The National Oceanic and Atmospheric Administration’s current Geostationary Operational Environmental Satellites (GOES) have the capability to produce higher quality images of weather phenomena on smaller spatial and temporal scales than any other geostationary weather satellite, past or current. Furthermore, advances in computer technology allow forecasters to examine and perform calculations on satellite imagery in near real time on desktop, and even some laptop, computers.
This tutorial utilizes satellite data covering a severe weather outbreak which took place in Kansas and eastern Colorado on 31 May 1996 to demonstrate many of these new capabilities. While it may prove educational to any user, it was created for use by those who have prior experience in the science of meteorology. Imagery from both GOES-8 (located over the equator at 75 deg. W) and GOES-9 (at 135 deg. W) are analyzed using capabilities from the RAMMT Advanced Meteorological Satellite Demonstration and Interpretation System (RAMSDIS), developed by the RAMM Team at the Cooperative Institute for Research in the Atmosphere (CIRA) in Fort Collins, CO.
To proceed to the next “page” in the tutorial (in this case the Table of Contents), always click on the “CONTINUE” button; to go back a “page”, click on “PREVIOUS”. Click on the “ENLARGE/LOOP IMAGERY” button to view the displayed image in its full resolution or as part of a loop, as the case may be. The Table of Contents (TOC), and other “pages” throughout the module, contain hypertext items (highlighted in blue, underlined letters) which can be clicked upon to allow immediate access to topics not necessarily in the order of intended presentation.
On the morning of 31 May 1996, a warm front, which extended eastward from a surface low in southeast Colorado, stopped its northward progression as it merged with a large outflow boundary in central KS. The figure indicates the low and boundary positions at 1215 UTC. Surface temperatures in KS had reached the mid-70s (F) by this time, with surface dewpoints running in the mid- to upper-60s.
The winds in KS were generally from the south-southeast at the surface, veering to southwesterly by 500 mb. The 500 mb flow was dominated by a broad, longwave trough covering most of the western U.S. An embedded, uper-level shortwave was moving from the four corners region into CO. Thunderstorms developed between 1600 and 1700 UTC in front of the shortwave as it crossed over the Rocky Mountains and into eastern CO. At the same time, thunderstorms also developed along the front/outflow boundary in central KS. Both of these thunderstorm areas reported tornadoes.
Between 2300 and 2342 UTC, six F0 tornadoes were reported in eastern CO. One notable tornado, in Kiowa county, had a path-length of 17.5 miles. Eight tornadoes were reported in KS, 2 F1s and 6 F0s; all occurring between 2306 and 2358 UTC.
This module looks at certain aspects of this case to illustrate the wide variety of analysis capabilities afforded by satellite imagery. Discussion begins with the synoptic scale, and progresses down to the sub-synoptic and meso-scales.
The GOES I-M system is a basic element of U.S. weather monitoring and forecast operations and is a key element of the National Oceanic and Atmospheric Administration’s (NOAA’s) National Weather Service operations and modernization program. Spacecraft and ground-based systems work together to accomplish the GOES mission of providing weather imagery and quantitative sounding data. They provide a continuous and reliable stream of environmental information used for weather forecasting and related services. The new, I-M series of GOES satellites provides significant improvements over the previous GOES system in weather imagery and atmospheric sounding information.
[For additional information on these improvements and their meteorological applications, see the related “Introduction to the GOES Imager” . For the more advanced satellite meteorologists, the RAMM Team’s GOES 3.9 µm Tutorial may be of interest.]
The operational GOES satellites are owned and operated by NOAA. The National Aeronautics and Space Administration (NASA) manages the design, development and launch of the spacecraft. Once the satellite is launched, placed in orbit and checked out, NOAA assumes responsibility for the command and control, data receipt and product generation and distribution. NOAA assigns a letter to a satellite’s name before it is launched (e.g., I, J, K, L and M), and replaces that letter with a number once the satellite has achieved orbit. Hence the first satellite of this series, GOES-8 our operational “eastern” satellite, was first referred to as GOES-I; GOES-9, our operational “western” satellite, was first designated GOES-J; and GOES-10, presently in “stand-by” status at 105 degrees west longitude, was known as GOES-K.
The GOES I-M mission is expected to run from the mid-1990s into the first decade of the 21st century. Each element of the mission has been designed to meet all in-orbit performance requirements for at least five years.
The National Oceanic and Atmospheric Administration (NOAA) and the Colorado State University (CSU) established the Cooperative Institute for Research in the Atmosphere (CIRA) in 1980. Among NOAA’s contributions to this joint research program was the transfer of a small cadre of federal scientists to CSU’s Foothills Campus in Fort Collins, CO. Over the years this group has been fortified by a growing number of CSU employed scientists-meteorologists. This organization is now known as the Regional and Mesoscale Meteorology (RAMM) Team of NOAA and CIRA, and numbers over a dozen scientists and technicians.
Activities at RAMM-CIRA are concentrated upon developing a thorough understanding of atmospheric processes, particularly those which initiate and evolve on the meso- or regional-scale. A primary goal is effectively transferring important discoveries and analytical tools to operational forecasters and fellow environmental research scientists. Information from a wide variety of meteorological satellites, generously supplemented by many complementary environmental data collection resources, are utilized to promote these objectives.
Meteorological satellite data has become quite complex with new combinations of standard imagery and better signal resolution. These higher-quality data, more than ever before, require enhancement because the human eye can distinguish only a limited number of shades. Earlier series of GOES satellites used gray scales and “repeat” gray scales to show more detail at cloud tops. Other enhancements can be used to focus the user’s attention on features such as surface temperatures and water versus ice clouds. This section of the tutorial will introduce aspects of enhancements developed by the RAMM Team in their use of satellite imagery.
GOES imagery is available to users in either 10-bit GOES Variable (GVAR) counts or 8-bit brightness (or display) counts. The process of converting raw satellite values into 10-bit values is called calibration and is explained at: http://www.nnic.noaa.gov/SOCC/goescal.html.
Traditionally, infrared images are enhanced in the opposite sense compared with visible imagery. In visible imagery, the lower energy portions are displayed as dark and the higher energy portions as bright. For infrared imagery, the opposite is true. This is done so that clouds which are highly reflective in the visible imagery and cold in the infrared imagery appear bright in both. It has also been determined that color enhancements are often the best way to highlight important features in the infrared and water vapor imagery.
The starting point for an explanation of the enhancement of each channel in this section of the tutorial are images in 8-bit (0-255) counts. Below are paths to examples of image enhancements used for each of the GOES Imager channels 1 through 5:
Visible enhancements rely primarily on improving the contrast among the various shades of the 8-bit (0-255) gray scale. Four images (one un-enhanced and three different enhancements) are given to show various ways to enhance visible channel imagery.
Un-enhanced VIS (ch.-1)
This un-enhanced visible image is available in 8-bit (0-255) display counts. This is the starting point for the three visible channel enhancements that will follow. An 8-bit count value is written above the gray bar on the bottom of all images.
Contrast-stretched VIS (ch.-1)
One can enhance the base VIS image by contrast stretching. This method determines the minimum and maximum counts in the image and stretches the gray scale to cover the range from minimum to maximum count, eliminating counts not used in the base image. The gray bar at the bottom of this image indicates that portions of the un-enhanced 8-bit scale (both low and high values) are no longer used. In this case the ramp of gray shades lies between 8-bit counts of 37 and 220, since no values outside this range exist in the un-enhanced image.
Histogram-stretched VIS (ch.-1)
Another means of enhancing VIS imagery is by histogram stretching. In this method a histogram of the image counts is made to determine the enhancement to be applied. The histogram is then divided into three sections: brightness counts which have a value below a dark-end threshold (some value greater than the minimum count); brightness counts which have a value above a bright-end threshold (some value less than the maximum count); and brightness counts lying between the dark and bright thresholds. Brightness counts which lie below the dark threshold are assigned a new count of 0. Similarly, counts which lie above the bright threshold are assigned a value of 255. The remaining values between the dark and bright threshold are assigned new counts between 0 and 255, according to a linear stretch. In this case the un-enhanced counts between 50 and 161 (compared to 37 and 220 in contrast stretching) are stretched to cover the 0-255 range displayed, using a single ramp of gray shades.
Histogram stretching eliminates low and high counts which are infrequently used, unlike contrast stretching. The remaining counts are stretched slightly more, thereby further enhancing some of the image features.
Fixed table enhancement of VIS (ch.-1)
Finally, one can apply a fixed-range enhancement table to the base image. In this example, un-enhanced counts between a fixed minimum of 10 and a fixed maximum of 212 are stretched to cover the full gray scale range of 0 to 255. Values outside the original range are fixed at 0 and 255 for the lower and higher values, respectively. This enhancement may not work well for visible images where significant numbers mof values fall outside the fixed minimum and maximum counts.
GOES shortwave infrared data are unique in that most of the energy sensed at that wavelength (3.9 um) is emitted from the earth’s surface and clouds. During the daytime, however, some of that energy is composed of solar radiation reflected from the earth’s surface and clouds, with the amount reflected depending on the albedo at 3.9 um. Three images (one un-enhanced and two with different enhancements) are presented to show steps to enhance imagery in this portion of the spectrum.
Un-enhanced IR (ch.-2, 3.9 um)
This un-enhanced GOES channel-2 (3.9 um) image is available in 8-bit (0-255) display counts. This is the starting point for the two enhancements that follow. The 8-bit count values have been converted into temperatures which are written above the gray bar on the bottom of all images. The temperature scale is bi-linear with a 0.5 degree C per count resolution for temperatures warmer than -31 degrees C [242 K], and 1 degree C per count resolution for temperatures below that value. This temperature scale is the standard Look Up Table (LUT) applied to all GOES infrared channels except those for the water vapor channel available for AWIPS distribution.
Inverted IR (ch.-2, 3.9 um)
The original channel-2 image can be enhanced by inverting its gray shades. Some users of this shortwave infrared channel prefer this view since the added solar component often makes reflective cloud tops look dark in a non-inverted display. An inverted enhancement returns the thickest (and generally more reflective) cloud tops to brighter shades and the thinner (less reflective) cloud tops to darker shades.
Fixed-table IR (ch.-2, 3.9 um)
Another way to enhance the channel-2 image is by the use of a fixed color enhancement table. In this image the inverted gray shades in the previous image are colored from white to black, and then to blue. The different shades of blue help the viewer distinguish variations in cloud reflectivity. Generally, the more reflective clouds have warmer effective temperatures due to the added reflected component, and the less reflective clouds have cooler effective temperatures due to a lack of that reflected component of radiation. This enhancement helps distinguish water (more reflective) clouds, which are colored very light gray, from ice (less reflective) clouds, which are colored black or turquoise.
GOES water vapor (channel-3) data are also special since little if any of the energy is emitted from the earth, but from middle-level atmospheric layers, except for the cloudy portions of the image where the energy comes from the cloud tops. Four images (one un-enhanced and three with different enhancements) are provided to demonstrate ways to enhance imagery in this portion of the spectrum. Click on the “CONTINUE” button below to see them; click on the “PREVIOUS” button to go back to the main enhancement page.
But first, users must remember that water vapor is the main absorbing constituent in this channel, and that variations in water vapor are detected through variations in the effective temperature measured in this channel. The effective temperatures are of the atmospheric layer which is most radiatively active, depending on the total amount of water vapor in the scene; basically the temperature of the layer from which the maximum radiation comes. In areas with warmer effective temperatures the radiation comes from lower layers of the atmosphere (generally those areas are drier than areas with colder effective temperatures). But this is only true if the actual temperature profiles are the same. The moisture effect is intertwined with real temperature variations as well, so that this channel sees both real temperature variations and variations in temperature due to changes in the amount of water vapor in the vertical column. Also, this channel, being infrared, does not see through clouds and the coldest temperatures observed are those from cloud tops.
GOES longwave infrared (channels 4 and 5) data are the easiest to understand since almost all of the energy in this portion of the spectrum is emitted from either the earth or cloud tops. There is little atmospheric absorption or emission in either of these longwave infrared window channels, with only slightly more absorption in channel-5 than in channel-4. These two infrared window channels are generally treated similarly and differences between the channels are small. Two channel-4 images (one un-enhanced and one enhanced) are given to show how to enhance imagery in this portion of the spectrum.
Un-enhanced ch. 4 IR (long-wave)
The un-enhanced GOES channel-4 image’s 8-bit count values have been converted into temperatures which are written above the gray bar on the bottom of all images. The temperature scale is bi-linear with a 0.5 degree C per count resolution for temperatures warmer than -31 degrees C [242 K], and 1 degree C per count resolution for temperatures below that value. This temperature scale is the standard Look Up Table (LUT) applied to all GOES infrared channels except those for the GOES water vapor channel available for AWIPS distribution. See: http://www.cira.colostate.edu/RAMM/cal-val/wvgini.htm
Fixed color table ch. 4 IR (long-wave)
The original image may be enhanced by the use of a fixed color enhancement table. In this image the cloud tops colder than -31 degrees C [242 K] are treated with several color variations, starting at yellow to magenta to cyan to green. Then at about -71 degrees C [202 K] the gray shades begin again. Then below -78 degrees C [195 K] the enhancement color changes to blue, for temperatures which are seldom seen except for cloud tops in the tropics. The examples in this tutorial use the same colors to highlight temperatures below about -45 C [228 K] for both channel-3 (water vapor) and the long-wave channels, -4 and -5. The goal is to be able to easily compare cold cloud tops in these wavelengths.
Other color variations for cloud tops can be used, but the idea is to vary the colors so that any given color represents only a small variation in temperature. This effectively stretches the ability to see variations in cloud top structure. And the addition of distinctly different colors for the most extreme cloud tops or overshooting tops helps to easily identify these cloud structures.
This water vapor image was taken from GOES-9 at 1200 UTC on 31 May 1996. The water vapor channel on the GOES Imager is centered at 6.7 um, which responds to water vapor at middle and upper layers of the atmosphere. Although the pressure level of maximum response varies somewhat with the atmospheric profile, it is typically near 400 mb. Therefore, this channel is useful for analyzing general flow patterns, including upper-level lows and highs, jet streams, shortwaves and areas of upper-level subsidence.
This image is an excellent example of how an upper-level shortwave trough appears in the water vapor channel. Note the comma-shaped feature over the four corners region. The white and purple pixels (see the loop) in western CO are associated with the clouds caused by the upward vertical motion on the northeast side of the shortwave.
Upward vertical motion is a cooling process which can create a steeper lapse rate, which is favorable for thunderstorm formation. The lifting ahead of the shortwave also appears to have aided in the initiation of thunderstorms (one of which was tornadic) in eastern CO later in the day.
This is the 1200 UTC GOES-9 longwave infrared image; it is at the same time, resolution, and projection as the water vapor image seen in the previous example (and available here again for comparison purposes). The longwave infrared channel is centered at 10.7 microns, a wavelength at which none of the earth’s atmospheric gases absorb very well. Therefore, this channel will be able to sense the earth’s surface and clouds. In this image, note the warm (low), light gray clouds over the Pacific Ocean to the southwest of CA and the Baja Peninsula. These clouds are not seen in the corresponding water vapor image because the middle- and upper-level water vapor is obscuring them.
10.7 um Image
On the other hand, because it is insensitive to atmospheric gases, the 10.7 um image gives no indication of the upper-level shortwave other than the clouds in western CO; the water vapor image, however, shows the shortwave much more clearly.
6.7 um Water Vapor Image
The color tables applied to these two examples are the same for brightness temperatures below -45.2C. Because there is little water vapor attenuation, even at 6.7 um, at the upper levels of the atmosphere, high, thick clouds will have the same colors in these two channels. An example of this is the thunderstorm in TX.
Thin cirrus clouds will have a different appearance in these two channels since the signal at 10.7 um is warmer than at 6.7um, due to a larger radiance component originating from below the cloud. An example of this can be seen in the clouds over northern Alberta and northern Saskatchewan.
Comparisons between channels can be very important in understanding both the cloud and surface features as sensed by GOES.
This GOES-8 sub-synoptic scale water vapor image depicts the same shortwave discussed previously with the GOES-9 large-scale imagery. Because of the different viewing angle, the shortwave is more difficult to see in the GOES-8 imagery, although the thunderstorms it helped initiate are clearly visible. Since this channel is sensitive to mid- and upper-level moisture, only convection of significant depth is seen.
Typically, water vapor imagery is used to monitor synoptic-scale flow patterns, circulations and jet streams. However, mesoscale influences may also appear in the imagery. The thunderstorm in east-central CO appears to have a darker area to the southwest of the bright cloud top, suggesting storm-scale sinking and rising motions, respectively.
10.7 um Image
The corresponding sub-synoptic-scale 10.7 um image, as was seen in the large-scale example, provides estimates of cloud and surface skin temperatures. A forecaster can use cloud top brightness temperature to compare with a local sounding to get an estimate of cloud top height. This estimate works best for thick clouds; it should not be used for thin clouds since the signal received by the satellite for thin clouds is a combination of radiance from below the cloud as well as from the cloud, resulting in a brightness temperature that is warmer than the actual temperature of the cloud.
Denver 12Z Sounding
The small, light blue region of clouds in the thunderstorm in eastern CO can be used as an example. This particular color matches a brightness temperature of -44C, as seen on the color bar at the bottom of the image. When compared with the 1200 UTC sounding from Denver, CO, this temperature corresponds to a height of 300 mb, roughly 30,000 ft (10,000 m). Remember that thick cirrus can be distinguished from thin cirrus by using the fog product (about which more later), or by using a combination of the water vapor imagery and the 10.7 um imagery.
Because the reflectance of solar radiation at 3.9 um differs between liquid water clouds and solid water (ice) clouds, images at this wavelength may be used to distinguish cloud phase. With the color enhancement used in this image, ice clouds are black or a dull turquoise. Liquid water clouds are difficult to distinguish from the ground by this channel during the summer months and are better resolved by using the multispectral reflectivity product (to be discussed later). Examples of this are the liquid water clouds on the NE/KS border, which are not discernible in the 3.9 um image, but they are evident in the reflectivity product image.
For more information about the GOES 3.9 um channel and its applications, see the tutorial on the subject, The GOES 3.9 um Channel.
VIS Image at 1815 UTC
This 2 km GOES-8 visible image, at 1815 UTC on 31 May 1996, was recorded prior to the onset of thunderstorms in KS, although one thunderstorm had already developed in CO. Because of its high spatial resolution, satellite imagery is useful for accurately locating boundaries that may go undetected by the coarser resolutions of the surface and upper air observation networks. Boundaries which should be noted include separations between clear and cloudy regions, separations between different types of clouds, as well as fronts and other convergence lines.
VIS Image at 2145 UTC
The image overlay delineates some of the features mentioned above. The blue line in NE lies along a cold front. The yellow, green and light blue lines mark inferred surface convergence lines. The red line marks the outflow boundary created the previous night, as will be discussed with the fog product. Each of these lines had thunderstorm development on or near them. The visible image from 2145 UTC shows the relationship between the boundaries delineated and the thunderstorms which had developed by that time. The loop shows the development of the thunderstorms from 1815 to 2145 UTC.
This is a 1 km GOES-8 visible image showing the thunderstorms on 31 May 1996 at 2215 UTC. The three marked towns all reported tornadoes in their vicinity within the time frame of the loop as follows: Sheridan Lake, CO (and into extreme western KS), between 2300 – 2350 UTC; Rolla, KS between 2252 – 2358 UTC and Ness City, KS, between 2309-2345 UTC. The cell in the TX panhandle produced tornadoes shortly after 0015 UTC.
Fine detail can be seen in all of the thunderstorm anvils, including overshooting tops and downstream cirrus plumes. The flanking lines of the Sheridan Lake and Rolla storms are visible since the satellite viewing angle sees beneath the anvils. Different appearances and patterns of the lower clouds can give an indication of the low-level flow and stability.
In particular, look at the north/south oriented lines of clouds in OK and KS. Here the cloud lines are oriented parallel to the low-level winds. Cloud lines oriented in this manner are known as cloud streets, and are indicative of a relatively unstable air mass.
In the section dealing with large-scale 10.7 um imagery, it was mentioned that low clouds can be seen at that wavelength, with some exceptions. This example shows low clouds that could not be detected using 10.7um imagery alone. This failure in detection occurs because, during the night, radiational cooling of the surface reduces the thermal contrast between the ground and the low-level clouds. In such cases, low-level clouds can be indistinguishable from the ground at 10.7 um. This 10.7 um image shows a night-time mesoscale convective system (MCS) along the NE-KS border. MCSs produce low-level outflow which can often be inferred by a line of low-level clouds that develop along its edge, or by stratiform clouds that develop above the cold, outflow air. In this image, no such cloudiness is visible.
10.7 um Image
Outflow clouds can often be seen at night using a multispectral image product, made by subtracting the brightness temperatures at 3.9 um from those at 10.7 um. The result, known as the fog product, relies on the fact that liquid water clouds (which include most clouds associated with low-level thunderstorm outflow) have an emissivity at 3.9 um that is less than that at 10.7 um, thereby making the fog product show a positive difference (colored white in this enhancement).
Although this product’s main use is in the detection of low-level, liquid water clouds at night, it is also useful for discriminating between thin and thick cirrus. The 3.9 um channel is more sensitive to radiation passing through thin cirrus than the 10.7 um channel. Here the fog product indicates a negative difference between 10.7 and 3.9 um. The color table used shows these negative differences as dark. For very cold clouds, such as the thunderstorm tops in NE and SD, the signal at 3.9 um becomes very noisy, with the multispectral image product dominated by that noise. In this case, the fog product takes on a black and white, speckled appearance.
As the MCS moved east, it left behind low-level cloudiness associated with its outflow over northern KS, and set up a boundary which played a role in the initiation of the severe weather in KS later on May 31.
For more information on the fog product, see the RAMMTeam’s tutorial on the GOES 3.9 um channel at: http://www.cira.colostate.edu/ramm/goes39/cover.htm
As just discussed, the fog product can be used to distinguish cloud phase at night. A multispectral image, called the reflectivity product, can do the same during the day. This product also uses 3.9 and 10.7 um images. Daytime 3.9 um radiation received by the satellite is a combination of earth-emitted and solar-reflected components. The 10.7 um signal contains only earth-emitted radiation. The 10.7 um information is used to remove the 3.9 um emitted component, leaving only that which has been reflected by the sun. Because liquid water clouds are more reflective than ice clouds at 3.9 um, this product may be used for daytime cloud phase determination. A more complete description of the reflectivity product and its applications may be found in the RAMM Teams’s 3.9 um tutorial. (Make sure you “bookmark” this location if you wish to return here from the tutorial!)
With the enhancement shown, the reflectivity product displays liquid water clouds as bright, and clouds comprised of ice crystals as dark. Cloud phase differentiation is important since lightning will not be present unless glaciation has occurred.
If you came to this page from the “Sub-synoptic-scale IR (3.9 um) Imagery” presentation, and wish to return there, click here.
10.7 um Image
Many types of weather analyses using satellite images focus on the qualitative nature of the images (e.g., signatures of short waves in the water vapor imagery and cloud patterns). There are times, however, when the quantitative information of the images, i.e. the brightness temperatures of the pixels, becomes useful. Examples of this use are: locating forest fires and volcanic eruptions in the 3.9 um channel, and identifying the cooling rate and height of overshooting cloud tops for severe storms in the 10.7um channel. As another example of using quantitative information from satellite images, this page shows how to estimate cloud height using the 10.7 um channel imagery and a sounding. The underlying assumption behind estimating cloud height is that the temperature of the cloud top, as sensed by the satellite at 10.7 um, is the same as the environmental temperature at cloud top. A sounding is then used to match the brightness temperature of the cloud to the pressure with that same temperature.
The focus of this example is a thunderstorm in north-central KS (yellow box). The minimum temperature at 10.7 um within the yellow box is -60 C (see color bar at the base of the image). The minimum temperature is the best estimate for the temperature of the highest cloud because the cloud element emitting the minimum temperature is most likely optically thick, minimizing the risk of contaminating the cloud top emission with radiation from below the cloud. Referring to the Topeka sounding, valid 2 hours and 15 min. before the satellite image, -60 C occurs near 125 mb. The conclusion is that the pressure at the storm top is approximately 125 mb, clearly overshooting the tropopause.
Satellite imagery sequences are usually presented in an earth-relative reference frame, wherein atmospheric features move with respect to the earth’s surface. However, satellite imagery animated in a storm-relative sense can offer additional insight into the nature of a moving feature (the term “storm-relative” includes any moving feature, not just storms). In a storm-relative loop, a constant motion vector is calculated from the individual earth-relative images. This vector is used, along with the image times, to reregister the images such that the feature which was moving relative to the earth remains stationary.
The first loop is a series of earth-relative images of the severe and tornadic thunderstorms over western KS from 2230 to 2325 UTC, on 31 May 1996. Apart from the line of thunderstorms itself, other features which may be important to storm evolution are visible. Some of these are the southerly low-level flow to the south of the line of thunderstorms (as seen by the motion of the cumulus clouds) and cloud top features such as overshooting tops and cirrus wakes.
The loop animated in the storm-relative sense is centered on a storm which produced a tornado near Ness City, KS around 2300 UTC. Storm-relative loops may make additional features stand out that are not immediately seen in earth-relative animation. Some examples seen here include an apparent circulation near the overshooting top, a low-level outflow boundary exiting the storm to the southwest, a region of rapidly developing towers beneath the anvil on the southeast flank of the storm and cirrus dissipation southwest of the growing anvil. Additionally, it can be seen from the motion of the cumulus clouds to the south of the thunderstorm line, that the low-level flow relative to the storm is southeasterly. So, although the low-level winds (as seen in the earth-relative loop) are from the south, when the motion of the storm is accounted for, it can be seen that the airmass feeding the storm is located to its southeast.
It is useful to be able to measure the velocity of a meteorological feature. In satellite imagery such measurements are possible when cloud features persist over multiple images. This example is based on an 8-frame loop showing the thunderstorm which produced tornadoes in eastern CO and western KS. In most cases, storm motion is complex. If one tracks the overshooting top, the resultant velocity turns out to be from 32 degrees at 12 kts. In this case, the storm is backbuilding to the southwest along the flanking line. However the overall cluster of storms is moving southeastward, from about 305 degrees.
In tornadic storms the most active area is usually where the flanking line joins the main storm (red dot in figure). When this intersection point is tracked, the resultant motion is from 326 degrees at 31 kts. That is, the propogation velocity of that point is much more rapid than certain individual storm features. Of course, these velocity measurements are estimates. Variations in cloud velocity and morphology will introduce uncertainty in the measurement. Other examples of features whose velocities may be estimated by satellite are shortwaves, fog/stratus cloud decks, and fronts.
Once a feature can be assigned a velocity, it is then possible to calculate the arrival time of that feature at a particular point, or to extrapolate the feature’s location ahead over a given amount of time.
In situ surface and upper-air observations are an integral part of weather analysis. These examples are from the 31 May 1996 convective outbreak in KS, northern TX and eastern CO. The GOES-9 visible image is overlayed with contours of surface dewpoint temperature and wind barbs for the surface wind. The dewpoint contours and wind barbs help identify the north-south dryline in western and northwestern TX where convection is active. The 6.7 um water vapor image from GOES-8 contains contours of 500 mb heights. The contours show a shortwave in the TX panhandle, near the active convection. Since shortwaves are often visible in loops of 6.7 um images, upper-air analyses can be checked against the satellite imagery for features that are not well represented in the NWS and NCEP analyses.
VIS image with SFC temperatures and winds
6.7 um water vapor with 500 mb heights
Earlier in the tutorial, loops of a shortwave moving over the Rocky Mountains, which most likely aided in the initiation of thunderstorms in eastern CO and the OK panhandle, were shown. This shortwave, as seen in the GOES-8 loop, provides an example of the advantages of using information from more than one goestationary satellite. The GOES-9 loop clearly shows the hourly progression of the shortwave from 1330 to 2030 UTC, on 31 May 1996. The GOES-8 loop contains images taken at roughly the same time as the GOES-9 images. From this vantage point the shortwave is not nearly as apparent as it is from GOES-9. Whenever practical, use GOES-8 and GOES-9 imagery when viewing an area or feature of particular interest.
The majority of the loops in this tutorial were created from satellite images taken every fifteen minutes, the normal frequency with which GOES-8 and GOES-9 scan the continental U.S. On 31 May 1996, however, the GOES-8 satellite was operated in a special scanning mode which allowed data over a portion of the U.S. (in this case, the KS region) to be scanned at frequencies of up to one image every thirty seconds. The loop shown here contains thirty-second interval imagery over the times from 2304:12 to 2311:16 UTC, which includes times at which the Ness City, KS and Sheridan Lake, CO tornadoes were reported. The thunderstorms associated with these tornadoes are in the storm in eastern CO and in the second storm from the right in KS. This was the first time ever that imagery was taken once every thirty seconds over tornadic thunderstorms. Even at this rapid interval, much change can be seen in the cloud field from frame-to-frame, particularly at the thunderstorm tops. Try viewing the imagery at different speeds as you study this loop.
This completes the topics covered in the tutorial, please go back to the TOC or to any other site of your choice. Thank you for your interest in the subject matter.