VISIT: Meteorological Interpretation Blog

Alaska Heatwave

During mid-June 2025, National Weather Service (NWS) Fairbanks, AK, issued Heat Advisories for the first time over the Alaskan Interior, where above normal temperatures were expected over the region. The newly implemented advisory system for Alaska was a way to help better communicate heat impacts to the general public. An example of a heat advisory issued by NWS Fairbanks, AK, via social media, can be found below.

The prolonged summer heat lead to significant snowmelt and the reduction of existing snow cover over northern Alaska, that included the Brooks Mountain Range. JPSS polar-orbiting satellites observed the snowmelt over the course of two weeks, captured by the VIIRS GeoColor product at 750-m spatial resolution. The daytime animation below shows the reduction of the snow areal extent, while observing fires and smoke, in addition to multiple rounds of convection during the time period. Note, the daytime images were taken each day, between ~19-02Z. Refer to the blue rectangle within the animation.

VIIRS GeoColor observations over northern Alaska from 9-24 June 2025

Another way to view the rapid melting of snow is from the VIIRS Snowmelt RGB at 750-m spatial resolution. The RGB utilizes the VIIRS 1.24 um channel that is not available on GOES satellites, and is highly sensitive to snow properties, such as grain size and relative wetness. In a qualitative way, the VIIRS Snowmelt RGB observes wet/old snow (dark blue colors) over northern Alaska that could lead to increased snowmelt runoff and flood potential. Dry/new snow appears brighter (cyan colors) in the imagery; as the snow ages over time, the imagery reflectance will gradually decrease. Additionally, the RGB differentiates between liquid clouds (white), thick ice clouds (shades of cyan), thin ice clouds (bluish-white) and vegetation (brown/green colors). The VIIRS Snowmelt RGB image comparison below highlights the existing snow cover on 9 June 2025 and the remnant snow cover seen on 17 June 2025.

Comparison of the VIIRS Snowmelt RGB on 9 June and 17 June 2025

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Galvez-Davison Index (GDI) as another tool to forecast convection in North America

June 13th, 2025 by José Manuel Gálvez

The Gálvez-Davison Index (GDI) (Gálvez and Davison, 2016) was developed by José Gálvez and Mike Davison at NOAA’s Weather Prediction Center in 2014, to improve the detection of environments favorable for tropical convection [Online access: https://www.wpc.ncep.noaa.gov/international/gdi/ ].  However, its skill does not only limit to tropical locations. It extends into mid-latitudes during the warm season. This means that it can be used in the United States during the summer, particularly in regions where warm and moist air masses develop.  This short blog uses satellite imagery to show a simple example of (1) how the GDI has skill in parts the United States during the warm season and can be used as a forecasting tool and (2) the importance of incorporating an analysis of atmospheric dynamics to better assess where convection might form or continue.

Like most stability indices, the GDI is not a standalone index and should be analyzed in combination with atmospheric dynamics.  The GDI only describes the static environment where convection might form, given that its calculation does not consider wind information.  Consequently, it only provides information about whether or not atmosphere is capable of hosting convection and rainfall, but it does not really tell the forecaster whether there convection triggering mechanisms will be present. This is where an analysis of flow dynamics will enhance convection forecasts.

Let’s start with a quick description of the Galvez-Davison Index (Figure 1).  The GDI is computed using temperatures and mixing ratios from 4 atmospheric levels: 950, 850, 700 and 500 hPa.  It represents three processes that are summarized in three sub indices:  (1) The CBI describes the availability of heat and moisture in the 950-500 hPa column, (2) the MWI incorporates the impacts of stabilization by warm air masses in the mid-troposphere and (3) the II represents the drying and stabilizing impacts of thermal inversions in the lower and low to mid troposphere.

How to interpret GDI values? A general interpretation diagram is available in Figure 2. The GDI is dimensionless.  Low values relate to shallow convection and very little precipitation, while higher values suggest a higher potential for deep convection. In the color scale we often use, the chances for deep convection generally increase when greens and yellows appear (GDI=15 to 35), and once oranges and reds appear (GDI>35), the potential for deep convection increases. A potential for heavy rain producing convection appears as well when GDI approaches 35, and increases with higher values.

For a quick look into the skill of the GDI in North America, a random day when GDI was high in the United States and convection was present was chosen: 12 June 2025.  Figure 3 summarizes the GDI field circa 18 UTC. This is close to midday or the early afternoon in the United States.  We are using GDI calculated with GFS model data, 18 hour forecast, using the software Wingridds.  According to the figure, deep convection is likely to develop in points A, B and D; and to a minor degree in point C.  A higher chance for deep convection appears in region E, in south-central Mexico, where GDI values are high.

Now let’s look at where the convection was actually forming around 18 UTC using satellite products.  We will use an animation of the 10.3 um channel or one of the longwave IR channels available in GOES-19 presented in Figure 4.

Figure 4. Animation showing the 10.3 um long wave IR band of GOES-19. Source: CIRA Slider.

Thanks to the satellite loop, we can verify that the GDI seems to be capturing the development of deep convection in points A and D. Using this band and the chosen color palette, we can generally interpret the presence of deep convection by oval-shaped areas colored green, yellow, orange and red that are generally growing. These colors represent cold cloud tops, which are present in deep convective cells. The strongest system appears to be located in southeast Texas in point A, while in point D, the satellite signature suggests diurnal convection already developing in Florida. But what is happening in the other points? And what about the deep convection forming in the Rocky mountains, west of point C, while nothing is occurring at point C itself where the GDI is higher? Let’s start answering this by a quick analysis of atmospheric dynamics.

Figure 5 is very similar to Figure 3, but contains additional model information to interpret atmospheric dynamics.

Incorporating an analysis of the structure of the upper flow (yellow streamlines and wind barbs) as well as the low-level flow (black streamlines and barbs) provides an important amount of additional information. It shows that the convection in point A is also being stimulated by a robust upper trough extending from eastern Nebraska across southwest Oklahoma into south-central Texas. Embedded in this large upper trough, a negatively tilted short wave trough structure (light blue dashed line) is propagating northeastwards across east Texas. The term “negatively tilted” trough is generally used to describe a trough that has its warm tier ahead or downstream of its cold tier. This configuration tends to associate with enhanced upper divergence (white contours) and enhances ascent in the column. In the low-levels, the upper trough has induced a surface trough and it associates with enhanced moisture convergence (red contour). Thus according to the model, there is important dynamical forcing in point A. Satellite imagery in Figure 4 supports this, although precise location of the strongest convection does not match what the model suggests with high precision. This partly relates to complex mesoscale structures associated with the mesoscale convective system in southeast Texas. But what is happening in the other points? Let’s dive into the Day Cloud Phase Distinction RGB (Figure 6) to explore convective initiation.

Figure 6. Animation showing the Day Cloud Phase Distinction RGB (JMA color table) available from GOES-19 data. It also contains CIRA’s GLM Group Energy Density product overlaid, to detect thunderstorms. Source: CIRA Slider.

The Day Cloud Phase Distinction RGB is a great tool to detect convective initiation, or the beginning stages of clouds growing vertically into mid and upper portions of the troposphere. Clouds that contain water droplets look cyan in this product. But when convection initiates and clouds grow, they reach higher altitudes where temperatures are colder. As a result, they develop ice. In this RGB, thick ice clouds appear yellow. The animation shows that in points B, E and F, convection is only starting to form. This suggests that the diurnal cycle in these locations could matter. In point C, however, atmospheric dynamics seem to be tampering the impacts of high GDI values. Looking back into the flow analysis presented in Figure 5, region C (eastern Colorado and west Kansas) lies under an upper ridge and in a region where low-level moisture convergence is not enhanced. Low-level divergence could be even present, but it is not plotted. To the west of point C, however, deep convection is rapidly forming along the Rocky Mountains. Figure 5 suggests that this could be responding to enhanced low-level convergence (red contours) and an approaching short wave upper trough marked with a yellow dashed line.

Since we would like to evaluate the role of the diurnal cycle, lets look at the evolution of convection later in the day using a similar product, but covering the 19-23 UTC period (Figure 7).

Figure 7. Similar to Figure 6, but covering the 19-23 UTC period. Source: CIRA Slider.

Once we look at the evolution past 19 UTC, we verify that – indeed – the diurnal cycle of solar heating seems to play a role as a convection trigger. Rising thermals enhanced by solar heating past 19 UTC have the ability to break the cap or convective inhibition region in the lower troposphere, reaching altitudes where they acquire the ability to use high GDI to produce convection. This is especially true in mountainous regions such as Mexico and the Rocky mountains (points G and H), given the enhanced role of diurnal heating in elevated terrain and continental regions. The western location of these mountain ranges also plays a role in the later occurrence of convective initiation. But evaluating the dynamics also matters. In points G and H, added only in Figure 7, enhanced low-level moisture convergence associated with moist diurnal upslope breezes seems to be important. In point B, convection developed in a scattered to isolated manner. Looking at the GDI alone could had been a little misleading. But when considering atmospheric dynamics, the basic analysis that Figure 5 allows suggests that there might be a general lack of significant dynamical forcing. Yet, the confluence of low-level flow in eastern Tennessee apparently resulted in a region of enhanced low-level moisture convergence producing the cluster of scattered thunderstorms in the region.

Note on the Day Cloud Phase Distinction (JMA) Product: A final aspect of the Day Cloud Phase Distinction Product, not related to the GDI, is the change of coloration as the sun sets. The reason is that this product uses reflective bands that depend on solar radiation. However, areas with cold cloud tops are visible throughout the night, but acquire a red coloration. This is caused by the 10.3 um band being included in the red component of the RGB. During nighttime, the green and blue components of the RGB disappear due to the lack of solar radiation. Only cold cloud tops are visible at night, and they look red.

Figure 8. Recipe to code the Day Cloud Phase Distinction RGB. Source: CIRA.

Thank you for reading.

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11-15 May, 2025 Flooding Event in the Mid-Atlantic and Southeast USA

by Sheldon Kusselson

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Manitoba and Ontario Wildfires

Earlier this week, wildfires ignited and rapidly spread throughout the Canadian provinces of eastern Manitoba, and western Ontario, that led to numerous evacuations, several structures destroyed and fatalities. The high temporal resolution of the GOES-19 nighttime and daytime data (every 5-minutes), observed the initiation and the spread of the fires throughout a ~2 day timeframe. The GOES-19 ABI 3.9 um detected the first fire at night, seen in white pixels at ~6Z, 12 May 2025, located northeast of Selkirk, Manitoba. The other fires initiated to the east (and southeast) later that day and into 13 May 2025. Notice that several fires exhibit white pixels (warm brightness temperatures) that then transition to red pixels. The fire hotspots became so intense that the satellite sensor saturated, producing the red pixels. Clouds exhibit colder brightness temperatures and pass over the scene intermittently and can be seen in black and blue-green colors.

GOES-19 ABI 3.9 um from 0541Z, 12 May 2025 to 1000Z, 14 May 2025

On 13 May 2025, a few of the fires exhibited extreme fire behavior that led to rapid fire spread and the development of pyrocumulus (PyroCu) and pyrocumulonimbus clouds (PyroCb). Four overpasses from the JPSS VIIRS Fire Temperature RGB captured the PyroCb from the largest fire in eastern Manitoba, where liquid water clouds are depicted in blue and ice clouds in green. Note, the RGB is mainly used to observe fires in a qualitative way, where warm, very warm, hot, and intense fires are seen in red, orange, yellow, and white pixels, respectively. By 1953Z, 13 May 2025, the most intense fires were seen on the eastern and northern flanks of the two largest fires: the most northern fire that produced the PyroCb and the narrow fire located in western Ontario.

VIIRS Fire Temperature RGB imagery overpasses from 1744Z-1953Z, 13 May 2025

Nighttime visible imagery that is uniquely available on JPSS satellites and not on GOES, highlights the emitted lights from the fires, showing another way to view the areal extent of the fires at 750-m spatial resolution. The imagery also captured the nighttime fire smoke that advects to the north/northeast. Forecasters can utilize the VIIRS Near-Constant Contrast (NCC) product to identify fire smoke at night, since smoke and aerosols are challenging to detect in the infrared channels.

VIIRS NCC during the early morning hours of 13 and 14 May 2025

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ALPW in AWIPS via SBN

As of Spring 2025, the Advected Layer Precipitable Water (ALPW) product became more widely available for NOAA National Weather Service (NWS) forecasters to access and utilize in the Advanced Weather Interactive Processing System – II (AWIPS-II) via Satellite Broadcast Network (SBN). Within the AWIPS interface and menu, forecasters can click on the ‘Satellite’ tab, scroll down to ‘Polar Derived Products Imagery’, then click on ‘Advected Layered Precip Water (ALPW)’ and select one of the following precipitable water layers: surface to 850 hPa, 850 to 700 hPa, 700 to 500 hPa, and 500 to 300 hPa. Note, hectopascals (hPa) are equivalent to millibars (mb) with respect to units of pressure. Refer to the screenshot of the AWIPS menu below.

AWIPS Satellite Submenu to Access ALPW

Forecasters are encouraged to display the ALPW product in AWIPS-II via 4-panel layout to track the moisture plumes horizontally while identifying the vertical distribution of the moisture. The ALPW 4-panel animation below shows the surface to 850mb (top-left), 850-700mb (top-right), 700-500mb (bottom-left) and the 500-300mb (bottom-right) precipitable water layers from 29-30 April 2025. The animation highlights the rich, low level moisture (i.e., surface to 700mb) that migrates from the Gulf into the southern U.S. Within the 700-500mb layer, an upper level low can be identified spinning over the desert southwest, while on the southeast side of the low, an upper-level moisture plume advects to the northeast into the southern states. Sources of the upper-level moisture come from the East Pacific and northern Mexico.

ALPW observations from 5Z, 29 April 2025 to 5Z, 30 April 2025

The moisture plumes aided in the development of heavy precipitation and severe weather that occurred over the region. Refer to the National Water Prediction Service (NWPS) 24-hour precipitation estimates below. Precipitation maxima can be found over southwestern Oklahoma, near the Oklahoma/Texas border.

National Water Prediction Service (NWPS): 24-hour Quantitative Precipitation Estimate (QPE)

12Z, 29 April 2025 to 12Z 30 April 2025

For users that do not have access to an AWIPS system, they can access the ALPW product online via CIRA webpages (i.e., near-real-time datasets here, via CIRA SLIDER, and archived data). The moisture product files can also be found on the AWS NOAA Open Data Dissemination (NODD) webpage. ALPW’s experimental products, Layered Vapor Transport (expressed in kg/m/s) and Percentile Rankings, can be accessed online too.

Users seeking ALPW training on its applications and utility in operations can take the product teletraining session via NOAA CLC, and/or access the session via web-based video.

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