOES-R will allow a better 3-dimensional perspective of the scene of interest.

The weighting function profile for the 7.3 micron band sees a lower layer (in altitude) relative to the 7.0 micron band.  The 7.3 micron band still shows some of the low-level clouds we observed in the visible imagery, along with numerous waves.  This channel also clearly delineates the upward and downward vertical motions around the surface low circulation.  Subsidence is associated with warmer brightness temperatures while lift is associated with cooler brightness temperatures.  We also see colder cloud tops being enhanced where convection develops.

The 7.0 micron band depicts even fewer low-level clouds compared to the 7.3 micron band due to the weighting function seeing a higher layer (in altitude), note the brightness temperatures are overall colder.  The circulation can still be seen in the imagery, which provides evidence for how deep the circulation is.  As you gain experience in viewing circulations with the 3 water vapor channels on GOES-R, you will get a better idea of the intensity and vertical extent of these circulations which can lead to better anticipation of trends observed in the imagery.

South-Central Colorado: Junkins Fire

Another fire started in south-central Colorado, tabbed the ‘Junkins Fire’. According to the Denver Post, the cause of the fire is unknown however, it was first spotted at 0345 AM MDT in Custer County on 17 October 2016. The fire has spread rapidly over the past day and a half due to high winds and low relative humidity. As of this morning, 18 October 2016, the Junkins Fire had burned over 15,000 acres of land and 0% of the fire was contained. Several structures in the area have already been burned while more than 175 homes have been evacuated. Figure 1 shows an image of the fire and can be seen below via the ‘Wildfire Todaywebsite.

JunkinsFire-824-am-Oct-17-2016

Figure 1: An image taken of the Junkins Fire in south-central Colorado during the early morning hours of 17 October 2016 (Source: Wildfire Today).

To aid in monitoring the Junkins Fire, polar-orbiting satellite data from the Suomi National Polar-orbiting Satellite (Suomi-NPP) can be utilized. One of the polar-orbiting satellite data products is the Near Constant Contrast (NCC). NCC can monitor and observe night-time light emissions (e.g., wildfires, city lights) and atmospheric features (e.g. clouds, lightning) utilizing a sun/moon reflectance model. An NCC animation (Animation 1) is shown below highlighting south-central Colorado on 17 October 2016 @ 0819Z (0219 local time) and 18 October 2016 @ 0800Z (0200 local time). Make sure to click the image and the animation will start.

animation

Animation 1: The animation spans over two days, 17-18 October 2016, and displays where the emitted lights of the Junkins fire is relative to the emitted city lights of Pueblo. The percent visibility of the moon, and the elevation angle of the moon above the horizon are seen in the top right-corner of the animation. The Junkins Fire via satellite imagery can be seen quite well since the fire is occurring during the Full Moon stage of the lunar cycle, and where the moon is above the horizon. 

If one takes a closer look at the Junkins fire in Figure 2 below, one can see how close this fire is in proximity to the smaller towns around the area (i.e., the towns of Wetmore, Beulah and Rosita). This close proximity is prompting current evacuations for people that are living in and or near those areas.

image3

Figure 2: An NCC image of the Junkins Fire (white circle) relative to the neighboring towns, I-25, and the city of Pueblo. 

For more information on the status of the fire refer to the Denver Post website and the Weather Channel.

Hurricane Matthew 100616

Hurricane Matthew will be making landfall along the Florida coast later this evening, 6 October 2016. Matthew is currently (~5PM EDT) a Category 4 hurricane topping out at winds of 140 miles per hour. Figure 1 below show Matthew’s path headed toward the east coast of Florida, Georgia and the Carolinas producing significant storm surge and flooding along the coast.

100616154742W5_NL_sm_5PM_EDTFigure 1: A National Hurricane Center image of the 5-day forecast for Hurricane Matthew. The hurricane is forecast to run along the east coast of Florida, Georgia and the Carolinas. In the image display, the M, H, and S stand for ‘Major Hurricane’, ‘Hurricane’, and ‘Tropical Storm’ respectively. 

A current GOES infrared (IR) satellite loop shows the evolution of the hurricane approaching the coastline via the CIRA-RAMMB link here.

The Near-Constant Contrast (NCC) a derived product of the Day/Night Band (DNB) utilizes a sun/moon reflectance model (via AWIPS-II) to track hurricanes during the night-time. Figure 2 shows this morning’s satellite overpass (~0646Z, or 0246 local time) from polar-orbiting data seen below. H_Matthew_100616_NCC_2Figure 2: An NCC image showing the location of Hurricane Matthew in relation to the state of Florida. One can see the emitted city lights, lightning and the subtle location of Hurricane Matthew’s Eye Wall. In the right hand corner of the image shows the percent visibility of the moon (approximate) and the elevation angle of the moon, -37.54 degrees. This elevation angle value implies that the moon is below the horizon and the atmospheric features displayed in NCC are seen predominately from atmospheric nightglow and by the surrounding emitted light sources. 

Emergency evacuations have been declared along the coastline in Florida and Georgia. Stay tuned from more updates over Hurricane Matthew.

Puerto Rico Power Outage 092116

Does everyone just love power outages? They occur at the most inconvenient times, when your cooking dinner, doing work on your home computer or watching the football game. But have you ever experienced a widespread power outage that affected thousands of customers? That’s exactly what happened in Puerto Rico late Wednesday night (21 September 2016) when a fire started near the Aguierre Power Plant located on the southern side of the island. The Power Plant was out of commission and over 1.5 million customers lost power for at least 12 hours. A great percentage of customers still do not have power as of right now (22 September 2016 @ 1750 EDT). The fire was caused by a power-switch that became overheated causing a large mineral oil tank to explode. More information on the incident can be seen via CNN and the Washington Post.

Interestingly enough, one can see the massive power outage over Puerto Rico via polar-orbiting satellite data. The Near-Constant Contrast (NCC), a derived product of the Day/Night Band (DNB) sensor on-board the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite can be utilized to see atmospheric features and emitted lights during the night-time. In this case from Figure 1 and 2 below, we can infer the areas in Puerto Rico that were affected by the power outage (i.e., the decrease in city lights seen from satellite from the 21 September to the 22 September 2016).

PR_O_5

Figure 1: The NCC product highlighting the emitted lights from cities and towns on the island of Puerto Rico. The satellite image is taken on 21 September 2016 @ 0627Z before the power outage occurred. The Aguierre Power Plant where the fire first started and took out the power-grid in Puerto Rico is also seen. In the top-right corner of the figure one can see the approximate moon phase of the lunar cycle, where there is a correlation between the distinct satellite imagery and moon phase. 

PR_O_4

Figure 2: The NCC product shows the decrease an emitted lights from cities and towns on the island of Puerto Rico on 22 September 2016 @ 0608Z after the power outage occurred. In the top-right corner of the figure one can see the approximate moon phase of the lunar cycle. 

Hurricane Season in the Atlantic: Invest Area 99L and TS Gaston

Ahh…it is that time of year again, it’s hurricane season for the Atlantic and Pacific Oceans; the blog will focus on the Atlantic hurricane activity that is ongoing. Two to mention that are active right now are the Invest Area 99L and Tropical Storm Gaston. The current status of both Invest Area/Tropical Storm whereabouts can be seen via the National Hurricane Center website.

Monitoring severe tropical weather events from the range of invest areas, tropical depressions, tropical storms to the order of Category 1-5 hurricanes by National Weather Service (NWS) forecasters can be challenging. Whether if the tropical event is occurring during the day or night, NWS forecasters can utilize satellite products and supplemental products to provide the best forecasts for the general public. One of the big challenges for forecasters is monitoring these events during the night-time. A product to consider is the Near-Constant Contrast (NCC), derived from the Day-Night Band (DNB), a sensor on the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on-board the Suomi-National Polar-orbiting Partnership (Suomi-NPP) satellite. The NCC has the capability of observing night-time light emissions and atmospheric features across the globe, including monitoring tropical storms.

The following animated gifs below highlight the NCC with infrared (IR) satellite imagery in the early morning near 5Z and 6Z, 26 August, 2016 of Invest Area 99L (Figure 1) and Tropical Storm Gaston (Figure 2), respectively.

Invest Area 99L

99L_Animation_III

Figure 1: The Invest Area 99L is currently hovered over Hispaniola in the Caribbean Sea (white circle) and has a 30% chance of formation in the next 48 hours. Within the animation, NCC is shown first depicting the distinct cloud cover of the invest area, and IR shown second showing the brightness temperatures (in degrees Celsius) of the cloud convective tops. If you look closely you can see lightning (horizontal white streaks) embedded in the storm. Cloud cover and city lights are depicted as well.

Tropical Storm Gaston

Gaston_Animation_II

Figure 2: Tropical Storm Gaston located in the mid-Atlantic Ocean has a chance of becoming a Category One Hurricane within the next 48 hours as well. Within the animation, NCC is shown first depicting the circulation of the tropical storm in the Atlantic, and IR shown second highlighting the brightness temperatures (in degrees Celsius) of the cloud convective tops. Lighting are also seen embedded in the storm while cloud cover and city lights are depicted as well.

Additionally, here are the variety of forecast track model outputs for Invest Area 99L and Tropical Storm Gaston for the next few days.

VIIRS Flood Detection Product: Gulf Coast Flooding, August 2016

The Gulf Coast has taken a major hit lately with intense rainfall and flooding across the area (Figure 1, below). This past weekend (12 August 2016 through today) there has been a Federal Flood emergency declared in the state of Louisiana. The areas that has been hit the most is south-central and southeastern Louisiana. A summary of the flooding with images across the state can be seen through the link here.

gulf_coast_flooding

Figure 1: Two people stranded in high flood waters in Louisiana over the past weekend (Source: The Weather Channel).

From the lower mississippi River Forecast Center (RFC), Figure 2 highlights the observed precipitation (cumulative) for the last five days (not including today) along the Gulf Coast states.

RFC_GCF

Figure 2: The observed precipitation for the state of Louisiana for the last five days. One can see the south-central and southeastern parts of Louisiana have been tremendously inundated with these areas having more than 20 inches of rainfall in the short time span. 

Interestingly enough, observed precipitation from events such as the one mentioned above can be compared to satellite observations. This comparison can be shown through the animated GIF below that changes from the Google Map view of south-central and southeastern Louisiana to the Visible Infrared Imaging Radiometer Suite (VIIRS) Flood Detection Product. The date of the images is for 15 August 2016 at 1930 UTC. Make sure to click the image for animation to occur.

VIIRS_Flood_Product

If we take a closer look at the VIIRS Flood Detection Product (Figure 3) we first look at the legend for interpretation. The product shows the ‘floodwater fraction percentage’ from 0-100% for each pixel, where the spatial resolution is at 375 meters. In addition to the floodwater fraction percentage, are the scene types that can be present in each pixel. Scene types range from land (LD, light brown), Supra-snow/ice water (SI, magenta), snow (SN, white), ice (IC, aqua), clouds (CL, light grey), cloud shadow (CS, dark grey), and open water (WA, dark blue). From the legend and images, one can see and discern the areas of Louisiana that experienced the most flooding which coincide with observed precipitation measurements.

image2

Figure 3: The VIIRS Flood Detection Product highlighting the areas of Louisiana that experienced the most flooding. The legend and interpretation of the color scheme is shown above.  

1-minute GOES-14 SRSO for 9 August 2016 Montana storm

On 9 August 2016, GOES-14 Super Rapid Scan Operations for GOES-R (SRSOR) collected 1-minute imagery of a severe thunderstorm in southeast Montana:

 http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=dev/lindsey/loops/9aug16_g14_montana&image_width=1020&image_height=720&loop_speed_ms=80

The 1-minute visible satellite imagery is particularly important for this case, since the storm is in southeast Montana where WSR-88D coverage is limited due to the large distance from the nearest radars:

WSR-88DCONUSCoverage2011

If one were looking at the closest radar in analyzing this storm, they would be sampling very high up into the storm, limiting the utility of the available radar data.

A meteorologist with experience in utilizing satellite imagery in assessing severe thunderstorms would immediately be able to recognize this storm as severe, particularly due to the high temporal resolution of the visible satellite imagery.

The storm has an overshooting top that is rapidly changing.  The upshear (southwest) edge of the anvil cirrus is crisp and well defined.   There are inflow feeder clouds east of the flanking line, such as observed at this time:Slide2

 

these were long lasting as well.

There were also times of invigorated cumulus above the RFD, another sign of a severe storm that is undergoing intensification or near peak intensity, here are some examples from two different times:

Slide1 Slide3

This storm was associated with hail reports up to 4 inches in diameter.  For additional imagery/discussion on this event, see this link on the CIMSS satellite blog.

The main point of this discussion is that GOES satellite imagery may provide useful information in assessing for a potential severe thunderstorm.  This is particularly the case with higher temporal resolution data such as the 1-minute imagery we see in this loop and for regions where radar coverage is sparse or potentially when the closest WSR-88D is down.  We look forward to GOES-R when 1-minute imagery will be available much more often than currently available, and of course the higher spatial resolution will also assist in identification.

NCC Imagery, Colorado Fires In July

In early-to-mid July there were several fires that started in Colorado. The fires were either lightning or human caused. Three fires to note were the Beaver Creek Fire (northern Jackson County, CO), Cold Springs, Fire (western Boulder County, CO) and the Hayden Pass Fire (western Fremont County, CO). They were all active approximately around the same time-frame, between 9-12 July 2016. These fires were also seen together via satellite imagery during the night-time. The Near-Constant Contrast (NCC) satellite product has the ability to monitor wildfires and other atmospheric features during the night-time. In Figure 1 below, a NCC image shows all fires, denoted by the white circles and the adjacent city lights along the Colorado Front Range shown by the arrows, on 12 July 2016.

071216_modified

Figure 1: A NCC image of the wildfires occurring in Northern and Central Colorado at 0838Z on 12 July 2016. In the t0p-right portion of the image one can see the approximate phase of the moon on 12 July 2016, which at this moment in time was the first-quarter stage of the lunar cycle. It is important to note the night-time satellite imagery is dependent on the phase of the lunar cycle, that is, the satellite imagery can be better seen during the full-moon stage of the lunar cycle, and less seen during the new moon stage of the lunar cycle.

An animation link below highlights the evolution of the fires between 9-12 July 2016.

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=visitview%2Fcustom%2FFires_07_12_16%2F

If one looks closely at the animation, the city lights appear to be seen in slightly different locations. The city lights move ever-so slightly from one day to the next. This is due to fact that the satellite imagery is not ‘terrain corrected’ in the data processing. In short, the imagery needs to be processed in a way to incorporate the altitude of where the cities are located. Once this processing is applied, the city lights will not move anymore, staying in the same location.

For further reference, here is the status of each of the fires described above, as of 22 July 2016.

Beaver Creek Fire: 25,491 acres burned, still active, 5% contained as of 22 July 2016.

Cold Springs Fire: 528 acres burned, no longer active, 100% contained as of 14 July 2016.

Hayden Pass Fire: 16,489 acres burned, still active, 55% contained as of 22 July 2016.

Source: ‘inciweb.nwcg.gov’

19 June 2016-Present: Beaver Creek Fire, Jackson County, Colorado

The Beaver Creek Fire started in northwestern Jackson County in Northern Colorado on 19 June 2016 (Figure 1) and presently is still an active fire. The cause of the fire is still unknown. As of 7 July 2016 the fire has burned approximately 13,642 acres and 5% of the fire perimeter is contained (inciweb.nwcg.gov/incident/4797). For National Weather Service (NWS) forecasters to monitor fires such as the Beaver Creek Fire not only during the daytime but during the night-time, Near-Constant Contrast (NCC) imagery can be utilized.

061916_0911Z

Figure 1: An NCC image on 19 June 2016 @ 0911Z, highlighting the location of Jackson County in Northern Colorado. This day was when the fire first initiated. In the right-hand corner of the image, is the corresponding moon phase, showing the fire first started near the full moon stage of the lunar cycle.

In the link below, an animation of the fire spread can be seen in Jackson County with intermittent white circles highlighting the location of the fire. The utility of NCC imagery is evident as NWS forecasters have the ability to monitor erratic fire spread during the night-time hours (e.g., Figure 2), bringing additional satellite imagery that is beneficial for NWS fire weather operations.

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=visitview/custom/NCC_Beaver_Creek_Fire/

Further NCC images of the Beaver Creek Fire will be collected as the present Beaver Creek fire spread evolves.

062816_0943Z

Figure 2: The magnitude of fire spread denoted by the white circle from a NCC image taken on 28 June 2016 @ 0943Z.

NUCAPS, Part Two: Field Campaign and Observations

Currently at the Cooperative Institute for Research in the Atmosphere (CIRA), there has been a field campaign underway comparing satellite temperature and moisture soundings (also known as the NOAA Unique Combined Atmospheric Processing System, NUCAPS) to RAwinsonde OBservation (RAOB) soundings along the Colorado Front Range (Figure 1). The focus of the field campaign is to analyze NUCAPS soundings in a pre-convective environment, examine how they compare to surface-based observations (i.e., RAOB) and how NUCAPS soundings are beneficial for National Weather Service (NWS) forecasters.

WP_20160519_14_05_19_Pro

Figure 1: CIRA personnel setting up a RAOB launch site located along the Colorado Front Range. Notice the balloon in the middle of the figure is accompanied by a radiosonde (not shown) that collects an atmospheric profile of environmental data (i.e., temperature, dew-point, wind and pressure) starting from the surface to the upper levels of the atmosphere.

Only a certain percentage of NWS Weather Forecast Offices (WFO) produce RAOB observations. From RAOB observations, this subset of WFO’s are able to analyze the current state of the atmosphere wherein they’re able to formulate the proper forecast for their WFO. In contrast, WFO’s that do not produce RAOB observations have to rely on either forecast models (e.g., HRRR, NAM, GFS soundings) or RAOB observations produced from nearby WFO’s to help them assess the current state of the atmosphere.

This is where NUCAPS soundings come into play and can help assist WFO’s that do not produce RAOB observations. NUCAPS soundings come in a series of swaths from the Suomi-National Polar-orbiting Partnership (Suomi-NPP) satellite overpasses that occur once during the early morning (local time) and once in the afternoon (local time). The NUCAPS soundings are approximately 50 kilometers apart (~31 miles) from one another. Figure 2 below shows the NUCAPS soundings (green) compared to where CIRA produced a RAOB sounding (red) in Colorado on 19 May 2016 at 20Z.

Counties_combined

Figure 2: Highlights several NUCAPS sounding (green) that are found across the state of Colorado. It is also important to see where the nearest NUCAPS sounding is located relative to the produced RAOB sounding by CIRA (red) on 19 May 2016 at 20Z.  

The two soundings, the RAOB sounding produced by CIRA and the nearest NUCAPS sounding are then compared to show the similarities and differences. Note the distance between the two soundings of interest is less than 50 kilometers apart and can be visually seen in Figure 2. Additionally, NUCAPS soundings are ‘volumetric’ as compared to a ‘point’ sounding provided by RAOB observations.

Figure 3 below shows the comparison between the RAOB observation (left) and NUCAPS sounding (right). As previously mentioned, the date of comparison is 19 May 2016 at approximately 20Z. Both soundings show the pressure in hectopascals (hPa) on the vertical axis and the temperature (red line) and dew-point temperature (green line) in degree Celsius along the horizontal axis.

combined_05_19_16

Figure 3: The comparison of the RAOB observation (left) and NUCAPS sounding (right) along the Colorado Front Range on 19 May 2016 at 20Z. 

One can see that the two soundings look similar to one another from the surface (~850 hPa) to the upper levels of the atmosphere (~200 hPa) inferring the satellite retrieval and the surface-based observation are in relative agreement on the current state of the atmosphere. Although it is only a small sample size, such agreement could increase confidence in the utility of NUCAPS for NWS forecasters in their daily operations.

However, an apparent difference to note is NUCAPS expresses a coarser vertical resolution than the RAOB observation. This can be seen in Figure 3 by the ‘smoother’ temperature/dew-point lines seen by NUCAPS in contrast to the rigid lines seen by the RAOB observation (not so smooth temperature/dew-point lines).The RAOB observation is also able to see an upper-level inversion near 500 hPa which NUCAPS does not clearly see due to the coarser vertical resolution. Furthermore, although it is not apparent in Figure 3, NUCAPS is limited in accurately seeing the lower-levels of the atmosphere due to the influence from clouds.

Now with the the limitations stated above, how can NUCAPS be modified to best represent atmospheric profiles similar to RAOB observations? First of all, NUCAPS data can be seen visually (e.g., Figure 3) in AWIPS-II, a forecasting and software display package that NWS forecasters use in their day-to-day operations. Secondly, within the AWIPS-II interface, NUCAPS data has a feature where it can be modified manually by the NWS forecaster to better represent the current state of the atmosphere. For the interested reader, this modification will be highlighted in the next blog update.

Fort McMurray Wildfires and Near-Constant Contrast (NCC) Imagery

The Fort McMurray Wildfires started in the city of Fort McMurray, located in the northeastern part of Alberta, a province of Canada. The wildfires started 01 May 2016 and are still currently burning. The wildfires have burned over 1,200,000 plus acres of land and has reached into parts of western Saskatchewan. Over 2,400 plus homes and businesses were lost within the Fort McMurray area (The Globe and Mail and Weather.com). Estimated insured losses from the fires are between 3-7 billion U.S. dollars (Insurance Journal). According to the Washington Post, the wildfires have produced an estimated 85 million tons of carbon dioxide equivalent emissions as of 20 May 2016.

The sequence of the estimated fire perimeters can be shown through the animation below.

fortmac-may17

A subset of operations for National Weather Service (NWS) is focused on forecasting and monitoring wildfire potential and growth (i.e., Fort McMurray wildfires) during the daytime and nighttime. NWS forecasters can monitor wildfires utilizing visible imagery from satellite during the day however, monitoring wildfires during the night can become cumbersome. To assist NWS forecasters during the nighttime, polar-orbiting satellite data from the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite is considered. On-board the Suomi-NPP is the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument that consists of 22 spectral channels, where one of those channels is the Day-Night Band (DNB).

The DNB provides the capability of observing night-time light emissions (e.g. wildfires) and atmospheric features across the globe and monitors the global distribution of clouds (Miller et al 2014). DNB has the ability to detect broad ranges of light intensities ranging from full sunlight during the daytime to faint atmospheric glow on moonless nights (8 orders of magnitude in radiance space). The broad range leads to difficulties in displaying an images without losing detail at either end of the radiance scale. Near-Constant Contrast (NCC) was developed to mitigate the enhancement issues utilizing a sun/moon model to convert DNB radiance values into a reflectance-like value. In short, the NCC is a derived product of the DNB.

Currently, NCC is available for NWS forecasters in the Advanced Weather Interactive Processing System-II (AWIPS-II); a weather forecasting display and analysis package. To access NCC data in AWIPS-II refer to Figure 1 below.

WP_20160520_14_10_57_Pro

Figure 1: Screenshot of the AWIPS-II interface, where users can access NCC data under the ‘Satellite’ tab.

For the interested reader, a link to the Quick Guide for Imagery Enhancement involving NCC in AWIPS-II can be found below.

ftp://rammftp.cira.colostate.edu/torres/Quick_Guide/VIIRS_NCC_Quick_Guide_Dec2015.pdf

Furthermore, to highlight the capabilities of the NCC we will take a closer look at the Fort McMurray wildfires. Displayed screenshots of NCC imagery were taken before the fire and during the fire shown in Figures 2-4. Emitted light sources from the active fires, city lights, gas flares, and also atmospheric features such as clouds and smoke are seen in the satellite imagery.

NCC Imagery Before the Fire

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Figure 2: NCC imagery was taken a week in a half before the initiation of the Fort McMurray fires at 1013Z, 18 April 2016. Note the city lights of Fort McMurray, the gas flares from Tar Island, and the clouds in the vicinity.

 

NCC Imagery of Fort McMurray Wildfire – 17 May at 0930 UTC

Picture3

Figure 3: NCC imagery taken during the Fort McMurray fires at 0930Z, 17 May 2016. One can see the emitted light from the fires and fire perimeter line that is forming.

 

NCC Imagery of Fort McMurray Wildfire – 18 May at 0915 UTC

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Figure 4: NCC imagery taken during the Fort McMurray fires at 0915Z, 18 May 2016. Note the active fires along the fire perimeter line and the large amount of smoke produced.

 

Lastly, a comparison was conducted between the estimated fire perimeter and NCC imagery for 17 May 2016 (Figure 5). Both images show relatively similar shape and size of the fire perimeter line. It is important to note the time stamps for each image in the comparison is offset by a few hours, however, the polygonal shape of the fire perimeter is still apparent.

Picture9Figure 5: The comparison between the estimated fire perimeter and NCC imagery for 17 May 2016. A polygonal shape of the active fire line perimeter is evident in both images.

NUCAPS, Part One: Introduction

By Jorel Torres

The National Weather Service (NWS) has over 120 WFO (Weather Forecast Office) locations across the CONtinental United States (CONUS) where only a certain percentage of these offices produce and display RAwinsonde OBservations (RAOB). RAOB’s are important real-time observations for NWS forecasters where RAOB’s display an atmospheric stability profile, producing atmospheric measurements from the surface to the upper levels of the troposphere. RAOB’s not only assess the stability of the atmosphere, but can show levels of mixing (moist and dry air), the convective available potential energy (CAPE) needed for thunderstorm potential, determine precipitation type and can display the vertical wind profile; key parameters that are helpful for weather forecasting. The issue that comes into play is the lack of RAOB observations around the CONUS, which can become problematic for NWS forecasters. If a WFO does not produce any RAOB observations, forecasters at that WFO might rely on observations taken from WFO’s nearby, most in which are tens or hundreds of miles away. Consequently, there is a high probability the observation profile they utilize from a nearby WFO will be different than what would be seen at their own WFO; potentially causing inaccurate interpretation of real-time observations.

To help assist RAOB observations are NUCAPS (NOAA Unique Combined Atmospheric Processing System) satellite observations which combine the CrIS (Cross-Track Infrared Sounder) and ATMS (Advanced Technology Microwave Sounder) instruments on-board the Suomi-NPP satellite producing vertical temperature and moisture profiles of the atmosphere. Wherein NUCAPS and RAOB observations can be compared and displayed operationally for NWS forecasters in the Advanced Weather Interactive Processing System (AWIPS-II). Furthermore, NUCAPS can be seen as a complement to RAOB observations. RAOB observations occur every day at 00Z and 12Z only, while NUCAPS produces observations in between those hours, from 00Z-12Z and 12Z-00Z. The combination of NUCAPS and RAOB observations can further highlight the diurnal change in the vertical profile of the atmosphere since the atmosphere is always changing, and could benefit forecasters in severe weather nowcasting and the storm warning process. Additionally, NUCAPS has more observations to choose from compared to RAOB’s. Each NUCAPS observation (i.e., sounding) is approximately 50 kilometers (~30 miles) apart and are beneficial to WFO’s that do not produce RAOB observations. The differentiation between the number of RAOB observations to NUCAPS observations over the CONUS are shown in Figures 1A and 1B below.

NUCAPSI

Figure 1A: The display of NUCAPS observations across the CONUS shown in the AWIPS-II interface on 01 April 2016, @ 0846Z. Each filled circle is a NUCAPS sounding and the color dictates if the data is of good (green), ok (yellow), or bad (red) quality, respectively. It is important to note how many more NUCAPS observations there are in comparison to RAOB observations.

RAOB_obs

Figure 1B: The filled in blue circles indicate the locations where RAOB observations take place across the CONUS every day at 00Z and 12Z respectively. Notice how far away RAOB observations are from each other in comparison to NUCAPS observations.

NUCAPS expresses benefits for forecasters at WFO’s, however NUCAPS also has a few caveats. NUCAPS has trouble producing quality data when clouds are present in the atmosphere. Due to this limitation, a percentage of observations need to be modified by the forecaster, especially in the lower levels of the atmosphere where most clouds are present. Currently, this modification can be solved manually through the AWIPS-II interface, although it is cumbersome and can take too much time out of the forecaster’s daily operations. However, there are research studies that are ongoing that could help alleviate the manual modification and are working toward developing an automative process. One research study to note, is what is occurring at the Cooperative Institute for Research in the Atmosphere (CIRA) located in Fort Collins, CO, this spring and summer. CIRA is having a field campaign launching RAOB’s and comparing them to NUCAPS observations along the Colorado Front Range. The comparison of observations will be one step in the right direction in assessing what modifications are needed to produce better NUCAPS retrievals from satellite, which in turn, will increase forecaster’s confidence in its utility for weather forecasting. The field campaign starts in early May and will continue throughout the spring and summer of this year, 2016. For interested readers, look forward to future blog updates regarding the NUCAPS field experiment.

Synthetic Imagery from the NAM Alaska Nest 4 km

By Jorel Torres, Dan Bikos and Lewis Grasso

A majority of National Weather Service (NWS) training is focused on satellite products for the CONtinental United States (CONUS). However, how can satellite products help NWS forecasters with satellite interpretation in Off CONUS locations such as Alaska? One goal is to use synthetic satellite imagery from the operational NAM Alaska Nest to aid in the identification of cloud liquid water in the winter, at times referred to as ‘Black Fog’.

An example of a satellite product is one in which the difference between two channels is employed. One way to identify cloud liquid water is to calculate the difference between brightness temperatures (Tb) at 10.7 and 3.9 um; that is, the fog product displays values of Tb(10.7 µm)-Tb(3.9 µm). In the fog product, cloud liquid water is indicated by positive values while ice clouds are indicated by negative values. Furthermore, liquid clouds and ice clouds can be differentiated optimally with no solar reflection. In order to eliminate solar reflection from synthetic imagery that is made from a 60-hour forecast from the NAM Alaska Nest, the time of any synthetic image is set to a value of 06 UTC. With the information above, one can use the fog product to aid in the identification of black fog in either observed or synthetic satellite imagery during the winter in Alaska.

nam_alaska_synthetic_band13minus7_20160201080000

Figure 1: Synthetic Fog Product from the 0000 UTC, 31 January 2016 model run, 32-hour forecast valid at 0800 UTC, 01 February 2016. In this image liquid water clouds are shown as blue and ice clouds are shown in black. Values range from -9.8 to 9.8 K. The bold oval indicates a region that contains liquid water clouds in a low-lying area.

Figure 1 shows the Synthetic Fog Product displayed over Alaska from the 0000 UTC, 31 January 2016 model run, 32-hour forecast valid at 0800 UTC, 01 February 2016. As seen in the figure, liquid water clouds (blue) existed over both the Gulf of Alaska and over the state of Alaska. Black fog would be that portion of the liquid water field over the state of Alaska that is confined to low-lying areas and valleys (as an example, note region bounded by the white ellipse in Figure 1, northeast of Anchorage, AK). Although valley fog may be challenging to identify in one satellite image, an animation of the scene shown in Figure 1 can ease the challenge (please click for a 31 January- 03 February 2016 animation). Note the model updates to the latest run between 0800 and 0900 UTC (for each day) in the animation.

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=visitview/custom/NAM_Synthetic_Alaska_Nest_10_minus_3_Fog_Product/

During the time period between 0000-1200 UTC in the animation there is a specific region that contains a liquid cloud layer as shown by the white ellipse northeast of Anchorage. The liquid cloud layer is stagnant and can be inferred that the layer is contained in a valley, demonstrating similar characteristics of fog or low stratus clouds.

In Alaska and the CONUS, detecting fog and low-lying clouds are of high priority among the Aviation Weather Centers (AWC), where they, in turn, inquire assistance from local NWS forecasters. Both AWC and NWC forecasters have many options to use to identify fog and low-lying clouds; however, products must demonstrate value. A way to assess the value of the Synthetic Fog Product is to compare the synthetic imagery to the observations. If observed imagery supports synthetic imagery, then forecasters can have confidence in the forecast of the Synthetic Fog Product.

Synthetic imagery is typically compared to observed imagery from the Geostationary Operational Environmental Satellite (GOES)-15. However since Alaska is the region of interest, this comparison has one disadvantage. GOES-15 imagery contains distortions of data at higher latitudes. Consequently, forecasters can take advantage of the utility of polar-orbiting satellites. A few polar-orbiting satellites have been launched in recent years, ranging from the National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-18, NOAA-19) to the Suomi-National Polar-orbiting Partnership (Suomi-NPP) satellite. Suomi-NPP is a more comprehensive satellite compared to other polar-orbiting satellites, as it contains more spectral bands (22), finer resolution and enhanced capabilities.

An instrument on board Suomi-NPP is the Visible Infrared Imaging Radiometer Suite (VIIRS), which for illustrative purposes, is used to evaluate synthetic imagery. Similarities and differences between VIIRS data (Figure 2A) and synthetic imagery (Figure 2B) are important to identify. For illustration purposes, images are chosen near 1300 UTC, 01 February 2016. When viewing the regions within the three white ellipses in Figure 2, liquid clouds are seen approximately in the same locations in both the observations and the synthetic imagery. However, the observations shows smaller (larger) regions of liquid clouds in northern (southern) Alaska when compared to the synthetic imagery. The location of liquid clouds could vary due to the off-set time comparison: observations taken at 1245 UTC compared to synthetic imagery at 1300 UTC. Nevertheless, the synthetic imagery shows the capability of detecting liquid clouds while being in relative agreement with observations. As a result, forecasters can have more confidence in the utility of forecasted synthetic imagery from the operational NAM Alaska Nest.

A)

N_Alaska_1245Z_full_view4

B)

nam_alaska_synthetic_band13minus7_20160201130000

Figure 2: Fog Product near 1300 UTC, 01 February 2016 from (A) Observed VIIRS data and (B) and corresponding forecast time from the NAM Alaska Nest synthetic imagery. The regions bounded by the three ellipses are used to compare and display locations of liquid water clouds in and around Alaska. 

For the interested reader, additional VIIRS imagery in the Arctic and real-time synthetic imagery from the NAM Alaska Nest can be seen via the links below.

VIIRS imagery in the Arctic

http://rammb.cira.colostate.edu/projects/alaska/blog/

Synthetic Imagery from the NAM Alaska Nest

http://rammb.cira.colostate.edu/ramsdis/online/goes-r_proving_ground.asp#Synthetic_Imagery_from_the_NAM_Alaska_Nest

Suomi-NPP, VIIRS, Day-Night Band (DNB): Moon Phases

By Jorel Torres and Erin Dagg

The Suomi-National Polar-Orbiting Partnership (Suomi-NPP) satellite is a prototype for the next generation of Joint Polar-Orbiting Satellite System (JPSS) series of satellites with JPSS-1 scheduled to launch in early 2017.  One instrument onboard the Suomi-NPP is the Visible Infrared Imaging Radiometer Suite (VIIRS) http://www.jpss.noaa.gov/viirs.html. It has 22 spectral bands that have a variety of applications, many of which will improve weather, flooding, and storm forecasting capabilities and allow for monitoring of ocean nutrient, aerosols, vegetation health, cloud microphysics and cloud top properties, cloud cover, snow, and fire detection.

One unique band found on VIIRS is the 0.7 µm Day-Night Band (DNB).  A concise description of how VIIRS views through the DNB can be found here “Earth at Night – the Black Marble” (http://www.jpss.noaa.gov/pdf/earth_at_night_2012.pdf). Note that the Black Marble image was processed to remove clouds, though for forecasting applications it is necessary to view clouds and their patterns.  The illumination of clouds at night is a function of the moon phase and the angle of the moon during subsequent Suomi-NPP overpasses.

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Figure 1: DNB imagery identifying clouds, city lights, aurora, and gas flares at 1003 UTC 19 January, 2016.

The animation link below shows VIIRS DNB imagery throughout the latest lunar cycle, from 9  January – 8 February 2016 (new moon to new moon). The approximate moon phase at the time of each image is displayed in the lower-right corner for reference.

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=visitview/custom/DNB_images/Moon_Phases_DNB

Prior to the First Quarter moon phase (between 9-17 January), you will notice that the imagery appears washed out. There is little distinction between individual cities while cloud patterns are hard to discern. Approaching the full moon phase, the imagery appears brighter overall, with noticeable texture differences between cities and clouds. The large synoptic-scale systems moving into and eventually through the contiguous United States (CONUS) appear to have sharper edges and increased contrast with the background (i.e. 23 January, 2016, cyclone depicted along the California coastline).

Another feature that stands out is the elongated bright stream of light across southern Canada. This is the aurora, which is produced when charged particles emitted from the sun, during a solar flare, are able to penetrate the Earth’s magnetic field, colliding and interacting with Earth’s atoms and air molecules.

Throughout the time-lapse there are variable light signatures seen in western North Dakota and in the Gulf of Mexico. These emitted light sources are a product of gas flaring by oil and gas industries and offshore rigs, respectively. The aurora, clouds, gas flares, city lights are identified in Figure 1, above, while the offshore rigs are specified below in Figure 2.

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Figure 2: DNB imagery at 1014 UTC 29 January, 2016, showing offshore gas flares in the Gulf of Mexico. 

Furthermore, there is an increased saturation of the city lights before the First Quarter and after the Last Quarter moon phase due to the decreased amount of lunar reflection. It is important to note that the gas flares, auroras, lightning and city lights provide their own light source, and often appear brighter in imagery during this time period.

Lightning is also seen by satellite displayed as short streaks of light (Figure 3). The satellite temporal resolution (each scan) is every 1.8 seconds and typical flash events are near ~10 milliseconds. Therefore, the offset timescales between the flash duration (with an influence of light diffusion, i.e., the optical scatter within the cloud) and the satellite temporal resolution produce the streaks of light (Miller et al 2013).

picVII_labels

Figure 3: DNB imagery at 1117 UTC 15 January, 2016 showing horizontal streaks of lightning in the Gulf of Mexico. 

Cold Air Aloft Product: Arctic

By: Jorel Torres and Jack Dostalek

Introduction

With all of the attention given to the pending winter storm along the East Coast, this blog may seem a bit out of place.  Nevertheless, high-profile blizzards aren’t the only dangerous cold-season weather phenomena of interest to forecasters.

During the winter months, especially at high latitudes, air temperatures at altitudes used by passenger aircraft can get cold enough to cause jet fuel to gel (known as “cold air aloft” situations by the National Weather Service).  The air temperature at which the gelling of jet fuel becomes a concern is typically considered to be -65°C.  The knowledge of the location of pockets of air this cold is of importance to weather forecasters.  Model forecasts are used, but confirmation from observations is beneficial.  Radiosonde measurements and aircraft observations are at times available, but the temporal and spatial sampling is too coarse to accurately delineate the cold air pockets.  Polar-orbiting satellites can provide vertical temperature profiles with higher temporal and spatial resolution and thus help to monitor the atmosphere for air which is dangerously cold for aircraft travel.

Example from 9 January 2015

On 9 January 2015, the Center Weather Service Unit in Anchorage, AK issued the following Meteorological Impact Statement concerning the existence of cold air aloft near Barrow, AK:

Picture2

The 1200 UTC Barrow radiosonde does indicate a thin layer of air with temperature below -65°C:

barrow

The S-NPP satellite passed over the region around 1220 UTC, allowing for a good matchup to the Barrow radiosonde.  Two different satellite retrieval algorithms will be compared to the radiosonde data.  First is the MIRS (Microwave Integrated Retrieval System), NESDIS’ current operational microwave-only retrieval algorithm, run on both the ATMS (Advanced Technology Microwave Sounder) and the AMSU (Advanced Microwave Sounding Unit).  The second is NUCAPS (NOAA Unique Combined Atmosphere Product System), which is a combination microwave/infrared retrieval algorithm which uses the ATMS and the CrIS (Cross-track Infrared Sounder) aboard the S-NPP satellite.

The map below shows the positions of Barrow and the MIRS and NUCAPS retrievals, as well as a GFS forecast sounding (6-hr forecast of the 0600 UTC run of 9 January 2015).

map

The full sounding (upper panel below) shows good agreement among all of the temperature profiles, with the low-level inversion somewhat higher in the MIRS data.  The cold air aloft occurred around 200 hPa and the lower panel shows that altitude more closely.  All temperature profiles reach -65°C or colder near 200 hPa, but the exact location and extent of the cold layer varies: the MIRS is at the lowest altitude followed by the Barrow radiosonde and the NUCAPS retrieval, with the GFS temperature minimum at the highest level.  The MIRS and NUCAPS profiles also show a broader layer of cold air.  This is expected, as satellite retrievals are usually quite smooth in the vertical.

compare closeup

All of the profiles were able to capture the cold air aloft event of 9 January 2015.  The determination of the overall performance of the satellite retrievals and model forecasts, however, cannot be determined without many more cases.  Of additional interest would be a study documenting the dynamical nature of these regions of wintertime cold air aloft.

Display of Near Real-Time Data

As the study of the utility of the MIRS and NUCAPS retrievals continues, a web page has been developed at CIRA to display in near real-time the existence of cold air aloft over the Artic (http://rammb.cira.colostate.edu/ramsdis/online/cold_air_aloft.asp).  The page currently displays only AMSU/MIRS data from NOAA-18, NOAA-19, MetOp-A, MetOp-B, and DMSP-18. ATMS/MIRS and NUCAPS data are not yet ready for near-real time display.

The page currently contains two links, one to an Arctic view and the second to a Bering Sea regional view.  The color scheme denotes the coldest temperature at each footprint of a satellite swath:

TABLE1

If the layer of cold air exists below FL450 (Flight Level 450 is approximately 45,000 ft.) a ‘+’ is displayed.  Additionally on the regional view, the extent of the layer of cold air, measured in units of flight level is also displayed.  The upper figure below shows the swath view of a MetOp-A pass on 19 February 2015, with most of the cold air aloft over Russia and Greenland.  The lower panel is an example of the Bering Strait view from 22 January 2015, a day when cold air aloft was reported by an aircraft at FL360 near the International Dateline and latitude 55°N.

arctic_20150219150400 new

Cold Air Aloft (1/21/16)

There is currently an extensive area of cold air aloft over the Arctic, and prompted the issuance of the following Meteorological Impact Statement from Anchorage yesterday (Jan. 21, 2016):

Picture3

The following image from CIRA’s cold air aloft web site gives an idea as to the extent of the cold air.

Image7-JT

Next Steps

Additional retrievals, particularly those from ATMS/MIRS and NUCAPS will be added to the web displays.  In addition, other regional loops will be added as needed.  Finally, CIRA is collaborating with SPoRT, CIMSS, GINA, and the National Weather Service to supply satellite information on the location of areas of cold air aloft to forecasters via AWIPS-II.

DNB Percent Solar Reflectance – Colorado

The image shows the state of Colorado, showing the Day/Night Band (DNB) of the Percent Solar Reflectance @ 2017-Z, the day of 12-27-15.

What is noticeable is the different textures between the mid-to-high level clouds (larger white swaths) and the snow on the ground (distinctly shown by the topography of the Rocky Mountains.

second

 

The DNB has the capability of showing the intricacies of the snow on the ground and clouds by utilizing a lunar reflectance model from the VIIRS on the Suomi-NPP. Furthermore, the DNB has many meteorological applications for forecasting after sunset, which are vital for improving present forecasting applications/techniques.

 

 

CIRA Layer Precipitable Water product – snow cover issue

The CIRA layer precipitable water product from 3-4 December 2015 is shown below:

Blended_LPW

 

Note the black region across South Dakota, Minnesota and portions of Nebraska / Iowa.  There are also black regions in portions of the Rockies and Canada.  Why is data missing over these regions?

The visible imagery from 1745 UTC on 4 December:

visible_1745

Depicts what appears to be a snow field over South Dakota, Minnesota and portions of Iowa and Nebraska.  Snow fields will reveal rivers and lakes, unlike a cloud field such as that over Indiana / Ohio / Illinois.  Another method to discriminate snow vs cloudy regions is the CIRA shortwave albedo (also known as low cloud) product:

swalbedo_1745This product consists of the 10.7 and 3.9 micrometer channels.  Low clouds show up as white /bright while snow cover shows up as black / dark since snow cover has low reflectivity in the 3.9 micrometer channel.

The Microwave Integrated Retrieval System (MIRS) uses passive microwave microwave radiances from polar orbiting satellites to solve for the temperature and moisture profile in the atmosphere.  Snow cover on the ground changes the microwave land surface emissivity, which determines how much microwave radiation is received by the sensor.  As snow is complex with varying depth, grain size and water content, this impact is complex.  In order to eliminate erroneous retrievals over snow and ice covered surfaces, these regions are flagged for no retrieval in the MIRS system and appear black in the CIRA blended product.  Snow emissivity modeling work continues, and improved atmospheric retrievals over snow and ice could be realized in the future.

Further details on and examples of the CIRA blended Layer Precipitable Water Vapor product is available in the article “A Multisensor, Blended, Layered Water Vapor Product for Weather Analysis and Forecasting”, available at http://www.nwas.org/jom/abstracts/2015/2015-JOM5/abstract.php.

Real-time imagery for the CIRA layer precipitable water product can be found here:

http://cat.cira.colostate.edu/sport/layered/blended/lpw.htm

 

4 June 2015 GOES 1-minute visible imagery and time lapse video

This blog entry consists of a youtube video:

The time lapse video discussed above can be seen in its entirety below (courtesy Scott Longmore, CIRA):

GOES 1-minute imagery on 2 June 2015 over North Dakota

This blog entry consists of a youtube video:

GOES visible imagery analysis for 26 April 2015 tornadic storm in Texas

This blog entry consists of a youtube video:

To view only the GOES visible loop animation (without recorded audio), see:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/26apr15_vis&number_of_images_to_display=52

2 April 2015 severe thunderstorm event

On 2 April 2015, severe thunderstorms were forecast by the SPC as summarized in their Day 1 convective outlook graphic:

20150402 1300 UTC Day 1 Outlook Graphic

We will focus on the western edge of the enhanced outlook area which is roughly southeast Kansas, northwest Oklahoma, southwest Missouri and northwest Arkansas.

One of the key ingredients for this severe weather setup was an upper-level jet.  The GOES water vapor imagery overlaid with the RAP 250 mb isotachs for the late afternoon / early evening hours depicts this upper-level jet:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/2apr15_wv&number_of_images_to_display=15

The core of the jet is across southern Colorado, and quickly moves eastward across Kansas towards the threat area in southeast Kansas / northeast Oklahoma.  There appears to be a good correlation between the arrival of the upper-level jet streak and the timing of convective initiation in southeast Kansas.

GOES visible imagery along with RAP 250 mb isotachs:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/2apr15_vis&number_of_images_to_display=13

Shows considerable cirrus at the leading edge of the jet streak across Kansas and in fact convective initiation occurs along a low-level convergence boundary around that time.  The low-level convergence boundary can be seen in southeast Kansas extending eastwards into southwest Missouri where convection occurred earlier and moved off to the east.

A closer inspection of the visible imagery at 20:15 UTC:

vis2015shows the different boundaries and air-masses.  Unstable cloud streets can be seen along and south of the segment where convective initiation occurs.  Stable cloud streets exist further north and northeast of the boundary where cooler temperatures exist.  The thunderstorms developed at the northern edge of the lid associated with an elevated mixed layer profile.

 

Typhoon Hagupit

By Kate Musgrave

Typhoon Hagupit in the northern West Pacific basin underwent intensification until reaching a peak intensity of 155 kt at 0000 UTC on 4 Dec 2014 (intensities obtained from Joint Typhoon Warning Center (JTWC)). This was a 65 kt increase over the intensity 24 hours previous (90 kt at 0000 UTC on 3 Dec 2014) and marked Typhoon Hagupit as a supertyphoon. VIIRS overpasses were available both during the intensification and at peak intensity in Typhoon Hagupit (all images provided by Dan Lindsey, NOAA/NESDIS). Below is shown the VIIRS visible image from Typhoon Hagupit at 0440 UTC on 4 Dec 2014, at peak intensity.

viirs_hagupit_Iband1_4dec14_0440Z_out_ann

Zooming in further, displayed below is the infrared from the same VIIRS overpass. The eye is clear down to the ocean surface and both the eye and eyewall display highly symmetric features.

viirs_hagupit_Iband5_4dec14_0440Z_ann

Previously, a VIIRS overpass occurred at 1555 UTC on 3 Dec, during the period when Typhoon Hagupit was rapidly intensifying (the intensity at 1200 UTC on 3 Dec was 100 kt). Shown below is the infrared from that earlier VIIRS overpass:

viirs_hagupit_Iband5_3dec14_1555Z_ann

Notably, the features in the eye and eyewall at this time are more asymmetric, with the interface between the eye and eyewall appearing more ragged and less circular than at peak intensity. The eye has also not yet cleared out to the ocean surface. The overall shape of the coldest cloud tops at this time bears a particularly strong resemblance to the common symbol used for representing tropical cyclones, as illustrated in the bottom right corner of the figure.

GOES-14 SRSOR for May 20, 2014

This blog entry consists of a youtube video (8 minutes in length):

http://youtu.be/36lR8Y7xvOw

After viewing the video, compare the GOES-14 RSO visible loop that would’ve been as seen on AWIPS:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/20may14_rso&number_of_images_to_display=20

with the SRSOR loop over the same time period (1940 – 2040 UTC) – this is a 194 frame loop so be patient for it to load:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/20may14_srso&number_of_images_to_display=194&loop_speed_ms=80

Use Ctrl + and Ctrl – to zoom in and out respectively.

What features can you see in the SRSOR (1-minute) animation that you cannot see in the RSO animation?  Make a list and compare with others in your office.  These are the benefits of high temporal resolution that will be available with GOES-R

SRSOR on 21 May 2014

This blog entry is available in 2 formats:

1) Youtube video (25 minutes):  http://youtu.be/H6jMoT3sGiw

2) Web format (below):

GOES Super Rapid Scan Operations for Research (SRSOR) 1-minute imagery was available for the severe weather event of 21 May 2014 that affected Colorado and Wyoming:

http://www.spc.noaa.gov/climo/reports/140521_rpts.html

At the surface, southeast winds advected moisture into the Denver area.  The following is the 1900 UTC RTMA surface dewpoint and wind:

Surface dewpoints were in the low 50s in the Denver area, meanwhile west of the moist axis in the mountains a much drier air mass was in place. Southwest winds there setup a zone of convergence west of Denver.  Convective initiation occurs along this boundary southwest of Denver:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/loop1&number_of_images_to_display=60&loop_speed_ms=80

Zoom in using Ctrl +, zoom out using Ctrl – and reset to default zoom with Ctrl 0

You may increase the speed of the loop with the animation speed slider.

Analyze the low-level clouds across the eastern Plains of Colorado and southeast Wyoming.  What are some of the differences?  The low-level clouds along and east of Denver are characterized by unstable cloud streets, parallel to the low level flow.  The low-level clouds in southeast Wyoming (with the exception of extreme southeast WY) are characterized by stable wave clouds, perpendicular to the winds at inversion top level.  West of the wave clouds, convection is developing over the Laramie range where convective temperatures are much lower at higher elevations.

Turning our attention back to Colorado, the next loop of visible SRSOR imagery,

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/loop2&number_of_images_to_display=25

depicts continued growth of the storm southwest of Denver, along with other smaller storms in the vicinity.  We observe low-level cloud streets quickly moving toward the primary storm; we therefore expect this storm to continue to grow in the near future.  Updrafts that try to develop south of the primary storm move toward the clear region with a lack of cloud streets.  This clear region is not as unstable so we would not expect additional thunderstorm activity in the clear area south of the storm in the near future.

The GOES SRSOR visible imagery for the next time period,

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/loop3&number_of_images_to_display=69&loop_speed_ms=80

shows that the storm near Denver has gone through intensification with a crisp / distinct edged anvil on the upshear (southwest) side of the storm.  The line of clouds extending southwest of the Denver storm is the flanking line.  The initial tornado report was at 2005 UTC – see the SPC storm report link above for the additional reports.  Observe the rapid clearing (denoted by the red box in the image below) that took place west of the flanking line between 1957 and 2046 UTC:

At 1957 the primary storm was not as intense, and there were other small storms nearby.  By 2046 the primary storm was much more intense, and compensating subsidence in the vicinity of the stronger updraft clears out the region where most of the subsidence is occurring (on the southwest flank of the storm).  Also, there is a transition from numerous smaller storms to a dominant storm as seen in the WSR-88D KFTG 0.5 degree tilt reflectivity between 2028 – 2156 UTC:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/kftg_z&number_of_images_to_display=20&loop_speed_ms=80

Early in the loop (2035 – 2130 UTC), west of the flanking line, an invigorated field of cumulus develops above the Rear Flank Downdraft (RFD).  This satellite signature has been observed with mature severe storms, but the mechanism for their development is a topic open for research.  Here is a schematic that depicts the satellite signatures observed on this storm:

Meanwhile, east of the flanking line in the region indicated by the red oval on this graphic

we observe inflow feeder clouds.  These inflow feeder clouds are a good indication of a severe storm.  For more information on this feature see:

Mazur, Rebecca J., John F. Weaver, Thomas H. Vonder Haar, 2009: A Preliminary Statistical Study of Correlations between Inflow Feeder Clouds, Supercell or Multicell Thunderstorms, and Severe Weather. Wea. Forecasting, 24, 921–934.

Note the region circled in yellow on the graphic above.  You will need to zoom in to the looping SRSOR visible imagery to see this.  Note that this feature is not there before the storm – it only appears in the wake of the storm.  Also, this feature does not move.  This is a hail swath caused by the storm which produced a significant depth of hail over a large enough area to be seen in the visible channel due to its higher reflectance of sunlight:

http://images.scribblelive.com/2014/5/21/2b632f39-3bf0-4594-b5ee-4e3b7349f205_500.jpg

The SRSOR visible imagery for the next time period,

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/loop4&number_of_images_to_display=57&loop_speed_ms=80

shows the continued evolution of the storm of interest, with indications of storm top divergence (and likely rotation as well), along with an enhanced-V/warm wake at storm top.

How does this compare with the satellite imagery that would’ve been available in real -time on AWIPS in “normal” Rapid Scan Operations (RSO) mode?

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/awips_vis&number_of_images_to_display=8

Keep in mind that the long gap at 2100 UTC is due to a full disk scan.

What can you see in the SRSOR imagery that you cannot see in the RSO imagery?

If you had only seen the RSO imagery, what features do you think you might have missed completely (e.g., the hail swath on the ground in the wake of the storm)?

When GOES-R becomes available, depending on the scan mode chosen, it is possible that 1-minute (and even 30-second) imagery will be available so this type of analysis may be routine when analyzing severe thunderstorm events.

Shifting our attention to southeast Wyoming, notice that much of this area has transitioned from stable wave clouds to unstable cloud streets by this time.  It appears that daytime heating has destabilized this area; however, you can still see stable wave clouds further north, and a boundary exists between these two air masses as depicted here:

What role might this boundary have for potential future convection?

By the end of the loop we observe convective initiation at the western end of this boundary, where it intersects the Laramie range.  The storm either initiated on the boundary or over the high terrain, or some combination of both.  But the key is if storm motion can stay along the boundary, then the storm will likely intensify.  Meanwhile, further east along the boundary, there is a growing field of cumulus that should be monitored for potential convective initiation in the near future.

The SRSOR visible imagery for the next time period,

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/loop5&number_of_images_to_display=64&loop_speed_ms=80

shows the storm over the mountains to be pulsing, going through cycles of slow growth, but not intensifying rapidly.  Further east, along the boundary, it appears that multiple small storms are developing and by the end of the loop merge into a dominant updraft.

Here is the SRSOR visible imagery for the next time period:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/loop6&number_of_images_to_display=67&loop_speed_ms=80

We label the storms of interest:

Storm A weakens rapidly.  Why?  Storm motion is toward the northeast and it does not stay along the convergence boundary but instead moves toward the region where stable wave clouds existed for an extended period.

Storm B (the one we had been monitoring over the mountains) appears to intensify considerably while storm motion has slowed down quite a bit.  The storm is likely along the boundary of interest, and just as importantly, does not have a movement to the northeast where it would meet a quick demise like storm A.  Also, there is a possibility that storm A left an outflow boundary for storm B to intersect.  Although it’s not discernible here, we will analyze the radar data later.  Storm B continues to cycle with updrafts while we see new updrafts grow rapidly.  This tornado was observed at 0115 UTC:

https://www.facebook.com/photo.php?fbid=707790585952357&set=o.211703005523464&type=1&theater

The 0.5 degree tilt reflectivity from the WSR-88D in Cheyenne, WY between 2333 – 0127 UTC:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/21may14/kcys_z&number_of_images_to_display=24

shows that storm B was initially moving northeast, then slowed down considerably (likely along the boundary) and one or two of the earlier updrafts associated with storm A merged with storm B.

The storm furthest to the northwest (Storm C) is also of interest since it shows signs of intensification (overshooting top, storm top divergence, well defined anvil edge on the upshear side) as it is over the high terrain.  The storm can be seen on the radar loop as well  as it approaches Douglas, WY.  This storm is of interest because it is quite far from the radar, so the 0.5 degree tilt reflectivity is looking pretty far up into the storm.

To see other exciting imagery that will be available with GOES-R see:

http://www.goes-r.gov/

May 8 2014 GOES SRSO for Severe Weather

GOES-14 Super Rapid Scan Operations (SRSO) was activated for the severe thunderstorm event of May 8, 2014.

This blog entry consists of a youtube video:

http://youtu.be/oHKCIIA95Oo

For access to real-time GOES SRSO (when available) click here:

http://rammb.cira.colostate.edu/dev/lindsey/loops/

Mesovortex over Lake Ontario from 12 December 2013

A cold air mass was in place over the Great Lakes during 12 December 2013, providing for lake-effect snow across the Great Lakes.  The synthetic imagery from the 4-km NSSL WRF-ARW model initialized 0000 UTC 12 December shows the various lake-effect snowbands (low-level clouds with brightness temperatures that are not that cold relative to high level clouds):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/12dec13_syn_ir_buf&image_width=1020&image_height=900

GOES visible imagery during the morning of 12 December depicts a single band across Lake Ontario:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/12dec13_goes_vis_ontario&image_width=1020&image_height=900

Note the appearance of a mesoscale vortex along the band on the eastern end of Lake Ontario, soon after it appears it moves over land and loses its organization.

The mesoscale vortex can be seen in the 0.5 degree radar reflectivity data from the KTYX WSR-88D (loop courtesy of College of Dupage NEXLAB):

10 June 2013 Colorado Dry Microburst

By Ken Pryor (NESDIS), Dan Bikos (CIRA) and Scott Lindstrom (CIMSS)

During the early afternoon of 10 June 2013, a cluster of convective storms developed over the front range of the Rocky Mountains of Colorado and then tracked eastward over the western High Plains, south and east of Denver.  These storms were shallow, with bases near 20,000 feet and echo tops of only 30,000 to 35,000 feet AGL, and produced only light precipitation.  By mid-afternoon (2100 UTC), the storm cluster tracked into a region with a deep, unstable mixed layer below the 500-mb level as illustrated in the Rapid Refresh (RAP) model analysis sounding profile over Parker, Colorado  shown below:

At 2120 UTC, a microburst with an 80-mph (70-knot) wind gust was observed 10 miles east of Parker, or 25 miles southeast of Denver (see SPC event summary).  A GOES sounding processing outage from 1800 to 2100 UTC precluded the generation of the MWPI product prior to the microburst, however, the next available sounder scan at 2200 UTC, indicated excessively high Microburst Windspeed Potential Index (MWPI) values to the east of the storm cluster, over Lincoln County.

As seen in the GOES-15 MWPI/NEXRAD composite product image above, the 80-mph microburst wind observation originated from a cell that appeared to be weak, with radar reflectivity of only 25 to 30 dBZ.  The displacement between the microburst (MB) storm as located by radar and by GOES-15 results from the viewing angle of the satellite.  For this case, the MWPI was calculated using the lapse rate and dew point depression difference between the 500 and 700-mb levels.  The closest representative MWPI values over Lincoln County were as high as 103 (102.7), and reflected a highly favorable environment for microbursts as characterized by the sounding profile above, with a large sub-cloud temperature lapse rate and vertical relative humidity gradient.  MWPI values over 100 are rarely observed and are correlated to wind gust potential of 65 to 70 knots (75 to 80 mph) as shown by the regression chart below, adapted to the environment in the vicinity of Parker:

The GOES Microburst product(s) in this event played a very important role because it showed the risk for dry microburst(s) in a more substantial way than being detected on satellite or radar imagery.

GOES visible imagery (Parker, CO indicated by yellow pixel):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10jun13_visible&image_width=1020&image_height=900

showed what would be considered “weak” convection from a satellite perspective.

Also, the 0.5 degree tilt radar reflectivity from the nearby WSR-88D at KFTG showed only a weak signature for this storm due to the lack of precipitation:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10jun13_kftg_z&image_width=1020&image_height=900

The 0.5 degree tilt velocity data did show a subtle signature with an increase of winds (to 30 to 45 kts) near the time of the wind report:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10jun13_kftg_v&image_width=1020&image_height=900

Given the subtle radar and satellite imagery signatures for dry microburst events such as this, one can readily see the utility of GOES microburst products to assess the potential for such events ahead of time.  Real-time GOES microburst products can be viewed here:

http://www.star.nesdis.noaa.gov/smcd/opdb/aviation/mb.html

Synthetic imagery comparison from 2 different models

This blog entry will make a comparison between synthetic imagery generated from 2 different models, the NSSL WRF-ARW and the NAM-Nest, both with a horizontal grid spacing of 4 km.  After comparing the synthetic imagery between the 2 models, we will compare them to the observed GOES imagery.

The major differences between the 2 models when considering synthetic imagery interpretation can be summarized with an example of an extra-tropical cyclone on 23 March 2013

The first thing to keep in mind is that since these are 2 different models, the forecasts will not be identical, the images in the example above are an 18 hour forecast.  For the synthetic imagery from the NSSL WRF-ARW model, the bands being simulated are those that will be available on GOES-R, whereas the synthetic imagery from the NAM-Nest are the bands on the current GOES operational satellites.  For the water vapor imagery, you can see that there is a substantial difference in the wavelength of the band being simulated (6.95 vs 6.5 microns).  6.95 microns has a weighting function that peaks lower in the atmosphere than 6.5 microns, this means the brightness temperatures will be warmer at 6.95 microns than at 6.5 microns.  For most operational applications, (i.e., identifying troughs/ridges, shortwaves, jet streaks etc.) this warm bias has no impact on interpretation of the signature of interest for comparison between model forecast and GOES imagery.  For the IR imagery, the difference is much less (10.35 microns for the WRF-ARW and 10.7 microns for the NAM-Nest), therefore the warm brightness temperature bias will not be present in the IR imagery.  The other important contribution to the difference in the appearance of clouds between the 2 models is the microphysics package being used.  They are different, which means the appearance of the clouds can be different due to this factor.

Consider the synthetic water vapor imagery from the 2 models (left is WRF-ARW, right is NAM-Nest) for a day with convection across the Plains, both initialized at 0000 UTC 29 March 2013:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29mar13_syn_wv_comp&image_width=1020&image_height=900

Concentrate on synoptic / mesoscale features of interest rather than the difference in brightness temperature that is primarily due to the difference in the wavelengths of the bands being simulated.   Can you see any shortwaves that appear to play a role in convective initiation?  Are the shortwaves easier to identify in one model vs another?  There is a difference in the appearance of the afternoon thunderstorms forecast to develop in the Plains which we will discuss in more detail when analyzing the synthetic IR imagery.

Next, let’s compare the synthetic water vapor imagery from the NSSL WRF-ARW (left) with the GOES imagery (right, at the corresponding times, hourly):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29mar13_wrf_goes_wv&image_width=1020&image_height=900

Compare any shortwaves / jet streaks between the model forecast and GOES imagery.  Note the discrepancies between the way the imagery appears from GOES versus that from the model.  Finally, evaluate how the model did in terms of forecast location / timing of these features.

Next, let’s compare the synthetic water vapor imagery from the NAM-Nest (left) with the GOES imagery (right, at the corresponding times, hourly):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29mar13_nam_goes_wv&image_width=1020&image_height=900

Compare any shortwaves / jet streaks between the model forecast and GOES imagery.  Note the discrepancies between the way the imagery appears from GOES versus that from the model.  Finally, evaluate how the model did in terms of forecast location / timing of these features.

Now let’s consider the IR imagery, as before we will compare the appearance of the synthetic IR imagery between the WRF-ARW (left) and NAM-Nest (right) first:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29mar13_syn_ir_comp&image_width=1020&image_height=900

Compare the difference in the appearance of forecast thunderstorms between the 2 models.  Which of the models has smaller anvil cirrus?  Compare the appearance of “non-convective” clouds (clouds that aren’t associated with thunderstorms).

Now compare the GOES imagery (right) with the synthetic IR imagery from the NSSL WRF-ARW (left):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29mar13_wrf_goes_ir&image_width=1020&image_height=900

and the synthetic IR imagery from the NAM-Nest (left):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/29mar13_nam_goes_ir&image_width=1020&image_height=900

For this case, which of the models represented the anvil cirrus more accurately?  Which of the models did a better job with the timing / location of afternoon thunderstorms?

For reference, here are the SPC storm reports for that day:

http://www.spc.noaa.gov/climo/reports/130329_rpts.html

Synthetic IR imagery can be quite useful in forecasting where clouds may inhibit daytime heating and thus surface temperature.  Which of the models appeared to do a better job with the cloud cover forecast on this day?  How early (in the loop) did you have more confidence in one model versus the other when comparing the synthetic imagery with the GOES imagery?

Real-time imagery synthetic imagery from both models can be found here:

http://rammb.cira.colostate.edu/ramsdis/online/goes-r_proving_ground.asp#Synthetic_GOES-R_Imagery_from_Real-Time_NSSL_4_km_WRF-ARW

Training can be found here:

http://rammb.cira.colostate.edu/training/visit/training_sessions/

Synthetic imagery application of a snow event on March 5-6, 2013

The synthetic IR (10.35 um) imagery from the 0000 UTC 5 March 2013 NSSL WRF-ARW run:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/6mar13_syn_wrf&image_width=1020&image_height=900

forecasts a region of colder cloud tops from the Ohio Valley towards the mid-Atlantic states during the late afternoon to morning hours of March 6.  This region of colder cloud tops is associated with an extra-tropical cyclone, so that a deformation zone is seen on the northwest flank of the system while further east we can see TROWAL development wrapping cyclonicaly around the surface low.

The forecast from the synthetic imagery can be compared with GOES IR (10.7 um) imagery over the same time period:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/6mar13_goes&image_width=1020&image_height=900

Overall, there is good agreement between the model forecast and the observed imagery with respect to the aforementioned deformation zone and TROWAL development with the system weakening by the end of the loop (the morning of March 6).  Heavy snow occurred (up to a foot of snow in 6 hours) in portions of western Pennsylvania in association with this feature.  By comparing the model forecast with the GOES imagery one can readily gauge confidence in the forecast for future times by noting how much agreement there is during the shorter term forecast range.

Interestingly, the 1200 UTC NAM run significantly underestimated the snowfall amounts in western PA:

This plot shows the accumulated QPF (inches) from 1200 UTC 5 March – 1200 UTC 6 March.  The areas that received heavy snow mentioned above were on the northern edge of the QPF shield (in the 0.2-0.4″ range).

Transverse bands on February 8, 2013

Transverse bands were observed between approximately 1400-1700 UTC in the vicinity of Buffalo, NY in the GOES water vapor imagery (from the CIMSS satellite blog):

http://cimss.ssec.wisc.edu/goes/blog/wp-content/uploads/2013/02/130208_g13_wv_east_coast_storm_anim.gif

and also in the GOES IR imagery:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8feb13_BUF_ir&image_width=1020&image_height=900

Note the orientation of the transverse bands is approximately perpendicular to the winds at upper levels (350 mb wind direction and isotachs depicted here).

Snowfall rates were observed to be relatively high during the passage of these transverse bands, embedded within a shortwave that was causing snowfall as it was moving east:

The isotach analysis (shown in the GOES IR loop above) depicts these transverse bands to be within an upper-level jet, this is supported by a east-west cross section in the vicinity of Buffalo, NY:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8feb13_BUF_xs&image_width=1020&image_height=900

Note the gradient in wind speed across this jet streak, as wind speeds rapidly decrease in time.  The CIMSS automated tropopause fold algorithm detected a tropopause fold around this time (and with that an associated risk of turbulence):

An interesting question is, how does synthetic imagery from two different models (but at the same horizontal grid spacing) depict these transverse bands?  First, we’ll consider the 4-km NSSL WRF-ARW synthetic imagery (from the 0000 UTC 8 February 2013 model run):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8feb13_BUF_wrf&image_width=1020&image_height=900

The model has a semblance of transverse bands moving across Lake Erie at this time, with the trend of colder brightness temperatures expanding in time.

Next, we consider the 4-km NAM-Nest synthetic imagery (also from the 0000 UTC 8 February 2013 model run):

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/8feb13_BUF_NAMNest&image_width=1020&image_height=900

Your first impression may be that this smooths out the transverse bands, however, this is run at the same horizontal grid spacing (4-km) as the NSSL WRF-ARW model.   One important difference between the two models is the microphysics scheme.  The NSSL WRF-ARW model utilizes WSM6, while the NAM-Nest utilizes the Ferrier scheme.  Note that the trend of increasing coverage of colder cloud tops is still present, but it smooths out any definition to transverse bands.

This example illustrates an important point, when analyzing synthetic imagery from a numerical model forecast it’s important to know which model you are looking at since clouds will appear different based on numerous factors, including microphysics schemes.

Mesoscale vortex development over Lake Ontario on January 23, 2013

An Arctic airmass over the Great Lakes provided for a significant lake-effect snow event off Lake Ontario leading up to January 23, 2013 (maximum snowfall reports 3 feet near the southeast shoreline).  Near the end of this lake-effect snow event, low-level wind speeds decreased as shown in this plot of RTMA surface winds/temperatures along with METARs at 0000 UTC 24 January:

Around this time period, the 0.5 degree reflectivity from the Buffalo, NY WSR-88D showed the development of a mesoscale vortex over Lake Ontario:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/23jan13_radar&image_width=1020&image_height=900

The convergence over Lake Ontario is driven by the temperature difference between the relatively warm lake surface and the relatively cold air over the land.  When conditions are favorable, mesoscale vortices may develop along convergence boundaries.

Conditions favorable for mesoscale vortex development include:

1) weak synoptic pressure gradient and low wind speeds.

2) large lake-air temperature differences.

3) low atmospheric stability.

4) organized convergence over the lake.

These conditions existed during the time period shown in the radar loop above.

In order to better understand the genesis of the mesoscale vortex shown in the radar loop, the WRF-ARW model was employed to simulate the conditions.  The model configuration included a 9-km domain over the eastern Great Lakes region and a nested domain with horizontal grid spacing of 3-km.  The NAM218 was used as initial conditions for a model simulation that begins at 1800 UTC 23 January and ends at 0600 UTC 24 January.  The following is a loop of the WRF-ARW MSLP (hPa, shaded) and surface winds:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/23jan13_wrf_mslp&image_width=1020&image_height=900

The thermally induced convergence zone over Lake Ontario takes some time to spin up during the early portion of the simulation.  Soon after seeing the increase in convergence over the lake, you can see regions of cyclonic vorticity develop along these convergence boundaries.  Also note the lower MSLP values over Lake Ontario, with localized regions of lower pressures where cyclonic vorticity develops over the various convergence boundaries.

The mesoscale vortex of interest that we observed in the Buffalo radar reflectivity field seems to be fairly well represented in the model with movement onshore by later in the loop which resulted in a heavier burst of snow.

Note that there are other other mesoscale vortices further east over Lake Ontario, these vortices are quite shallow so that at this range from the Buffalo radar, the beam would overshoot any mesoscale vortices that existed there.  Since the cyclonic vorticity signature in the model field is so pronounced with this mesoscale vortex over the southeast portion of Lake Ontario:

It’s interesting to construct a cross section (denoted by the purple line) through this mesoscale vortex that was forecast by the model.

Here is a cross section that depicts the temperature across the mesoscale vortex:

Note the warm core at the middle of the transect which corresponds to the mesoscale vortex.  The warm core signature is most evident at low levels and decreases with height.

A cross section of the v-component of the wind across the mesoscale vortex:

Shows the cyclonic circulation extending up to about 2.6 km AGL.

The warm core structure and wind field is consistent with that shown in Laird et al. (2001).

For additional (daytime) examples of mesoscale vortices in visible satellite imagery, see:

http://rammb.cira.colostate.edu/case_studies/20051214/

References / further reading:

Grim, J.A., N.F. Laird, and D.A.R. Kristovich, 2004: Mesoscale vortices embedded within a lake-effect shoreline band. Mon. Wea. Rev., 132, 2269-2274.

Laird, N.F., L.J. Miller, and D.A.R. Kristovich, 2001: Synthetic dual-Doppler analysis of a winter mesoscale vortex. Mon. Wea. Rev., 129, 312-331.

Schoenberger, L.M., 1986b: Mesoscale features of the Michigan land breeze using PAM II temperature data.  Wea. Forecasting, 1, 127-135.

December 3, 2012 Fog event in South Carolina

During the VISIT Satellite Chat on the morning of December 12, forecasters at the WFO in Columbia, SC alerted us to a fog event that took place in their CWA during the early morning hours of December 3, 2012.  During the discussion we showed different types of imagery and products that might be useful to help diagnose and predict fog, and below we expand on some of these.

A relatively new product that is currently being used at some WFOs is model forecasts of satellite imagery, made possible by using a high-resolution model combined with a radiative transfer model.  One of the motivations for producing this synthetic satellite imagery came from the GOES-R Proving Ground, which seeks to allow forecasters to test and evaluate potential imagery and products that will be available when the GOES-R satellite is launched around late 2015.  The idea is that synthetic imagery can be used to show different bands and products that are not available today from the current GOES satellites.  Conventional imagery is also generated, in order to allow forecasters to compare predicted imagery with actual satellite imagery.  One of the products that is produced emulates the legacy fog product that is currently available on AWIPS.  A loop of this synthetic low cloud / fog product between 01:00 and 08:00 UTC December 3 is shown here:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3dec12_synthetic_fog&image_width=1020&image_height=900

The 4-km NSSL WRF-ARW model is used to generate this synthetic imagery. The model is run once per day (at 0000 UTC) out to 36 hours.  The model output is used as input to a radiative transfer model that calculates brightness temperatures at given wavelengths.  After the 3.9 um and 10.35 um bands have been created, they are differenced, similar to the GOES legacy fog product.  In this color table, low clouds appear blue (positive temperature difference), and high clouds are black (negative temperature difference).  This is from the model run on 0000 UTC December 2.  Note that this model forecast does well in delineating the low clouds from the high clouds.

We can compare the synthetic imagery with the familiar GOES low cloud / fog product which is simply the difference between the 10.7 um and 3.9 um bands:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3dec12_GOES_fog&image_width=1020&image_height=900

Note the similarities and differences across South Carolina with respect to the low cloud / fog (blue) location and timing.

One important thing to remember when looking at the synthetic low cloud / fog product is that it does NOT discriminate between low clouds and fog.  We overlaid the ceiling and visibility observations onto the GOES fog product loop to help address this question.

Another way to address this question is to look at the cloud cover field at the lowest vertical level in the model, this would indicate fog forecast by the model:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/3dec12_exp_fog&image_width=1020&image_height=900

Note the model forecast of fog development across South Carolina during this time period.

Real-time imagery may be found on the following web-sites:

Synthetic low cloud / fog from the NSSL 4-km WRF-ARW:

http://rammb.cira.colostate.edu/ramsdis/online/goes-r_proving_ground.asp#Synthetic_GOES-R_Imagery_from_Real-Time_NSSL_4_km_WRF-ARW

Cloud cover at the lowest vertical level from the NSSL 4-km WRF-ARW:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=dev/lindsey/loops/wrf_fog_forecast&image_width=800&image_height=600

While synthetic satellite imagery is not routinely produced at this time by most models, there are other forecast products available from high-resolution models that attempt to address fog occurrence.  An example is shown below for this case from the HRRR (High Resolution Rapid Refresh) model, available online at http://ruc.noaa.gov/hrrr/

Displayed is a 4-hour forecast of surface visibility from the 0400 UTC HRRR run on 3 December, valid at 0800 UTC. A large area of visibility below 0.5 miles is predicted, including over much of South Carolina.  Other applicable products (not shown) include cloud base height and aviation flight rules.

Synthetic Low Cloud / Fog Product for October 10, 2012

Dan Bikos

This blog entry will examine the synthetic low cloud / fog product produced by the 4-km NSSL WRF-ARW model.  For detailed information on this product, see:

http://rammb.cira.colostate.edu/training/visit/training_sessions/synthetic_imagery_in_forecasting_low_clouds_and_fog/

Let’s examine this product on the Washington and Oregon coastline from October 10, 2012:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10oct12_west_syn_fog&image_width=1020&image_height=900

Recall that low cloud or fog is depicted as blue in this color table (positive temperature difference) while the black / dark grey regions are mid- to high level clouds forecast by the model.  This is from the 0000 UTC 10 October model run.  At the start of the loop (0900 UTC) we see extensive regions of low cloud / fog along the Oregon and Washington coastline.  As we move forward in the loop, we observe an expansion of low cloud / fog in a few areas.  One of the more obvious places this is occurring is the Strait of Juan de Fuca just northwest of Seattle, WA.  We can see an eastward of expansion of the low cloud / fog towards the coastline between Seattle and Bellingham, WA.  Soon thereafter we see the expansion of the low cloud /  fog signature in the vicinity of Seattle.  We also observe a northeastward expansion of the low cloud / fog signature southwest of Seattle, in the Chehalis gap moving towards Tacoma.  Less subtle is an eastward expansion of the low cloud / fog signature along the Columbia River that separates Washington and Oregon.  By the end of the loop (1900 UTC) we begin to see dissipation of the low cloud / fog regions as daytime heating becomes more significant.

As a comparison with observations, we will utilize the CIRA GOES low cloud / fog product with corresponding visibility (bottom number, miles) and ceiling (top number, hundreds of feet AGL) observations:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10oct12_west_sw_albedo&image_width=1020&image_height=900

In this color table, low cloud / fog corresponds to grey / dull white.  Note the terminator crosses the scene at sunrise and the product is briefly not as usable at this time.  Compare this loop with what we just discussed in the synthetic imagery model forecast to assess how well the model performed.  Use a high loop speed and click the rock button, does this help identify anything you missed at the normal loop speed? Recall that this product (also the synthetic low cloud / fog product) does NOT discriminate between low cloud / fog, therefore we overlay the ceiling and visibility observations to help us make this discrimination.

At about the same time, over in the southeast we can see large areas of low cloud / fog in Texas / Louisiana and also Florida / Georgia forecast by the model:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10oct12_east_syn_fog&image_width=1020&image_height=900

Note the locations as well as the timing of low cloud / fog.

As before, we will use the CIRA low cloud / fog product along with ceiling and visibility observations for our comparison with what actually occurred:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10oct12_east_sw_albedo&image_width=1020&image_height=900

Over Texas, we see a favorable comparison with the model forecast in that the model forecast a region of low cloud / fog advecting from southeast / southern Texas towards the northwest.  Further north, a rapid expansion of low cloud / fog occurred that eventually merged with the other low cloud / fog field to the south so that most of Texas became covered in low cloud (and fog, in a few locations where visibility was quite low).  Meanwhile, further east across Georgia and Florida, note the low visibilities which would correspond to fog over a fairly large area. By late in the loop, daytime heating helps to dissipate the fog in the 1500-1600 UTC time range, very close to the model forecast.

Note the mid- and high level clouds in the synthetic low cloud / fog product over southern Florida.  This may make identification of the low cloud / fog underneath this cloud field difficult to identify.  One way around this would be to look at the model output directly, this is a loop of the cloud liquid water at the lowest vertical level from the NSSL WRF-ARW forecast for the same time period:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/10oct12_exp_fog&image_width=1020&image_height=900

The blue regions indicate where cloud liquid water is present at the lowest vertical level in the model, therefore this would likely be fog. This product has 2 advantages over the synthetic low cloud / fog imagery:

1) Mid- and high level cloud obscuration is no longer a problem.

2) You may discriminate between low cloud and fog.

The one disadvantage of the product is the comparison with GOES satellite imagery. This is more readily accomplished between the synthetic imagery and the GOES imagery.

This model output of fog from the model is referred to as the NSSL WRF-ARW experimental fog product and the real-time data can be found here:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=dev/lindsey/loops/wrf_fog_forecast&image_width=800&image_height=600

The real-time synthetic and GOES imagery described above can be found on the GOES-R Proving Ground Real-time Products page.

Super Typhoon Jelawat

Dan Bikos

Typhoon Jelawat developed in the Phillippine Sea of the western Pacific Ocean around September 20, 2012.  See the westernmost track of the figure below:

As the cyclone began turning towards the north / northwest while it was east of the Philippines, it went through rapid intensification.  In order to address the rapid intensification, we will consider estimates of MSLP and maximum winds associated with Jelawat from 2 different sources.  First, we’ll consider the estimates by the Joint Typhoon Warning Center (JTWC):

For comparison purposes, we will consider MSLP and maximum wind estimates from the multi platform tropical cyclone wind analysis:

Minimum Sea Level Pressure is calculated directly from the azimuthally averaged gradient level tangential winds produced by the multi platform tropical cyclone wind analysis. The circular domain for the numerical integration has a 600km radius. The pressure deficit resulting from the integration is then added to an environmental pressure. The environmental pressure (Penv) is interpolated from NCEP analyses in a circle 600 km from the cyclone center. The maximum surface winds produced by the analysis are also shown.  For more detailed information see Knaff et al. 2011.

Experience has shown that this estimate of  MSLP may be slightly too low with very intense tropical cyclones such as this, so that the minimum MSLP with this cyclone may be at (or slightly above) 900 mb.  The MSLP values from this method are lower than the JTWC estimates.  Since there is no aircraft recon data available, it is difficult to ascertain the actual MSLP and the winds are estimated via satellite methods with errors on the order of +/- 12 knots.

A loop of high resolution IR imagery of Jelawat can be pieced together from the various pass times of low-orbit polar satellites:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/25sep12&image_width=1020&image_height=730

Here is the corresponding visible imagery at one of those times:

This imagery is available in real-time for global tropical cyclones at the CIRA Real-Time Tropical Cyclones Products web-page.

The Multiplatform Tropical Cyclone Surface Wind Analysis displays related real-time imagery.

Microburst Risk Algorithm observes Favorable Conditions for Strong Convective Winds over the Chesapeake Bay

Ken Pryor (NESDIS / STAR)

A GOES sounder-derived Microburst Windspeed Potential (MWP) algorithm, based on convective available potential energy (CAPE), and vertical temperature and humidity lapse rates, and based on the vertical difference in equivalent potential temperature (theta-e difference, TED) between the surface and middle-troposphere (between 10,000 and 20,000 feet above ground level) recently observed favorable conditions for strong thunderstorm-generated winds over the Chesapeake Bay region.  Theta-e is a measure of moist energy in the troposphere in which the vertical difference characterizes moisture stratification and infers the presence of a mid-tropospheric dry-air layer that, upon interacting with the storm precipitation core, can foster intense thunderstorm downdrafts.  During the afternoon of 8 September 2012, a line of strong thunderstorms developed over piedmont region of Maryland and Virginia ahead of a cold front and then tracked eastward over the upper Chesapeake Bay region during the late afternoon.  A segment of the squall line produced severe downbursts that were recorded by a NOAA buoy on the upper Chesapeake Bay and a Weatherflow station near Annapolis, Maryland. One-minute Super Rapid Scan (SRSO) imagery was available from GOES-14 during this event.

Composite Geostationary Operational Environmental Satellite (GOES)-13 WV-IR brightness temperature difference (BTD) – GOES sounder theta-e difference (TED) – NEXRAD reflectivity composite image at 2040 UTC 8 September 2012 (top) compared to a regional enhanced WV-IR BTD image from GOES-14 at 2038 UTC (bottom) as visualized by McIDAS-V.  In the composite image, “P” represents the location of the Patapsco Buoy and “G” represents Greenbury Point Weatherflow station.

Downburst wind gusts of 40 and 43 knots were recorded by Greenbury Point Weatherflow station near Annapolis, Maryland and by the NOAA Patapsco Buoy in the upper Chesapeake Bay, respectively, between 2035 and 2040 UTC.  As shown in the composite image in Figure 1, theta-e difference (TED) values greater than 20 (black values), corresponding to a high risk of downbursts, were indicated by GOES sounding retrievals over one hour earlier at 1855 UTC over the upper Chesapeake Bay. GOES-14 1-minute color-enhanced WV-IR BTD imagery, from 2030 to 2041 UTC, effectively displayed the interaction of dry-mid tropospheric air with the deep, moist convection cores of the squall line that resulted in strong downdraft generation.  An interactive, web-based version of the animation is available at this link:

http://www.star.nesdis.noaa.gov/smcd/opdb/kpryor/mburst/srsoanim/btd_srso.html

Large TED values, echoed by the theta-e cross section shown below, signified the presence of a prominent mid-tropospheric dry air layer, especially downstream of the squall line over the upper Delmarva Peninsula.  As the precipitation in the deep, moist convective storms interacted with the dry mid-tropospheric air, downburst generation occurred that was confirmed by the presence of spearhead echoes in radar imagery and dry-air notches in the regional BTD image associated with the segment of the squall line moving over the upper Chesapeake Bay.

Rapid Refresh Model-derived theta-e cross section over the upper Chesapeake Bay region at 2000 UTC 8 September 2012.  White triangle marks the location of the Patapsco Buoy.

Similar composite GOES-NEXRAD imagery for nowcasting downburst potential is described and presented in the VISIT lesson “Convective Downbursts”:  http://rammb.cira.colostate.edu/training/visit/training_sessions/convective_downbursts/

New Mexico’s Whitewater-Baldy and Colorado’s High Park Fires: Case(s) of the “Disappearing Smoke” – Differences in Visible Smoke Detection – GOES 15 vs GOES 13

Louie Grasso and Jeff Braun

Updated June 25, 2012

Now: The Whitewater-Baldy Fire was a very large wildfire that existed over southwestern New Mexico during the last couple of weeks of May and into June 2012.  At nearly 300, 000 acreas in size, it it easily the state’s largest wildfire in modern times.  The High Park fire in northern Colorado was at 83,205 acres during the last week of June 2012 with only 45 percent containment and at that time was the second-largest fire in Colorado’s history.

Geostationary satellites can be an excellent tool in tracking the movement of “new” and “old” smoke. New smoke from fires in the late afternoon tends to be optically thick enough to show up well in imagery from both GOES-15 and GOES-13 satellites (the “visible” band on-board these two satellite is centered near 0.67 um).  However, in the early morning hours, “old” smoke from a previous day’s fire may be optically thin in the visible band.  As a result, certain sun-smoke-satellite geometries are required for old smoke to appear in visible imagery for each of these satellites.

Figure 1 - GOES-15

Figure 1 - GOES-15

The Explanation: Solar energy at 0.67 um is scattered in a forward direction from smoke.  Therefore, “old” smoke will be more prevalent in GOES-15 (West) visible imagery during sunrise (Figure 1).  In contrast, “old” smoke can be difficult to detect in GOES-13 (East) visible imagery at the same time (Figure 2).  Both of these images were taken at 1245 UTC.  The difference in the appearance of the smoke in both Figure 1 and Figure 2 is due to the physical and optical properties of the smoke in relation to the angle of the sun and therefore result in smoke plumes that can seem to  disappear during sunrise if looking from the “wrong” direction.

Figure 2 - GOES-13

Figure 2 - GOES-13

Here are some examples from the High Park fire taken on June 25, 2012.

HighPark Fire - GOES West - June25 2012

The above example is a visible image from GOES West taken on the morning of June 25, 2012. Here you can easily see not only the High Park (1) fire in northern Colorado, but five  additional Colorado fire/smoke plumes as well (see image below).

GOES West June 25 2012 Colorado fires 1. High Park 2. Waldo Canyon 3. Treasure 4. Little Sand 5. Weber

The GOES East vantage point, however, is severely lacking fire/smoke detail since the morning sun angle is all wrong (see below).

Where are the same fires? GOES East - June 25, 2012

The only fire that is somewhat easily seen is the huge High Park fire.  However, even then, much of the smoke detail has been lost.  The actual locations of the same fires as seen in the GOES West imagery are circled in the GOES East imagery below for the same time period.  The circles denote each fire area.

GOES East - June 25 2012 - Colorado Fires - GOES West June 25 2012 Colorado fires 1. High Park 2. Waldo Canyon 3. Treasure 4. Little Sand 5. Weber

Into the Future: GOES-R ABI will have a visible band centered near not only at 0.67um  but also another centered near 0.47 um.  These bands are similar to the observed JPSS imagery below (figure 3 – Red and figure 4 – Blue) taken of the Whitewater-Baldy fire in May of 2012.

Figure 3 - 0.67 um Red

Figure 3 - 0.67 um Red

Figure 4 - Visible 0.47 um Blue

Figure 4 - Visible 0.47 um Blue

At 0.47 um (blue), solar energy scatters in a backward direction more than at 0.67 um for a given particle size. One consequence is that “old” smoke will appear in imagery at this wavelength while, at the same time, seem to be absent in imagery at 0.67 um (red – which scatters in the forward direction more than at 0.47 um for the same particle size).  Also note the difference in the appearance of the smoke over eastern New Mexico and the Texas Panhandle between the two wavelengths.  Both visible bands available on the GOES-R ABI will provide an improvement in the detection and tracking of smoke from a single satellite.

1-Minute GOES Imagery over Colorado Severe Storms

On 6 June 2012, GOES-15 SRSO was called, meaning intermittent 1-minute scans would be collected over a mesoscale sector centered in eastern Colorado.  The Storm Prediction Center had a slight risk of severe storms over the region, and by late afternoon convective initiation occurred along the Denver Convergence Zone north and east of Denver.  The loop below shows a sample of the 1-minute Visible Imagery just before sunset.

To view a longer animation with loop controls (stop, step through, etc.), go here http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=dev/lindsey/loops/6jun12_goes15_vis&amp;image_width=1020&amp;image_height=720

The frequent scans allow the movement of storm-top gravity waves to be resolved.  By the end of the loop near sunset, the steep sun angle helps elongate shadows cast by above-anvil cirrus clouds on top of the remaining anvil clouds.  This gives the correct impression that these clouds are indeed well above the rest of the convective anvil.

The corresponding color-enhanced Infrared loop is below.

For a longer loop with controls, go here: http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=dev/lindsey/loops/6jun12_goes15_ir&image_width=1020&image_height=720

Note that the overshooting tops most evident in the Visible loop correspond to relative cold (red) colors in the IR loop.  It’s also quite interesting that most (or maybe all) of the above-anvil cirrus have relatively warm brightness temperatures compared to anvil cloud below.  This suggests that this cirrus resides in the lower stratosphere and has been warmed by the environmental temperatures there (see the sounding below from Denver at 00 UTC and note the temperature inversion above the tropopause). The resulting IR presentation resembles “cold-ring-shaped storms” as documented by Martin Setvak et al. (Atmospheric Research, 2010).

Finally, a technique introduced by Dr. Setvak is to overlay a transparent color-enhanced IR image onto a Visible image – the result is often referred to as a “sandwich” image.  An example from 6 June is below.  The advantage of this presentation is the ability to easily co-locate features in the Visible image with their corresponding IR brightness temperatures.

Finally, one of the VIS images near sunset can be used to do a rough calculation of how high the cirrus sits above the ambient anvil cloud (see below).  At 0151 UTC, based on the shadow length and solar zenith angle, we estimate the cirrus cloud to be about 2.3 km (+/- 0.75 km) above the anvil cloud on which it’s casting a shadow.

March 2, 2012 Severe Weather Outbreak

The severe weather event of March 2, 2012 was forecast well ahead of time by the Storm Prediction Center to be associated with significant severe weather:

An animation of GOES-13 visible imagery can be found here:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=dev/lindsey/loops/2mar12_vis&image_width=1020&image_height=720

Sufficient clearing took place over a large portion of the High and Moderate risk areas during the day, allowing for heating to take place to increase CAPE values.

Numerous supercell thunderstorms developed across this region which resulted in many severe weather reports.

The early imagery between approximately 1500-1700 UTC depicts the storms that led to tornadoes in northern Alabama.

Further north in the warm sector, skies were primarily partly cloudy  in Kentucky, central and western Tennessee, southern Indiana, and southern Illinois.  This allowed insolation to occur and allow for the buildup of CAPE.

Convective initiation occurred in Missouri and Illinois in the early portion of this loop.  Some thunderstorms developed along the cold front (further west), while other storms developed further to the east along a pre-frontal boundary.  The southernmost of these storms developed in southern Illinois and and soon after initiation moved more to the right (eastward) compared to the other storms.  This can be indirectly observed by the orientation of the anvil cirrus, which is more east-west oriented compared to other storms further west which have a northeast-southwest oriented anvil cirrus.  This right moving storm went on to produce numerous tornado reports with significant damage in southern Indiana, northern Kentucky and southern Ohio.  This is a classic example of a long-track tornado.  Although there may have been broken segments in the tornado damage path (where the tornado may have lifted), the path of the supercell is readily detected.

The storms that developed in western Tennessee by 2030 UTC also appear to have initiated along a pre-frontal boundary, just east of the cold front.  Between 2030 and 2125 a left-moving storm is observed in central Tennessee (note the storm with the northward storm motion).  An east-northeast to west-northwest oriented outflow boundary is produced by this storm, which appears to have interacted with the pre-frontal storm mentioned earlier in the vicinity of Nashville.  These outflow boundaries can intensify other storms via enhanced horizontal vorticity that becomes tilted by the storm’s updraft into vertical vorticity, so long as the magnitude of the cold pool isn’t so great that it stabilizes the air mass the storm is ingesting.

There are numerous other boundaries that we see in the visible later in the loop in Mississippi and Alabama.  Some of these are obscured by cirrus from the sub-tropical jet moving into the area by the end of the loop.

Puget Sound Convergence Zone in Action

By J.Braun

An impressive overrunning snow event is on tap for much of western Washington Tuesday, January 17th with bands of snow showers moving inland across northwestern Washington.  The (mainly) snow shower activity has increased in coverage as mid level a disturbance rotates around a weak low west of Cape Flattery.  In addition to this, more organized (and heavier) shower bands are merging with a Puget Sound Convergence Zone (PSCZ) and are bringing moderate to heavy snow to the region around an Everett to Port Angles line.  The PSCZ set up is characterized by flow around the Olympic Mountains (red arrows in image above) through the Strait of Juan de Fuca on the north and the Chehalis Gap on the south.

Associated with the  with the mechanism  in place forming the PSCZ is a snow/rain shadow downwind of the Olympic Mountains (dotted yellow arrow pointing to orange oval, south of the convergence zone).  This area typically gets only a third to half as much precipitation as the rest of the local region.

Forecasts issued Tuesday morning called for about 5 to 10 inches of snow in the Seattle metropolitan area through Wednesday…with 1 -3 inches today and 3 – 7 inches tomorrow. Mostly like the kids are home from school for at least a couple of days.

Our VISIT Orographic Effects Session has more details on this as well as other convergence zones around the country:  http://rammb.cira.colostate.edu/training/visit/training_sessions/satellite_interpretation_of_orographic_clouds/

Snow and Cloud discrimination with GOES-R Proving Ground Product

The GOES-R Proving Ground serves to demonstrate products that will be available on future satellites that are part of the GOES-R series.  One of the Proving Ground products developed at CIRA is the GOES Snow / Cloud discriminator.

The utility of this product can be shown with the snow event that affected Colorado on October 26, 2011.  The GOES visible imagery the following day:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/27oct11_vis&image_width=962&image_height=911

depicts a field of clouds moving over snow covered land in portions of Colorado.  Due to snow and clouds having similar colors, it can be difficult to discriminate between snow on the ground and clouds, particularly for a still image.

Now let’s look at a animation of the GOES Snow / Cloud discriminator product for the same time period:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/27oct11_snow-cloud&image_width=962&image_height=911

In this product, snow cover on the ground appears red, low-level clouds (i.e., stratus) appear off-white, and high-level clouds (i.e., cirrus) appear bright magenta.  Note that this is a daytime only product.

A loop of other channels and proving ground products may be displayed at the same time for comparison purposes:

http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/27oct11_loop&image_width=962&image_height=911

Feel free to stop the loop and move through the slides using the arrow keys or the < and > buttons at the top.

Slide 1:  GeoColor Imagery

Slide 2: GOES 10.7 um (IR)  imagery

Slide 3:  GOES Low Cloud / Fog imagery

Slide 4:  GOES Snow / Cloud discriminator imagery

Slide 5: MODIS visible imagery

The pre-storm environment for the 27 April 2011 tornado outbreak

Knowledge of various air masses and low-level convergence boundaries in the pre-storm environment is critical for a severe weather event.  This blog post will examine the various air masses and boundaries associated with the 27 April 2011 tornado outbreak in the southeast.

First, we will examine the GOES 10.7 um infrared (IR) imagery during the overnight to early morning hours (0545 – 1315 UTC):

http://rammb.cira.colostate.edu/visit/web/27april11/loop_ir_early.asp

An MCS tracked across Mississippi and Alabama during the overnight hours.  The coldest cloud tops tracked from central Mississippi northeastward through central Alabama and into Georgia.  Usually the outflow boundary associated with an MCS will exist just south of the region of coldest cloud tops as this corresponds to the southern edge of the thunderstorm complex.  Near the end of the loop, we see another thunderstorm complex developing in southeast Arkansas moving into northwest Mississippi.

We’ll pick up where the IR loop left off with the higher resolution GOES visible imagery (1332 – 1725 UTC):

http://rammb.cira.colostate.edu/visit/web/27april11/loop_vis_early.asp

Focus on central Alabama early in the loop, there is a distinct line between clear skies and low level cloud steets moving northward.  As we get to the later segment of the loop, you can see north-south oriented lines within this region of low-level cloud streets.  Some of these features are low-level convergence boundaries, others are gravity waves associated with a strong upper level jet that moves over the region.  We’ll discuss this more later.  The convection we observed in the earlier IR loop in northwest Mississippi is moving northeast with a distinct outflow boundary being left behind on its southern flank:

Let’s examine the air mass characteristics via this visible loop with METARs:

http://rammb.cira.colostate.edu/visit/web/27april11/loop_vis_obs.asp

Behind the outflow boundary in northern Mississippi and Alabama, we see a much more stable air mass.  However, as we move forward in time, this outflow boundary is moving north as a warm front, note the strong southerly winds to the south of the boundary.  Note the observation of 67/64 with north winds on the 1825 UTC visible image in north central Alabama, by the 1932 UTC image, this site is now 75/72 with south winds.  A similar trend is seen in northeast Mississippi.  Look further south in central Alabama, the whole region has destabilized considerably and we can see numerous north-south oriented lines which we will examine next.

This is the GOES visible imagery from 1545 – 2132 UTC:

http://rammb.cira.colostate.edu/visit/web/27april11/loop_vis_late.asp

South of the MCS outflow boundary mentioned earlier, we see numerous north-south oriented lines in Mississippi and Alabama.  Some of these are gravity waves associated with an upper level jet that are moving over the region, others are low-level convergence boundaries, some of which may be associated with the overnight MCS activity.  One way to discriminate between gravity waves and low-level convergence boundaries is that convection may develop along the convergence lines while convection does not develop along the gravity waves since they exist at a higher level.  Careful insepction of looping imagery can be compared with these labeled images to help identify the various low-level convergence boundaries:

Labeled visible image at 1825 UTC:

Labeled visible image at 1902 UTC:

Note the low-level convergence lines across central Alabama.  Careful inspection of the looping imagery allows one to detect these features after the gravity wave passes to the east.  The 0.5 degree tilt base reflectivity loop from the Birmingham WSR-88D also shows both of these boundaries in the vicinity of Tuscaloosa and Birmingham:

http://rammb.cira.colostate.edu/visit/web/27april11/loop_radar.asp

The boundaries are easier to see in the early portion of the loop but they become more subtle in the radar loop as times goes forward, and in the visible loop are obscured by anvil cirrus.  Their location and orientation to storm motion would strongly indicate that they played a role later in the afternoon.

This case illustrates the importance of identification of low-level convergence boundaries.  Some of these boundaries were relatively easy to identify, others were much more subtle.  Be sure to use multiple sources of observational data when identifying boundaries, particularly when they are more subtle.

Synthetic IR imagery for 19 April 2011

This blog entry will consider the synthetic IR imagery from the NSSL 4-km WRF-ARW model for 19 April 2011.  There were many severe weather reports on this day:

http://www.spc.noaa.gov/climo/reports/110419_rpts.html

The synthetic IR imagery from the WRF-ARW model from 1200 to 2300 UTC (from the 0000 UTC 19 April 2011 model run) is given here:

http://rammb.cira.colostate.edu/visit/web/19april11/loop_synthetic_ir.asp

The synoptic scale feature of interest is a trough that is moving eastward across the Plains.  A surface low (not shown) is forecast to move across Missouri with an attendant warm front towards the northeast and a trailing cold front to the southwest.  There are indications of the cold front in the synthetic imagery, note the arc of colder brightness temperatures extending southwest from Missouri to north Texas then bending northwest across the Texas panhandle by later in the day.  A dryline is forecast to move eastward across Texas, it intersects the cold front near the Oklahoma border by late afternoon.  Model brightness temperatures are warmer on the dry side of the dryline during the afternoon.  The model has partial clearing in the warm sector ahead of the surface low in eastern Missouri and portions of Illinois.  Initial afternoon thunderstorm development is forecast in this area.  Shortly thereafter, thunderstorm development is forecast by 2200 UTC in southeast Oklahoma and northeast Texas.  By 2300 UTC, thunderstorms are forecast in between these two areas from northwest Arkansas into southwest and central Missouri.

Let’s look at what happened by examining the GOES IR imagery during the same time period:

http://rammb.cira.colostate.edu/visit/web/19april11/loop_goes.asp

After the departure of a morning MCS, clearing took place in eastern Missouri and the southern half of Illinois which allowed destabilization to occur south of a warm front and east of the surface low.  The cold front and dryline in Oklahoma and Texas do show up during the mid-afternoon hours but the lack of contrast by later in the day makes them more subtle than the synthetic imagery.   Toggle on the surface winds (from the RTMA) so you can confirm where the various low-level boundaries are located.  The initial afternoon storms developed in southeast Oklahoma along the intersection of the cold front and dryline with thunderstorm development in eastern Missouri soon thereafter.  Later, convection develops in between these two regions in northwest Arkansas / southwest Missouri.

The synthetic imagery from the WRF-ARW model depicts a similar evolution to thunderstorm development compared to what actually happened.  Monitoring cloud cover trends and identifying the various low-level boundaries throughout the day can assess how much confidence one should have in the model forecast.  Be aware that at times, various low-level boundaries may show up differently in the synthetic imagery compared to GOES imagery.

For more information on severe weather applications of the synthetic imagery from the NSSL 4-km WRF-ARW model, you may take this VISIT training session:

http://rammb.cira.colostate.edu/training/visit/training_sessions/synthetic_imagery_in_forecasting_severe_weather/

Synthetic Imagery for Severe Weather Forecasting

For this blog entry, we’ll consider applications of the NSSL 4-km WRF-ARW model synthetic imagery towards a severe weather event that occurred on June 22, 2010.  Synthetic imagery is model output that is displayed as though it is satellite imagery.  Analyzing synthetic imagery has an advantage over model output fields in that the feature of interest appears similar to the way it would appear in satellite imagery.   The primary motivation for looking at synthetic imagery is that you can see many processes in an integrated way compared with looking at numerous model fields and integrating them mentally.

Figure 1 shows the WRF-ARW synthetic imagery for the 6.95 um (water vapor) band.  The forecast times are indicated at the bottom middle portion of the image, they are from 1200 UTC to 0300 UTC so we are looking at the 12 to 27 hour forecast from the 0000 June 22, 2010 model run.  The model shows an upper-level low over Montana and Wyoming moving eastward.  South of this feature, we can see a region of relatively fast moving warmer brightness temperatures (the red colors moving from Arizona and Utah towards Colorado).  This appears to be associated with an upper-level jet streak.  Another example of a region of warmer brightness temperatures would be across Michigan moving towards Ohio, Pennsylvania and New York.  With both features in the east and the west, there appears to be convection developing during the late afternoon hours.  Remember, we’re looking at mid to upper level features in the water vapor imagery.  The main role of the synthetic water vapor imagery is identifying shortwaves and jet streaks that may play a role in the initiation, maintenance and intensity of convection.  It’s important to understand what you’re looking at in the water vapor imagery when you see a region of warmer brightness temperatures, we’ll discuss this more in future blog entries and in VISIT training sessions that address this topic.  Next, let’s look at lower levels, so we turn to the synthetic IR imagery.

Figure 2 shows the WRF-ARW synthetic imagery for the 10.35 um IR band.  We’re looking at the same time period as the water vapor loop we just looked at.  The advantage to this channel is that low-level features will show up.  This is useful when analyzing cloud cover, to see if clouds will dissipate and allow for sufficient insolation to warm up the surface.  At 1400 UTC we can see low-level clouds showing up as the colder brightness temperatures across western Nebraska and northeast Colorado, these are forecast to dissipate by afternoon hours, however note the higher level clouds forecast across eastern Colorado.  A morning MCS exists across eastern Nebraska moving eastward towards Iowa.   We see the early afternoon convection just ahead of the upper low in Wyoming and Montana by 2000 UTC, while isolated storms develop in eastern Colorado and the Nebraska panhandle shortly thereafter, in the region of strong southwest flow aloft.  Additional convection develops further south in Texas later.  Upscale growth occurs during the late afternoon and evening hours, particularly over Nebraska where there is stronger flow aloft then further south in Texas.  It appears to be an MCS over Nebraska by 0300 UTC.  The best use of the synthetic imagery is to look at the forecast in the morning hours, follow the trends in GOES and other observational data during the day to gauge how much confidence you should have in the model forecast.

Figure 3 shows the GOES 10.7 um IR band over the same time period as the forecast imagery we just looked at.  Notice the low-level clouds in western Nebraska and northeast Colorado dissipated, as was forecast.  The high level clouds in Colorado were well forecast, and looked to be covering a greater area than forecast.  The early thunderstorm activity in Wyoming near the upper low is forecast well.  Notice the later storms in western Nebraska and Kansas, they have much large anvil cirrus canopies than forecast by the synthetic imagery, this is a known bias in the model so that storms in the model will typically appear smaller than observed in GOES.  Upscale growth into an MCS late in the loop in Nebraska seems to be handled well also, and keep in mind that the anvil cirrus canopy will always appear larger in GOES than in the synthetic imagery.

Figure 4 shows the GOES 6.5 um water vapor imagery over the same time period.  The brightness temperatures will generally appear warmer in the synthetic imagery compared to GOES imagery.  The main role of the synthetic water vapor imagery is identifying shortwaves and jet streaks that may play a role in the initiation, maintenance and intensity of convection.  Keep this in mind as you examine the synthetic water vapor imagery, then look at GOES visible imagery and surface observations to see where the key low-level convergence boundaries exist.

The synthetic imagery has exciting potential as an additional tool in forecasting severe thunderstorms, just keep in mind we are looking at model output with its familiar limitations.

For more information on severe weather applications of the synthetic imagery from the NSSL 4-km WRF-ARW model, you may take this VISIT training session:

http://rammb.cira.colostate.edu/training/visit/training_sessions/synthetic_imagery_in_forecasting_severe_weather/

Synthetic Cloud Top Heights

by Louie Grasso, Dan Lindsey, Jeff Braun

Cloud tops heights, an important forecast parameter, is being generated from the NSSL 4km WRF-ARW real-time run.  At CIRA we have been generating synthetic GOES-R imagery from this real-time model since spring 2010.  Based on information and contact at the 14th Great Divide Workshop in Billings, MT, we have begun to generate cloud top height products from the real-time model.  The two images below  indicate the initial step in this direction.  Figure 1 shows a 12 hour forecasted synthetic GOES-R brightness temperature image at 10.35 um.  Similarly, figure 2 shows the corresponding cloud top heights.  These images are valid at 12Z 11/15/2010.  The next step (to come later) is to get this product into an AWIPS friendly format and then follow this with forecast cloud base height and cloud layer information. 

We encourage readers to give us feedback.  This will allow us to improve these products before they are put into AWIPS.

Color Temps

Figure 1 – Synthetic GOES-R 10.35 um image (temperatures – deg K) valid at 12Z 11/15/2010

Cloud Heights

Figure 2 – Synthetic GOES-R 10.35 um image (heights – meters MSL) valid at 12Z 11/15/2010     

United Airlines Flight 967 – Severe Turbulence, July 20, 2010

Jeff Braun and Dan Lindsey NOAA/RAMMB CIRA/CSU

***(Also, please see addendum near the end of this message)

The following are a sequence of GOES-13 visible images from 19:45 UTC on 20 July 2010 to 00:45 UTC on 21 July 2010.

turbtsgw_3.gif

On 20 July 2010, a United Airlines Boeing 777 aircraft experienced severe turbulence during its trip from Washington, D.C., to the west coast.  There were multiple injuries reported by the passengers and the plane was rerouted to Denver.  Such extreme turbulence is quite rare, so we decided to check and see whether GOES satellite imagery provided any clue as to what caused   it.

The loop (above) is GOES-13 visible imagery from 1945 UTC on 20 July to 0045 UTC on 21 July.  Convection forms in northeast Kansas by 2030 UTC, and the storms expand in coverage and intensify as they move into northwestern Missouri.  The anvil cloud from the convective mass expands so the south and east, and wavelike features are evident atop the cloud.  At 2232 UTC, a linear cloud feature can be seen pushing slightly ahead of the expanding anvil  in central Missouri.  Its height is uncertain, but it appears to be below the ambient anvil, but above the boundary-layer cumulus clouds.

United flight 967 was flying at 38,000 ft. when it reported the severe turbulence at 0001 UTC.  The location of the report is plotted on the 2345 and 0015 UTC GOES images by a red ‘X’ (below).  Unfortunately, no GOES-East image is available at 0000 UTC because the 2345 UTC image is a full-disk scan.  It should be noted that 2345 UTC is the beginning of the 26-minute scan, and since it starts from the north, it was probably scanning Missouri around 2353 UTC, plus or minus a couple of minutes.  As can be seen in the loop (above), the above-mentioned linear cloud line appears to intersect the location of the turbulence at the correct time (0001 UTC).  However, the anvil cloud is also rapidly moving in that direction.

goes-vis-compare.jpg

So what caused the turbulence?  One possibility is a gravity wave initiated by the convection earlier in northwestern Missouri.  However, gravity waves require a stable layer in which to propagate, and a nearby sounding in Springfield showed no such stable layer near 38,000 ft.  Another possibility is what can be simply described as an upper-level front generated from the convection.  Trier and Sharman (MWR, 2009) discuss how upper-level outflow from an MCS can generate widespread turbulence.  They show model-derived perturbation winds at jet cruising altitudes, and significant anomalous winds can propagate away from intense convection.  If a plane is experiencing relatively constant head winds (for example), but then suddenly encounters winds from a different direction, it will change the plane’s lift and likely cause it to experience altitude fluctuations while it attempts to adjust to the changed environmental wind vector.  So something along these lines might help explain what flight 967 encountered, but this is pure conjecture.  Trier and Sharman (2009) also discuss how low values of the Richardson number and elevated regions of Turbulent Kinetic Energy (TKE) can be associated with observed turbulence.  If (gravity) waves propagate into an already low Richardson number environment (low stability and or high vertical wind shear), these waves may further reduce the Ri locally to the point of inducing Kelvin-Helmholtz instabilities and turbulence away from the storm. A similar analysis for the present case is needed.goes-13_ir_loop.gif Above is a a GOES longwave IR loop of the same event and time period.  Long recognized as a telltale satellite signature for turbulence, these images show the rapid lateral expansion of the cold convective anvil cloud and convectively induce outflow (and very rapid expansion – final 3 frames of loop).  Even this sequence of lower resolution geostationary IR window imagery is sufficient to observe this signature as we are simply looking at expansion rates of the anvil cloud pattern and outflow signatures.  Rapid anvil expansion indicates strong upper-tropospheric divergence associated with this developing convection.  This example shows both signatures, with rapid cloud top expansion followed by very rapid (but on a smaller scale) expansion  – represented by a surge of outflow to the east and southeast of the primary convective regions (see below).  Strong vertical wind shear between the outflow layer and the atmosphere above/below has induced turbulent mixing which could be responsible for the turbulence observed in this case. In the final two images of the above loop, (images below), the yellow X marks the position of the aircraft and the red arrows and lines mark the “outflow” boundaries (area of most rapid expansion).  In the first of the two images (2345Z July 20) the aircraft lies near or just outside the outflow region, however, 30 minutes later (second image – 0015Z July 21)we can plainly see that the aircraft now is well within the outflow surge.  The single image (below) with the red hatched area (again, yellow X represents the aircraft position) outlines the most significant  turbulent region.  This IR image also shows that the brightness temperature of this region falls between the warmer lower levels and colder upper reaches (which could be estimated with the cloud top algorithm).  Moreover, this area of extreme divergence represents (locally) a level above and below which there would be maximum negative and positive vertical velocities respectively.
goes-ir-compare.jpggoes-13_ir_loop_frame_0014expansion.jpg Finally, we took at a look at simulated satellite imagery from the 4-km NSSL WRF-ARW initialized at 00 UTC on 20 July (below).  The series of images show the simulated 10.35 micron data at hourly time steps.  Note that the model does a reasonable job of forming the convection in northwestern Kansas, and wavelike features can be seen propagating to the southeast, even though the anvil cloud dissipates too soon in the model.  We plan to closely examine the model output to see whether conditions were favorable for turbulence generation (in the model world).

sim_ir_loop.gif

***Addendum***

Since this blog was originally posted, new information has been gathered.  Thanks to additional data provided by Dr. John Williams (NCAR), it was learned that the location of the aircraft that we were originally given at the time of the event (00:14 UTC) was wrong.  The new corrected location is about 100 miles southwest of the old location…well under the southern periphery of the MCS’s canopy.  The other piece of information, which undoubtedly added to the turbulence encounter, was the formation/initiation of a convective cell near the path of Flight 967 (see radar image below).  While the scenario depicted and explained previously still holds true, with multiple areas of divergence and convergence occurring along with associated strong changes in both horizontal and vertical wind shear, the addition of a growing convective cell near the path of the aircraft would certainly add to the probability of an encounter with severe turbulence.

Radar Aircraft Location

Oil and Water Really Don’t Mix

By J. Braun

Additional information/images from  – Steven Miller, Bernie Connell, Dan Lindsey, and NASA

Tower of Fire and smoke

Figure 1(above).

Eleven crew members of the state of the art floating oil rig “Deepwater Horizon” are now presumed dead following an explosion on the rig which then caught fire, burned for two days, then sank in 5,000 ft of water in the Gulf of Mexico.  Our deepest condolences go out to their families.

The rig, located roughly 50 miles southeast of the coast of Louisiana, was contracted by BP Oil Company through its owners Transocean (the world’s largest offshore drilling contractor).  The rig is state of the art because
it is not moored (i.e. it does not use anchors) but instead uses a triply-redundant computer system together with global satellite positioning (gps) to control the location of the rig within a few feet of its intended location, at all times (called Dynamic Positioning System).  It may take quite some time to figure out just how/why a rig of this advanced state of technology caught fire in the first place, much less sank, with all the “fail safe” hardware, software and equipment on board.  Up to 5000 barrels of oil are currently leaking into the gulf every day.  Efforts to cap the well have so far been unsuccessful.

pyro

Figure 2.

sat pyro

Figure 3 – NASA’s Aqua/MODIS RGB image.

sat pyro 2

Figure 4 – Same as above only close-up.

However, after Deepwater Horizon did catch fire – flames were often visible up to about 35 miles away as they were often 200 – 300 ft high!  There were also some very white portions of the plume which is believed to be pyrocumulus – and can actually be seen in the April 21 MODIS image as a white spot directly over the rig(Figures 2, 3 and 4.).

448577main_img_feature_1649_4x3_1024-768.jpg

Figure 5 – NASA’s Aqua/MODIS – April 25, 2010 three channel RGB image.

snapshot_045413.jpg

Figure 6 Same as above only close up view.

snapshot_041914.jpg

Figure 7 – Same as previous two images only very close up.

snapshot_045213.jpg

Figure 8 – NASA’s Terra/MODIS April 22, 2010 – three channel RGB image.

Other satellite views (figures 5, 6, 7, 8) use sunglint to identify the oil/fuel slick…in this case appearing as a bright swirly region owing to mirror reflection of the solar disk off the waters whose capillary waves (a wave traveling along the phase boundary of a fluid, whose dynamics are dominated by the effects of surface tension) have been suppressed by the slick making it appear “smoother.”  It also depends strongly on the sun/satellite sensor geometry relationship in terms of how these features will appear in the imagery. That is, if the feature had been located in another part of the “glint zone” it may have appeared as dark against lighter surrounding waters.

Oil slicks are often notoriously difficult to spot in natural-color satellite imagery because a thin sheen of oil only slightly darkens the already dark blue background of the ocean.  However, under unique viewing conditions (such as with the help of sunglint), oil slicks can become visible.  For the following images (figures 9 and 10 and 12), sun glint was also the major reason that the slick could be observed (not temperature variations – even in the 11.0 or 3.9 micron imagery). The 3.9 shortwave IR imagery is able to pick up the slick because of the differences in the reflective component between water and oil. The longwave IR (figure 11) is not able to separate these differences…and the apparent temperatures across the two surfaces is being sampled as being virtually the same. Note, however, the variation that different enhancements make to the shortwave IR (3.9 um) imagery.

CTREG2

Figure 9 NASA’s Aqua/MODIS 3.9um image April 25, 2010 “normally” enhanced.

ir3_9_enhct1.GIF

Figure 10 – Same as above only further enhanced (effective temperature range reduced).

ir11_ctreg1.GIF

Figure 11 – Same as previous two images only taken at 11.0um longwave IR.

oil_spill_goes_edited.gif

Figure 12 – GOES-13 visible April 29, 2010

Is it Real, or is it…Synthetic GOES-R Imagery?

Jeff Braun, Louie Grasso and Dan Lindsey

As part of the GOES-R Proving Ground activities, synthetic GOES-R imagery has been produced from model output run at the National Severe Storms Laboratory (NSSL). This model is being run with horizontal grid spacing of 4 km over the continental United States during the spring of 2010. An automated system was designed at the Storm Prediction Center (SPC) to send model output to the Cooperative Institute for Research in the Atmosphere (CIRA) as soon as it is produced. At CIRA, an automated system was also in place to generate synthetic GOES-R imagery at selected wavelengths. Once the imagery is ready, the data is converted into McIdas AREA files and placed on an ADDE server at CIRA. These AREA files are then acquired at the SPC for viewing on an NAWIPS system.

As a result of the automated system, such imagery represents a real time forecast of GOES-R imagery. Due to resource limitations, only four GOES-R bands could be generated in a real time setting. As an example, Figures 1 and 2 show a twelve hour loop (The twelve hour loop shows images every hour from 1200 UTC to 0000 UTC) of GOES-R synthetic WV forecast imagery (top)with that of the corresponding GOES-13 WVobserved imagery (bottom).  Figures 3 and 4 show the same 12 hour period for both forecast and observed imagery, only for the longwave IR channel.  Differences in brightness temperatures between GOES-R synthetic and GOES-12 observed may be due to the different central wavelengths and band widths (GOES-R 6.185 µm vs GOES-13 6.5 µm – for the WV channels – and GOES-R 10.35 µm vs GOES-13 10.7 µm – for the IR channels).

GOES-R Synthetic WV(Figure 1: Animated gif of GOES-R 6.185 µm synthetic forecast WV brightness temperatures. The twelve hour loop shows forecast images every hour from 1200 UTC to 0000 UTC – April 24, 2010.)GOES-13 WV Imagery

(Figure 2: Same as figure 1, but for GOES-12 6.5 µm WV channel observed brightness temperatures.)

GOES-R synthetic IR Imagery(Figure 3: Same as figure 1, but for GOES-R 10.35 µm forecast synthetic IR brightness temperatures.)GOES-13 IR Imagery(Figure 4: Same as figure 1, but for GOES-12 10.7 µm observed brightness temperatures.)

Note of interest – this imagery was forecast and observed for April 24, 2010 which was a day of extreme  severe weather over the southern portion of the USA…particularly for Mississippi and Alabama.   Nearly 60 tornadoes were counted for April 24 alone (about 40 of these were discrete tornado reports).  At least 12 people lost their lives on this day.  A three day total for this spring’s strongest severe weather system (up to now) was that it spawned around 110 tornadoes (about 90 when adjustments were made for duplicate reports) between April 22nd and April 24th. This three day period produced more tornadoes than had been reported during the entire previous four months!

MODIS Longwave channel difference of Gulf Stream and Labrador currents

Jeff Braun and Louie Grasso

Figures 1-4 show Modis images at 3.9, 8.53, 11.02, and 12.03 µm, respectively at 1825 UTC on 6 April 2010. Off the east coast of the United States, two ocean currents are evident in the imagery: The cool, southward flowing, Labrador current and the warm, northerly flowing, Gulf Stream. All of the first four figures shows the clear boundary between these two currents.

6apr2010_39.GIF

Figure 1: Modis 3.9 µm, 6 April 2010, 1825 UTC.

6apr2010_85.GIFFigure 2: Modis 8.53 µm, 6 April 2010, 1825 UTC. 6apr2010_11.GIFFigure 3: Modis 11.02 µm, 6 April 2010, 1825 UTC.6apr2010_12.GIF

Figure 4: Modis 12.03 µm, 6 April 2010, 1825 UTC.

Figures 5-7 show the brightness temperature 8.53-11.02, 8.53-12.03, and 11.02-12.03 difference, respectively. As seen in Figures 5 and 6, small variations in color existed between the two ocean currents. In particular, the sign of the difference is the same for both currents: negative. However, a different picture emerged in Figure 7. More detail is evident in this channel difference compared to the previous two. This may be a result of the larger range of values in Figures 5 and 6. Nevertheless, the sign of the difference in Figure 7 does change. Noticeable variations exists between the two currents, within the Labrador current, along the Carolina coast line, and between the waters east and west of the Outer Banks of North Carolina.

6apr2010_8m11.GIF

Figure 5: Modis 8.53-11.02 µm, 6 April 2010, 1825 UTC.

6apr2010_8m12.GIF

Figure 6: Modis 8.53-12.03 µm, 6 April 2010, 1825 UTC.

6apr2010_11m12.GIF

Figure 7: Modis 11.02-12.03 µm, 6 April 2010, 1825 UTC.

What information is contained in the variation seen in Figure 7? In particular, what does the change of sign within the Labrador current imply? What does the change of sign say about the waters east and west of the Outer Banks? Give us your comments and best answers if you’d like.

Severe thunderstorms over southwest Arkansas on 10 March 2010

Jeff Braun, Louie Grasso and Dan Lindsey

IR

Figure 1: GOES-12 10.7 µm on 10 March 2010 at 2115 UTC. Color table shows enhances temperatures in Celsius.

On the afternoon of 10 March 2010, thunderstorms developed over northeast Texas. These storms moved northeastward into southwest Arkansas. A GOES-12 10.7 µm image at 2115 UTC shows the location of the storms (Figure 1). Note the rather small enhanced V signature over the storm in extreme northeast Texas. Likewise, a larger, although not as pronounced, enhanced V signature was evident over the storm in southwest Arkansas. As seen in the color table in Figure 1, cloud top temperatures were near -50 C in and around the enhanced V signatures.

3.9

Figure 2: GOES-12 3.9 µm on 10 March 2010 at 2115 UTC.

A different pattern was evident in the 3.9 µm image for the same two storms (Figure 2). The speckled green region in both these storms bounds a grey region. This grey region represents brightness temperatures near -10 C. This suggests that more solar energy at 3.9 µm was reflected back to GOES-12 from the grey region.

Ice Particle Size

Figure 3: GOES-12 ice particle size on 10 March 2010 at 2115 UTC. Small particles are blue, medium sized are yellow, and large are red.


Both the 10.7 and 3.9 images are used to retrieve ice particle size, results are shown in Figure 3. In this figure, the largest particles are represented by red, while the smallest particles are represented by blue. A comparison between Figures 2 and 3 shows that the smallest particles are the most reflective. An open question is, “What can one infer about the behavior of a storm based on the reflective nature of the anvil of the storm?” Figure 4 shows the base reflectivity from Shreveport, LA at approximately the same time as the satellite imagery.

Radar

Figure 4: Shreveport, LA radar base reflectivity on 10 March 2010 at 2118 UTC.

MODIS Images: Lake Erie Ice Break-up and Ice Floe

Lake Erie Break-up March 3, 2010 through March 8, 2010

Figure 1: March 3rd through March 8th 2010 (NexSat Project/Naval Research Laboratory)

J. Braun

Whew!  Hints at spring may finally be around the corner.  Cold Arctic winter temperatures through most of the beginning of this new year have kept much of the Great Lakes in frozen repair through early March.  However, a hopefull proxy for spring is showing itself in the figure 1 (18Z) loop (above) and figure 2 (close-up below) with the ongoing break-up ice ofver the last week or so. Winds finally shifted from northerly to southwesterly, helping to lift temperatures from the region’s icy continental grip with the mercury climbing into the lower 40s. Although the signal is welcome, there can be problems.  The combination of shifting winds and warmer temperatures can bring hazardous conditions to the lake in the form of ice floes.  For those who venture too far out when the break-up begins, there is a distinct change at becoming trapped and adrift on the ice…and headed towards Canada. �
Close-up Ice Break-up
Figure 2: March 5th through March 9th 2010 (Steve Miller and NexSat Project/Naval Research Laboratory)

In the past, hundreds of ice fishermen have been stranded on huge ice floes when the shifting winds caused a floe to break away from the main ice.  Some have even perished trying to escape (e.g. cold water and heart attacts, drownings, and being crushed by the ice itself).  Many also lose expensive equipment to this phenomenon – such as snowmobiles, trailers and fishing equipment.  Most of those who are rescued are taken off by either helicopter or hovercraft…and a lot of dollars.  Ice fishermen often try to get as far out on the ice as they can for the best fishing…breaking many ice safety rules and put themselves in danger in the process. Timely imagery from remote sensing sources (like this MODIS group of images) can keep (some) fishermen from becoming trapped, by visually supporting (direct observation) the forecast process that may indicate the the beginning of the ice break-up.  Although this MODIS imagerey has great resolution (250m), it’s temporal resolution could be better.  When GOES-R finally arrive in the latter half of the decade, some of the resolution will be lost (500m), but the images will arrive every 5 minutes instead of once a day…so take your pick.   

GOES-11/MODIS channel differencing for icing hazard

J. Braun and Louis Grasso

Fog Product

Figure 1: GOES-11 fog product for 8 UTC 2 March 2010.

Identification of liquid water cloud layers that have temperatures below freezing is important for aviation safety. Brightness temperature differencing two channels may be used to identify liquid water cloud layers. Figure 1 (above)shows the channel difference between 3.9 and 10.7 µm from GOES-11 prior to sunrise. A liquid water cloud layer (white) is evident over Nebraska, western Kansas, and western Oklahoma. LW Fog

Figure 2: GOES-11 (10.7-12.0) µm channel difference for 17 UTC 2 March 2010.

Nine hours later, the channel difference between 10.7 and 12.0 µm from GOES-11 (Figure 2 – above)  indicates the same cloud liquid water cloud layer over central Nebraska and northern Kansas(red). MODIS imagery also captured the same cloud layer at 830 UTC at 3.9 µm (Figure 3 – below). Brightness temperature differences of MODIS imagery between 8.53-11.02 µm (Figure 4 – below) and 8.53-12.03 µm (Figure 5 – below) are unable to reveal the cloud layer. This is due to the lack of contrast between the liquid water cloud and the surrounding ground.

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Figure 3: Modis 3.9 µm at 830 UTC 2 March 2010.

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Figure 4: Modis 8.53 – 11.02 µm for 830 UTC 2 March 2010.

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Figure 5: Modis 8.53 – 12.3 µm for 830 UTC 2 March 2010.

However, brightness temperature differences of MODIS imagery between 11.02 – 12.03 µm does reveal the liquid water cloud layer (Figure 6 – below). These examples highlight the benefit of channel differencing to identify liquid water cloud layers. When this information is combined with the GOES-11 skin temperature (Figure 7 – below), one can see that the liquid water cloud layer, shown in the above figures, has a cloud top temperature below freezing. This may be valuable information to those concerned with icing potential of aircraft.

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Figure 6: Modis 11.02 – 12.3 µm for 830 UTC 2 March 2010.

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Figure 7: GOES-11 skin temperature at 17 UTC 2 March 2010.

GOES-R ABI smoke imagery for the Proving Ground

J. Braun, Louis Grasso and Don Hillger

GOES-R ABI will have the ability to produce imagery at 0.47 µm (blue) and at 0.67 µm (red). Although GOES-R will be unable to produce any images at 0.555 µm (green), color imagery can still be generated with certain techniques. These techniques can be tested through the use of synthetic GOES-R ABI imagery. Synthetic imagery refers to satellite imagery of numerical model output. Shown in Figure 1 is an example of synthetic GOES-R ABI color imagery over southern California for 23 October 2007. On this particular day, southern California was experiencing wildfires. As a result, smoke properties were used to include smoke in the synthetic imagery. Each of the above listed bands were reproduced followed by the RGB combination that led to the color image seen in Figure 2. Smoke detection with GOES-R ABI will exceed current GOES capabilities as thin smoke plumes are only visible during low sun angle periods, while GOES-R will be able to highlight these areas during the entire daylight period.  This is due to the inclusion of a band at 0.47 µm (blue).

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Figure 1: Synthetic GOESR-ABI color imagery over southern California for 23 October 2007.

withsmokeFigure 2: Synthetic GOESR-ABI color imagery over southern California for 23 October 2007 with smoke.

Proving Ground: GOES-R ABI color imagery

J. Braun, Louie Grasso and Don Hillger

When GOES-R becomes operational, one new capability that no other GOES satellite has had to date is the ability to produce geostationary color imagery. As a result, satellite detection of pollution and/or thin smoke from wildfires can be detected (where they couldn’t in the past). GOES-R ABI will have the ability to produce imagery at 0.47 µm (blue) and at 0.67 µm (red). Although GOES-R will be unable to produce any images at 0.555 µm (green), color imagery can still be generated with certain techniques. These techniques can be tested through the use of synthetic GOES-R ABI imagery. Synthetic imagery refers to satellite imagery of numerical model output. One benefit of synthetic imagery is to aid in the preparation of GOES-R ABI data once the satellite becomes operational. Shown in Figure 1 is an example of synthetic GOES-R ABI color imagery over southern California for 23 October 2007. Likewise, a second example of synthetic GOES-R ABI color imagery over the upper high plains is displayed in Figure 2 for 27 June 2005. Both images have been enhanced using a procedure that is similar to the way NASA enhances MODIS imagery.

California
Figure 1: Synthetic GOESR-ABI color imagery over southern California for 23 October 2007.

Plains color
Figure 2: Synthetic GOESR-ABI color imagery over the upper high plains for 27 June 2005.

GOES-11 Longwave channel difference for liquid water

J. Braun, Louie Grasso and Dan Lindsey

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Figure 1: GOES-11 (0.67) µm image for 24 February 2010. (click image for larger view)

GOES-11 has two channels in the window region from which a channel difference can be produced. One channel is centered at 10.7 µm while the second is centered at 12.0 µm. Figure 1 (above) shows a visible GOES-11 image for 24 February 2010. Northern sections of lake Superior are clear while a cloud layer has formed over the southern portion of the lake. This was a result of relatively cold northerly winds advecting over the lake. In addition, portions of the eastern Pacific are cloud free. Reflective signatures at 3.9 µm suggest that the cloud layer over the southern portions of lake Superior is composed of liquid droplets. That is, the cloud free portion of lake Superior is a plane surface of liquid water; the cloud layer over the lake is composed of liquid water droplets; and the cloud free portion of the Pacific is also a plane surface of liquid water. Shown in Figure 2 (below) is the brightness temperature difference between the two channels, Tb(10.7) – Tb(12.0). Note the sharp contrast in values between the three liquid water bodies in the image. Although the features of interest are composed of liquid water, perhaps the difference is due salt water, fresh water, and small liquid water droplets. This may give rise to emissivity differences. Vertical profiles of water vapor and temperature may also give rise to such differences.  GOES-11 Channel DifferenceFigure 2: GOES-11 (10.7-12.0) µm channel difference.  (click image for larger view)

Thunderstorm features through channel differencing in the GOES-R Proving Ground

J. Braun

GOES-R ABI will have several channels in the window region from which channel difference products can be produced. Four channels, 8.5, 10.35, 11.2, and 12.3 µm can be differenced to produce a total of six difference images. In this example, differences were produced from a numerical simulation of the 27 June 2007 thunderstorm event over Wyoming. Satellite imagery of numerical model output is termed “Synthetic” imagery. Shown in Figures 1-6 are the six possible synthetic channel differences that can be produced from the above four GOES-R ABI channels (click each image for a larger view). All figures feature a thunderstorm anvil (160 < X < 260 ; 460 < Y < 560) that exhibits a small negative difference (Figure 1) that increases—as one views the figures in numerical order–to small positive values (Figure 6). In contrast, the edge of the thunderstorm anvil exhibits relatively large positive values in all figures. In regions of no clouds (X=280; y=540), differences change from negative values to positive values as one views the figures in numerical order. In summary, channel differencing can be used to identify the thick ice clouds composing the interior of the thunderstorm anvil; thin ice clouds along the edge of the thunderstorm anvil; and water vapor in the clear sky region.

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GOES-R Proving Ground At the Great Lakes

Louie Grasso, Dan Lindsey, J. Braun

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GOES-R ABI will have several channels in the window region from which channel difference products can be produced. Three of the channels, 8.53, 11.02, and 12.03 µm are available from MODIS. From these three channels, three channel differences can be produced. In this example, one of the channel differences highlights lake ice more effectively than any of the three separate channels and the other two channel differences. Figures 1 through 3 (click for larger images) show MODIS images at 8.53, 11.02, and 12.03 for 19 February 2010.

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In the 8.53 – 11.02 difference (Figure 4) lake ice shows up as blue against the green background of open water and the blue surrounding land mass (see white circles). The contrast between the ice and surrounding land mass is relatively small in these locations. Further, the sign of the channel difference (negative) is the same for ice, land, and open water in this image. A somewhat different pattern appears in the 11.02 – 12.03 difference (Figure 5). The ice has a channel difference that is of the opposite sign as the land mass and a different enough color contrast. However, differences between the ice and open water are of the same sign and small enough that contrast differences are relatively small. Finally, the 8.53 – 12.03 difference (Figure 6) exhibits ice that has a channel difference that is of the opposite sign compared to the surrounding land and open water. Furthermore, the color of the ice is a sharp contrast to the color of the surrounding land and open water. Thus, this example illustrates the usefulness of channel differencing in the identification of lake ice.

Synthetic imagery for model evaluation in the Proving Ground

Louie Grasso, Dan Lindsey, J. Braun

Something Wrong Improvement

Synthetic GOES-12 satellite imagery at 3.9 µm has been produced for a thunderstorm simulation. This event occurred on 27 June 2005 over the upper Midwest of the United States. Observations (Figure 1 – click image for larger format) shows warmer cloud tops (light grey) for storms over western Nebraska compared to the colder cloud tops (black) for the storms over Iowa. In contrast, synthetic imagery produced from a numerical model output shows cold cloud tops for the storm over western Nebraska and Iowa. This is an example of how synthetic satellite imagery can be used to evaluate the performance of a numerical model. Compared to observations, the synthetic imagery suggested a problem with the microphysics: Ice crystals were too large in the anvil of the storm over western Nebraska. After some time, the problem was identified and solved. Synthetic imagery from a new simulation (Figure 2 – click image for larger format) shows that the cloud top temperatures warmed for the storm over western Nebraska. This resulted in a better match with observations.

Proving Ground: Future GOES-R – Convective Initiation

Louie Grasso, Dan Lindsey  and J. Braun

GOES-R ABI will have several channels in the window region from which channel difference products can be produced. Four channels, 8.5, 10.35, 11.2, and 12.3 µm can be differenced to produce a total of six difference images. Shown in Fig. 1 is a loop, every five minutes, of one such difference: 10.35 – 12.3 µm. This difference was produced from a numerical simulation of the 27 June 2007 thunderstorm event over Wyoming. Satellite imagery of numerical model output is termed “Synthetic” imagery.

Figure 1. Brightness Temperature Differences

Figure 1: Animated gif of brightness temperature differences between synthetic imagery at 10.35 and 12.3 µm. The loop shows images every five minutes, similar to the frequency of GOES-R.

One advantage of a channel difference of synthetic imagery is to highlight the ability of a forecaster to identify regions where cumulus clouds, and eventually thunderstorms, may form. In the above loop, the precurser is the increase of positive values of the channel difference near the center of the image. Values are near 5 C prior to the development of convection. This signature was a consequence of the horizontal convergence (Fig. 2) of water vapor, which locally increased its depth within the boundary layer (Contours in Fig. 3). Water vapor is transported upward by positive vertical motion (Shaded region in Fig. 3).

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Figure 2: Surface wind vectors plotted on a 10.35 – 12.3 µm difference image. Note the horizontal convergence associated with the 5 C difference near the center of the figure. The horizontal black line is the location of the cross-section in Fig. 3

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Figure 3. Vertical cross section through the channel difference maximum (see Fig. 2). The cross section was taken in the east-west direction and shows the increased depth of the water vapor contours at x=170. Values of vertical motion are shaded every 0.3 m s-1.

SHyMet for Forecasters Development Plan

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Jeff Braun

New Developmet Plan (LMS) available!

This is the forecaster track of the Satellite Hydrology and Meteorology (SHyMet) Course (similar in structure, but exanding on or covering different subjects than the intern version of SHyMet) will cover satellite imagery interpretation, including feature identification, water vapor channels and what to expect on future satellites. There is a session on remote sensing applications for hydrometeorology, this includes uses of remote sensing data for operational hydrology. There is also a session on aviation hazards such as volcanic ash, fog, dust etc. Other topics include an understanding of the Dvorak method in tropical cyclone analysis and the utility of cloud climatologies in forecasting.

For more information or to register for this development plan, click here.

Toward an Advanced Sounder on GOES?

Temperature and Dew Point Profiles

Jeff Braun

New Training Session from COMET!

Satellite imagery and soundings have been an integral tool in the weathercaster tool belt over the past four decades and continue critical support for operational meteorology and monitoring of our ever changing weather patterns. Some products however, particularly those from today’s geostationary sounders are under-utilized in the forecasting community. This new module asks members of the meteorological community why that is.

In addition, the future of geostationary sounder technology is clearly hyperspectral observation. Current sounders have 18 infrared spectral bands to detect the vertical structure of the atmosphere and we have seen how forecasters feel about this instrument. Hyperspectral observations from a geostationary infrared sounder would mean more than a thousand spectral bands on a single instrument for increasing vertical resolution.  The module answers the question, “Why do we need a HES on boeard the next GOES series?”

A few additional points to ponder:  1. The first and likely second in the next generation of GOES satellites will not carry a sounder.  2. The GOES-R ABI will provide a continuity of legacy sounder products.  3. Without a geostationary sounder, there will be an increased dependence on NWP to create legacy products.  4. A hyperspectral sounder would allow for the next major step forward.

To take this session in its entirety, please follow this link:  http://www.meted.ucar.edu/goes_r/geo_sounding/

Also, for more thoughts concerning this topic from this blog, click here.

Forecasting Convective Downburst Potential: Update

Ken Pryor (NESDIS)

The VISIT lesson “Forecasting Convective Downburst Potential Using GOES Sounder Derived Products” presents current applications of a suite of GOES sounder-derived products. The lesson has been recently revised to include updated imagery examples, and new case studies of downburst events that occurred over the United States Great Plains during June and August 2009. The cases demonstrated the effectiveness of coordinated use of the GOES Microburst products in evolving convective storm environments.  The objective of the lesson is to provide better understanding of techniques for predicting the risk of convective downbursts utilizing GOES sounder derived data.  The guide for the lesson is available at the following web address:  http://www.cira.colostate.edu/ramm/visit/downburst.html .  Also revised is the audio playback version of the lesson, also available on the student guide page.  An example of one of the case studies of a significant downburst event is summarized below. 

During the afternoon of 26 August 2009, strong convective storms developed along a weak, slow-moving cold front as it was tracking eastward over Oklahoma. Although there was very little temperature contrast across the front, the front acted as a convergence zone and a trigger for deep, moist convection. The pre-convective environment downstream of the cold front over western Oklahoma was dominated by vertical mixing that fostered the development and evolution of a convective boundary layer. Elevated Geostationary Operational Environmental Satellite (GOES) imager brightness temperature difference (BTD) values (yellow to orange shading) and Microburst Windspeed Potential Index (MWPI) values in the vicinity of downburst occurrence over western Oklahoma served as evidence of the presence of a well-developed mixed layer. Strong downbursts that were recorded by Oklahoma Mesonet stations between 0000 and 0100 UTC 27 August resulted from a combination of precipitation loading and sub-cloud evaporation of precipitation. These downbursts occurred in proximity to moderate to high microburst risk values as indicated in the 2200 UTC GOES microburst products.

Microburst Risk

Microburst Windspeed Potential
The images above are a Geostationary Operational Environmental Satellite (GOES) imager microburst product at 2200 UTC 26 August 2009(top) and a corresponding GOES sounder Microburst Windspeed Potential Index (MWPI) product (bottom), with the location of Oklahoma mesonet observations (i.e BESS, WEAT, etc.) of downburst wind gusts plotted on the MWPI image. Both product images displayed convective storms developing along the cold front over western Oklahoma. Convective storm activity increased in coverage near the cold front during the following three hours. Downburst wind gusts between 41 and 56 knots were recorded by the Oklahoma Mesonet stations plotted in the MWPI image above between 0000 and 0100 UTC 27 August.
The following are significant downbursts recorded by the Oklahoma Mesonet during this event:
Station – Time (UTC) – Wind Gust (knots)
Bessie – 0005- 50
Kingfisher – 0020 – 43
Weatherford – 0030 – 41
El Reno – 0040 – 50
Medford- 0055 – 56
 
Also important to note the general increase in MWPI values from southwest (BESS) to northeast (MEDF) associated with a progression from hybrid to stronger wet type downbursts. Downbursts over western Oklahoma were predominantly “hybrid” type, while over north-central Oklahoma (MEDF, BREC), downbursts were “wet” type associated with heavy rainfall.

Texas Severe Storm during GOES-11 SRSO: 5 May 2009

On 5 May 2009, NESDIS staff at CIRA called for Super Rapid Scan Operations (SRSO) for GOES-11 due to the forecast of severe weather in Texas.  The GOES-11 SRSO activation is in preparation for the VORTEX-II field project so that high temporal resolution satellite imagery is available for this important field project.

During the VORTEX-II field project (May 10 – June 13) , GOES-11 SRSO will be activated on days when RSO schedule is not in use for GOES-11.  The temporal resolution during an SRSO schedule is one-minute data, however, there will not be continuous 1-minute data due to operational scan sectors:

http://www.ssd.noaa.gov/PS/SATS/GOES/WEST/s-srso.html

For this reason, there will be gaps between higher and lower temporal resolution data.

During times of GOES-11 SRSO activation,  the Visible and IR data respectively,  may be viewed on the following web-sites:

http://rammb.cira.colostate.edu/projects/svr_vis/vortex2/visloop.asp

http://rammb.cira.colostate.edu/projects/svr_vis/vortex2/irloop.asp

You may also view the data on the GOES West Visible Floater sector on the RSO RAMSDIS online page:

http://rammb.cira.colostate.edu/ramsdis/online/rso.asp

The visible imagery from 5 May 2009 over Texas may be viewed here:

http://rammb.cira.colostate.edu/projects/svr_vis/srso/5may09/visloop.asp

 On this day, a thunderstorm developed along a low-level convergence boundary and quickly became severe.  Hail up to softball size was reported with this storm.  Note the development of inflow feeder clouds along the southern flank of the storm.  The inflow feeder clouds became evident between 0000 and 0030 UTC 6 May, and afterward can be seen moving quickly towards the storm.  The viewing angle from the GOES west perspective offers a favorable perspective of the inflow feeder clouds. There are occasions when GOES-east data will not show this signature over the central US while GOES-west does due to its more favorable view angle.

West Texas Windstorm: 8 February 2009

Ken Pryor


A convectively active late winter season over the Great Plains has proven fruitful for the assessment of the GOES-11 imager microburst risk product. During the evening of 8 February 2009, a line of convective storms tracked through eastern New Mexico and western Texas, producing several strong downbursts west of Lubbock. This event served as another good example of the utility of the GOESWest (GOES11) imager microburst algorithm described in the previous blog entry (“Forecasting Convective Downburst Potential”, 29 January 2009). This downburst event occurred at the end of a day of boundary layer mixing due to a combination of strong surface heating and low-level wind shear, and thus, demonstrated the importance of the evolution of the convective mixed layer in downburst generation as reflected in the GOES microburst product imagery. The line of convective storms crossed the New Mexico border into Texas around 0100 UTC 9 February. The first downburst recorded in Texas was observed at Denver City West Texas Mesonet station with a wind gust of 47 knots at 0100 UTC, followed by a stronger downburst with a wind gust of 60 knots at 0125 UTC. Further downburst activity was observed at Anton (50 knots) and Reese Center (46 knots) mesonet stations west of Lubbock at 0225 UTC and 0235 UTC, respectively.

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The image above is a recent example of the GOES11 imager microburst risk product at 0000 UTC 9 February 2009 with overlying radar reflectivity imagery from Lubbock (KLBB) NEXRAD at the time of downburst occurrence at Denver City, 0100 UTC. The product image was visualized by McIDAS-V software, available online at http://www.ssec.wisc.edu/mcidas/software/v/. The image was filtered to display only reflectivity higher than 35 dBZ to emphasize the heaviest precipitation cores where downbursts are likely to be generated. Apparent in the product image is the storm line crossing the Texas border, propagating into a region of high microburst probability as indicated by the progression from orange and red shading in the image. Clouds are represented by light to dark blue shaded areas in the product image. The next product image, valid at 0030 UTC with overlying radar reflectivity imagery at the time of downburst occurrence, shows the eastward progression of the storm line. 0221 UTC radar reflectivity data indicated a small bowing segment of the line northwest of Lubbock (L), associated with the downburst in progress at Anton.

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Note that the Anton downburst again occurred in close proximity to elevated imager microburst risk values as displayed by darker orange shading. This case demonstrated that the GOES-11 imager microburst algorithm output, when combined with radar reflectivity data into a composite product image, can effectively show forecasters where microbursts are likely. The GOES-11 microburst algorithm models the preconvective environment by utilizing brightness temperature differences between the midwave and longwave infrared channels to approximate favorable temperature and moisture gradients in the boundary layer that would enhance convective downdraft generation. More information about the GOES-11 microburst product can be found in the VISIT lesson titled “Forecasting Convective Downburst Potential Using GOES Sounder Derived Products”.

Forecasting Convective Downburst Potential – by Ken Pryor (NESDIS)

The VISIT lesson “Forecasting Convective Downburst Potential Using GOES Sounder Derived Products” presents current applications of a suite of GOES sounder-derived products. A recent concern pertaining to the GOES sounder products is the current temporal and spatial resolution (60 minutes, 10 km). The GOES-R Advanced Baseline Imager (ABI) has promising capability as a sounder with greatly improved temporal and spatial resolution as compared to the existing GOES (8-P) sounders. However, until GOES-R soundings and associated derived products are operational, a need has been established for a higher resolution GOES-derived microburst risk product. Accordingly, a multispectral GOES imager product has been developed and experimentally implemented to assess downburst potential over the western United States with improved temporal and spatial resolution. The availability of the split-window channel in the GOES-11 imager allows for the inference of boundary layer moisture content. The experimental product is available on the web:

http://www.star.nesdis.noaa.gov/smcd/opdb/kpryor/mburst/mbimg.html.

 

The GOES-West (GOES-11) imager microburst algorithm employs brightness temperature differences (BTD) between band 3 (upper level water vapor, 6.7um), band 4 (longwave infrared window, 10.7?m), and split window band 5 (12um). A study of the relationship of water vapor radiance and layer-average relative humidity has found a strong negative correlation between 6.7um brightness temperature and layer-averaged relative humidity between the 200 and 500-mb levels. Thus, large BTD between bands 3 and 5 imply a large relative humidity gradient between the mid-troposphere and the surface, a condition favorable for strong convective downdraft generation due to evaporational cooling in the sub-cloud layer. This product provides a higher spatial (4 km) and temporal (30 minutes) resolution than is currently offered by the GOES sounder microburst products (10 km, 60 minutes) and thus, provides useful information to supplement the sounder products in the convective storm nowcasting process.

 

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The image above is a recent example of the GOES-11 imager microburst risk product at 2030 UTC 8 December 2008. Apparent in the product image is clusters of convective storms over the western Texas Panhandle and over eastern New Mexico that would track eastward over western Texas during the following three hours. Associated with the convective storm cluster near the New Mexico border were downburst wind gusts of 50 and

57 knots (plotted in image near the New Mexico border) that were recorded by Plains and Denver City (West Texas) mesonet stations at 2115 UTC. Note that the downbursts occurred in close proximity to elevated imager microburst risk values. The downbursts resulted in the generation of a dust storm over western Texas that affected the Lubbock area (see below).

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(Photo:  Texas Tech University – West Texas Mesonet) For more click here.

 

For more information about the GOES imager microburst product, please review the paper published in the preprints of the 16th Conference on Satellite Meteorology and Oceanography:http://ams.confex.com/ams/89annual/techprogram/paper_147786.htm.

The Summer Edition of ‘The Front’ – What’s New?

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The June 2008 copy of The Front” newsletter highlights upcoming changes to the new Terminal Aerodrome Forecast (TAF) which is scheduled to go operational this November (2008).  See this site: www.weather.gov/os/aviation/taf_testbed.shtml for more information. 

And speaking of TAFs, want to know just what happens to those TAFs you write?  Just what do the Center Weather Service Units (CWSU)s, Air Traffic Control Centers (ARTCC)s, and the airports themselves do with those forecasts? Well, they turn them into valuable graphical displays that help reduce weather related airspace congestion, that’s what.  See the story starting on page three.  For a good “live” example, click here

Finally, this season’s newsletter ends with a bit of research coming from the Aviation Weather Center (AWC) regarding the prediction of thunderstorm movement throughout the seasons.  More than 27,000 Convective SIGMETs were analyzed for this study whose details begin on page ten.  Good way to categorize and address the “climatology” of thunderstorms in your area throughout the year. 

“California Burnin’ on Such a Summers Day”

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(Courtesy NASA/MODIS/TERRA – July 26, 2008)

Jeff Braun

Currently, California has 26 fire incidents…mostly across the northern half of the state.  Fourteen of these fires are considered large at the moment (= or > 100 acres…see following map).

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To date (this fire season – through July 30, 2008), over 750,000 acres of California have burned. Most of the current fires started as a result of lightning strikes between June 20th and June 28th…with a couple of them going back to the last week in May!  Several thousand firefighters from across the country have been deployed to the region over the last couple of months, with additional fire specialists from Canada, Australia, and New Zealand also helping out.  Many Incident Meteorologists are also working these fires this summer.  Check out this recent visible image loop that shows the region between the evening of July 29th and through the morning of the 30th. For more information on these fires as well as others across the country, please go to these sites:  http://gacc.nifc.gov/oncc/, http://www.nifc.gov/, and http://www.inciweb.org/ .

Flash Flood Season in the Rocky Mountain West – Just a Reminder

Jeff Braun

This is just a brief reminder that it is monsoon/(FLASH) flood season here in Colorado and the rest of the Rocky Mountain West and adjacent High Plains.  While this region is no stranger to flooding conditions…particularly in the late spring and early summer when combined severe weather threats often aggravate the ongoing snow melt, a secondary, and often much more dire, flood season often accompanies the arrival of the North American Monsoon (see July 15, 2008 blog).

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(From:  Petersen, W. A., and Coauthors, 1999: Mesoscale and radar observations of the Fort Collins flash flood of 28 July 1997. Bull.Amer. Meteor. Soc., 80, 191–216.) The problem is two-fold, as the increase in subtropical moisture via the monsoon is injected into a rather “quiet” mid-upper level flow pattern (typically associated with a mid-upper level ridge – see 500mb analysis above).  If the cap can be broken (which is most often accomplished in the mountains and foothills due to the increase in elevation), the convective storms are most often quite slow to move…sometimes remaining terrain tied to the same area for a period of (several) hours.  Even when there is net movement in one direction, it is relatively slow and new development tends to replace the older storms almost immediately…so storm propagation opposite of the storm’s forward motion gives the illusion of remaining in place and going nowhere. This is a most dangerous situation. Flash flooding is always a concern in mountainous terrain anytime of the year where steep valley walls can contain rainwater…forcing it into gullies, creek beds, streams and rivers in a very short period of time. You can be many miles away from the actual storm…not even able to see or hear it…and get yourself in a great amount of trouble in short order. However, this time of year when you add the additional conditions of increased moisture with slow, or nearly “stationary” storm motion – the problem becomes hugely magnified when the rain falls over these same areas.  Just a few major flash flooding events of interest that have occurred this time of year “out west” follow:The Big Thompson Flood – July 31, August 1, 1976: During the evening of the 31st, over 4 inches of rain fell across a large portion of the Big Thompson basin in less than 6 hours, with over 12 inches falling in a smaller area containing the western third of the Big Thompson Valley.  Much of the canyon was devastated with a 20 foot plus wall of water – killing 139 people!  See these links for more information: http://www.coloradoan.com/news/thompson/ , http://www.reporterherald.com/webextra/1976flood/ , and The Big Thompson Fact Sheet .

The Cheyenne Wyoming Flood – August 1, 2 1985: During the afternoon and evening of August 1st, 1985 a nearly “stationary” thunderstorm produced over 6 inches of rainfall in just under 3 hours.  Add to this, large quantities of hail (three to four feet deep in some slide areas) and a tornado and you have the makings of a disaster.  Twelve people died and over 70 were injured in this mess.

The Fort Collins Colorado Flood – July 28, 1997: The late afternoon and evening of the 27th of July began as a relatively typical thunderstorm event for this time of year with storms blossoming and putting down heavy rain off and on through the overnight hours.  It was a little heavier than normal, with a gradient of precipitation from east to west across the city of Fort Collins lying between 0.75 inches and 4 inches…and higher amounts up to nearly 7 inches just to the northwest of town (near Laporte, Colorado).  This would only herald the beginning as the next day would be the straw that broke the camel’s back.  By noon on the 28th, and under very similar meteorological conditions as the previous day, imbedded thunderstorms once again erupted. In the following six hours anywhere from 1 inch (far eastern Fort Collins) to around 10 inches (far western Fort Collins) of precipitation fell…with rain rates at times as high as 4 to 5 inches per hour.  Spring Creek which runs west to east just south of the center of town exploded from its banks, killing five and causing another 200+ million dollars in damage. For more information see the following links:  http://ccc.atmos.colostate.edu/~odie/rain.html, http://fcgov.com/oem/historical-flooding.php, http://media.www.collegian.com/media/storage/paper864/news/2007/08/01/News/Video.Fort.Collins.Flood.Of.97-2927035.shtml, http://rammb.cira.colostate.edu/resources/docs/Two_floods.pdf, and http://olympic.atmos.colostate.edu/flood97.html.

The Las Vegas Flood of July 8, 1999: The valley that Las Vegas (“The Meadow”) resides in is a primary reason that rainfall events can get out of hand fairly quickly.  Las Vegas is nestled between mountains on nearly all sides…is built over an area that drains these mountains toward the Colorado River…and the composition of the of all this runoff soil/silt is relatively impermeable to water (i.e. runoff).  Add to this the thousands of miles of asphalt and cement from all the new building and you have a recipe for disaster.  The usual Saving Grace is that the region “normally” only receives about 4 inches of rainfall per year, which if stretched over an entire year is not of concern.  However, thunderstorms during the monsoon season can easily put down over a half an inch of precipitation inside of an hour…which is about all that it takes to get flash flooding going in this drain-less oasis in the Mohave.

On this day, rain rates were somewhat higher…bringing between and inch and a half and three inches of rain inside of 90 minutes.  There were two fatalities and over 20 million in damages with this “little” storm.  Just goes to show how relative conditions are from place to place.  Please click here for more info concerning this event.

If you have any interesting season events/phenomena in your region of the world, please let us know and send us the info and images (if you have them)…and we will post them for the interest of others. See “Contact Us” or “Topics, Ideas and Questions” to the right under “Pages.”

Hello Dolly! Tropical Cyclone Season has “un”officially arrived!

Jeff Braun

As Hurricane Dolly made its way into southern Texas July 24, 2008 with 100+ mph winds, drenching a 40 mile wide and 100mile long stretch, along and north of the Rio Grande River, with anywhere between 8 and 22 inches of rain, it heralded the true beginning of tropical cyclone season here in the lower 48.  Yes, the “official” season starts on June 1st and we have already had named storms in both the Atlantic and Pacific, however, this was the first direct hit that the USA has taken in the new year.  Tropical Storm Arthur formed near coastal Belize and then immediately tracked westward into Mexico.  Hurricane Bertha started out as a wave off the coast of Africa, reaching hurricane strength (as high as a category 3 for a short time) northeast of the Windward Islands and then weakened back to tropical storm intensity a hundred miles or so south southeast of Bermuda.  Several days later, after passing almost directly over the island of Bermuda, it slowly headed off to the northeaast where it regained category 1 intensity briefly before heading into the much cooler waters of the North Atlantic.  Of note:  While Bertha did not bother too many interests other than Bermuda and the shipping industry, it did wander the waters of the Atlantic for well over two weeks.  Just before the appearance of Dolly, Torpical Storm Cristobal formed near the gulfstream waters just east of Savannah, Georgia.  Cristobal tracked to the northeast, along and away from the eastern coast of the USA,  for the next 5 days – becoming extratropical on July 23rd.  Cristobal provided areas of much needed rain to the southeastern USA in addition to some good breakers for the surfers up the coast.   In the Pacific, there have been 7 named storms from Alma to Genevieve, however, most of those have had little direct impact (again, other than the shipping business) on the USA.�

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(Courtesy NASA/MODIS/TERRA – July 23, 2008 – at landfall)

The week of July 27th is turning out to be on the slow side  – northern hemisphere, tropical cyclone speaking – so I thought, during this lull, that I would mention what the primary missions of this year’s (2008) Hurricane Field Program are …in the case that you haven’t already heard about them (from the 2008 Hurrcane Field Program Plan, signed June 30, 2008):

(1) Three-Dimensional Doppler Winds: This is a multi-option, single-aircraft operational missiondesigned to use the NOAA P-3 to sample TCs ranging in intensity from tropical depression to a major

hurricane. The definition is meant to separate this category from tropical waves and disturbances that have yet to develop a well-defined warm-core circulation. The main goals of these missions is: 1) to improve understanding of the factors leading to TC intensity and structure changes, 2) to provide a comprehensive data set for the initialization (including data assimilation) and validation of numerical hurricane simulations (in particular HWRF), 3) to improve and evaluate technologies for observing TCs, and 4) to develop rapid real-time communication of these observations to NCEP. The overall experiment is comprised of two parts: one designed to obtain regular 12- or 24-h resolution airborne Doppler-radar observations of hurricanes, with optional dropwindsondes, and one, the National Environmental Satellite, Data, and Information Service (NESDIS) Ocean Winds and Rain Experiment, designed to improve understanding of microwave surface scatterometery in high-wind conditions over the ocean by collecting surface scatterometery data and Doppler data in the boundary layer of hurricanes.

(2) Tropical Cyclone Landfall and Inland Decay Experiment: This is a multi-option, single-aircraft experiment designed to study the changes in TC surface wind structure near and after landfall. It has several modules that could also be incorporated into operational surveillance or reconnaissance missions. An accurate description of the TC surface wind field is important for warning, preparedness, and recovery efforts. It addresses IFEX Goals 1, 2, and 3.

(3) Tropical Cyclone Unmanned Aerial System (UAS) Inflow/Eyewall/Eye Experiment: This is a multioption, multi-aircraft experiment that uses the Aerosonde UAS and dropwindsondes or aircraft expendable bathythermographs (AXBTs) launched from the NOAA P-3 to fully demonstrate and utilize the unique capabilities of the Aerosonde platform to document areas of the TC environment that would otherwise be either impossible or impractical to observe. It is planned that this effort will be based in Barbados. The immediate focus is to document and significantly improve understanding of the rarely observed TC boundary layer and undertake detailed comparisons between in-situ and remote-sensing observations obtained from manned aircraft (NOAA P-3 and Air Force Reserve (AFRES) C-130) and satellite-based platforms. In addition, a primary objective of this effort is to provide real-time, near-surface wind observations to NHC in direct support of NOAA operational requirements. These unique data will also be made available to EMC for both model initialization and forecast verification purposes. This addresses IFEX Goals 1, 2, and 3.

(4) Tropical Cyclogenesis Experiment: This multi-option, multi-aircraft experiment is designed to study how a tropical disturbance becomes a tropical depression with a closed surface circulation. It seeks to answer the question through multilevel aircraft penetrations using dropwindsondes, flight-level data, and radar observations on the synoptic, mesoscale, and convective spatial scales. It will focus particularly on dynamic and thermodynamic transformations in the low-and mid-troposphere and lateral interactions between the disturbance and its synoptic-scale environment. It addresses IFEX Goals 1 and 3. (5) Nadir Off-set SFMR Experiment: This is a single-aircraft experiment designed to obtain measurements off nadir of the sea surface to help develop retrieval models for the HIRAD.

(6) Tropical Cyclone/AEW Arc Cloud Experiment: This is a single-aircraft experiment, designed to collect observations across arc cloud features in the periphery of an AEW or TC using aircraft flight-level and dropwindsonde data to improve understanding of how these features may limit short-term intensification. Observations could be made using either the P-3 aircraft conducting another experiment, or the G-IV during a synoptic surveillance mission.

(7) Saharan Air Layer Experiment: This is a multi-option, multi-aircraft experiment which usesdropwindsondes launched from the NOAA G-IV and NOAA P-3 to examine the thermodynamic andkinematic structure of the SAL and its potential impact on TC genesis and intensity change. The dropwindsonde release points will be selected using real-time GOES SAL tracking imagery from UWCIMSS and mosaics of SSM/I total precipitable water from the Naval Research Laboratory. Specific effort will be made to gather atmospheric information within the SAL as well as regions of high moisture gradients across its boundaries and the region of its embedded mid-level easterly jet. The goals are to better understand and predict how the SAL dry air, mid-level easterly jet, and suspended mineral dust affect Atlantic TC intensity change and to assess how well these components of the SAL are being represented in forecast models. It addresses IFEX Goals 1 and 3. 8) Sea-Salt Aerosol and Cloud Base Number Concentration Experiment: This single-aircraftexperiment is a downwind flight leg outside the eyewall in relatively clear air, or just inside the inner edge of the eyewall. It will measure the concentrations of sea-salt aerosol and CCN concentrations below cloud base (1200- to 2000-ft flight levels are likely) in tropical storms and category 3 or less TCs, as well as approximately 200 ft above cloud base.

(9) Eyewall Microphysics Experiment: This is a single-aircraft, high-altitude penetration of eyewall convection, designed to document the ice-phase microphysics of the eyewall better than ever before, to benefit microphysical parameterizations in simulation of TCs. This could improve modeling of precipitation production, thus accurately estimating latent heat release (LHR) (affecting hurricane intensity) and rainfall quantitative prediction. It is preferred that it be flown at or above 20,000 ft. (10) TC-Ocean Interaction Experiment: This is a multi-option, single aircraft experiment acting insupport of upper ocean and air-sea flux measurements made by oceanic floats and drifters. One to three float and drifter arrays will be deployed into one or two mature storms by an AFRC C-130J and provide real-time ocean data. A NOAA P-3 will deploy dropwindsondes and make SFMR and Scanning Radar Altimeter (SRA) measurements within the float and drifter array as the storm passes over it. This work will be coordinated with NASA P-3 deployments of CTD probes.

(11) Hurricane Synoptic Surveillance: This is a multi-option, single or multi-aircraft operational mission that uses dropwindsondes launched from the NOAA G-IV, and the AFRES C-130 to improve landfall predictions of TCs by releasing dropwindsondes in the environment of the TC center. These data will be used by NCEP to prepare objective analyses and official forecasts through their assimilation into operational numerical prediction models. Because the atmosphere is known to be chaotic, very small perturbations to initial conditions in some locations can amplify with time. However, in other locations, perturbations may result in only small differences in subsequent forecasts. Therefore, targeting locations in which the initial conditions have errors that grow most rapidly may lead to the largest possible forecast improvements. Locating these regions that impact the particular forecast is necessary. When such regions are sampled at regularly spaced intervals the impact is most positive. The optimal targeting and sampling strategies is an ongoing area of research. This addresses IFEX Goal 1.

For more on the many (other) research oriented activities of the Hurricane Research Division (HRD) – part of the Atlantic Oceanographic and Meteorological Laboratory (AOML) – please follow this link:  http://www.aoml.noaa.gov/hrd/index.html .

The North American Monsoon Season Has Begun

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(Courtesy NOAA/NWS – July 15, 2008)

Jeff Braun

Strong heating over the elevated (Mexican Plateau) desert southwest CONUS causes an area of low pressure to form known as a thermal low.  Since the air pressure is relatively higher over the nearly adjacent ocean areas (Gulf of California and the Tropical Pacific) to the south and west, air flow (from high pressure to low pressure)  begins to bring much more humid air toward the thermal low.  Instability with this lower source (level) of moisture will help in developing thunderstorms in which rain can actually reach the ground (instead of just virga storms) which will additionally add to the boundary layer moisture and help in increasing thunderstorm chances for a prolonged period…at least until the cycle reverses in late summer/early fall (when land temperatures decrease some and the oceanic waters reach their maximum).  Mid and upper level flow around high pressure aloft will also bring mid and upper level moisture into the region from the Gulf of Mexico.

Of course there can be much variability with the North American Monsoon as to where and how intense the moisture and thunderstorms tend to be.  Where both the thermal and upper level lows/highs set up is of major importance and can mean the difference between all or nearly nothing.  For example, while a good strong upper level ridge over the great plains area will help drive moisture into the southwest CONUS, a weaker ridge or one located further west over New Mexico or northern Mexico will keep the moisture located to the east and over the great plains.  There are also a large number of other variables which can adversly affect the monsoon (see this short paper).  Currently the thermal low is located over N to NWrn Mexico and the upper level ridge is centered over the Rio Grand Valley region of Texas/Mexico.

According to the the National Weather Service (NWS) out of Tucson, who track the North American Monsoon and its progress, the monsoon “officially” began here in the United States around the 2nd of July, 2008 (when average dewpoints in the Tucson region remain at least 54 deg F. or higher) – see the Monsoon Tracker page.  Past 24 hour rainfall (as of July 15, 2008) is depicted at the top of the page and is very typical for an early season monsoon pattern.

For a more detailed and fascinating look at the North American Monsoon, see the NWS Tucson’s Monsoon section.

See also these papers:

Adams, D.K., and A.C. Comrie, 1997: The North American Monsoon. Bull. Amer. Meteor. Soc., 78, 2197-2213.

Douglas, M.W., R.A. Maddox, K Howard, and S. Reyes, 1993: The Mexican monsoon. J. Climate, 6, 1665-1667.

Carleton, et.al., 1990: Mechanisms of Interannual Variability of the Southwest United States Summer Rainfall Maximum. J. Climate, 3, 999-1015.

Higgins, R.W., Mo, K.C. and Yao, Y., 1998: Interannual Variability of the U.S. Summer Regime with Emphasis on the Southwestern Monsoon. J. Climate, 11, 2583-2606.

Barlow, M., Nigam, S., and Berbery, E.H., 1998:  Evolution Of the North American Monsoon System. J. Climate, 11, 2238-2257.

Higgins, R.W., Chen, Y. and Douglas, A.V., 1999: Interannual Variability of the North American Warm Season Precipitation Regime. J. Climate, 12, 653-679.

Higgins, R.W. and Shi, W., 2000: Dominant Factors Responsible for Interannual Variability of the Summer Monsoon in the Southwestern United States. J. Climate, 13, 759-776.

Higgins, R.W. and Shi, W., 2001: Intercomparison of the Principal Modes of Interannual and Intraseasonal Variability of the North American Monsoon System. J. Climate, 14, 403-417.

Castro, C.L., McKee, T. B. and Pielke, R.A., 2001: The Relationship of the North American Monsoon to Tropical and North Pacific Sea Surface Temperatures as Revealed by Observational Analyses. J. Climate, 14, 4449-4473.

Vera, C. et. al., 2006: Toward a Unified View of the American Monsoon Systems. J. Climate, 19, 4977-5000.

Castro, C.L., Pielke, R.A. and Adegoke, J.O., 2007: Investigation of the Summer Climate of the Contiguous United States and Mexico Using the Regional Atmospheric Modeling System (RAMS). Part I: Model Climatology (1950-2002). J. Climate, 20, 3844-3865.

Castro, C.L., Pielke, R.A. and Adegoke, J.O., 2007: Investigation of the Summer Climate of the Contiguous United States and Mexico Using the Regional Atmospheric Modeling System (RAMS). Part II: Model Climate Variability. J. Climate, 20, 3866-3887.

Volcano Season is Year ‘Round

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Jeff Braun

Volcanoes, particularly volcanic ash, are major concerns to many of us in meteorology.  While the physical presence of the mountain and the energy expended during an eruption can be quite enormous, dangerous, and both life and property threating,  the atmospheric discharge of ash can also be a major hazard to aviation as well as the local health communities.

The recent eruption of Okmok (1st eruption – July 12) volcano in the Alaskan Aleutian Islands has garnered some recent press in the USA which started as recently as July 12, 2008 (see photo above – courtesy NOAA).  Days later (July 17th) part of the plume was seen coming into our own Pacific Northwest (CIMMS blog).  However, there have been many other important eruptions around the world which have already impacted the lives of many and continue threaten many more.  The Chaitin Volcano in Chili is a good example (