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Pyrocumulonimbus cloud in Alberta

10-minute Full Disk sector GOES-18 (GOES-West) Fire Temperature RGB, “Clean” Infrared Window (10.3 µm), Cloud Top Height and Cloud Top Temperature (above) showed the formation of a pyrocumulonimbus (pyroCb) cloud produced by a wildfire southeast of Edson, Alberta (station identifier CYET) on 04 May 2023. Cloud-top 10.3 µm infrared brightness temperatures were as cold as -61ºC, while the multispectral... Read More

GOES-18 Fire Temperature RGB (top left), “Clean” Infrared Window (10.3 µm, top right), Cloud Top Height (bottom left) and Cloud Top Temperature (bottom right) [click to play animated GIF | MP4]

10-minute Full Disk sector GOES-18 (GOES-West) Fire Temperature RGB, “Clean” Infrared Window (10.3 µm), Cloud Top Height and Cloud Top Temperature (above) showed the formation of a pyrocumulonimbus (pyroCb) cloud produced by a wildfire southeast of Edson, Alberta (station identifier CYET) on 04 May 2023. Cloud-top 10.3 µm infrared brightness temperatures were as cold as -61ºC, while the multispectral Cloud Top Temperature derived product revealed values as cold as -66ºC. Cloud Top Height values reached 39,000 feet. It should be noted that these Full Disk sector images and products are (unfortunately) provided at a reduced spatial resolution in AWIPS.

A plot of 0000 UTC rawinsonde data from Edmonton, Alberta (below) indicated that the pyroCb cloud-top temperatures colder than -60ºC were close to the tropopause temperature, suggesting that some smoke and cloud material may have been injected into the lower stratosphere.

Plot of 0000 UTC rawinsonde data from Edmonton, Alberta [click to enlarge]

GOES-18 True Color RGB images from the CSPP GeoSphere site are shown below.

GOES-18 True Color RGB images [click to play animated GIF | MP4]

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Lightning on the Laramie Mountains

A long-wave diffluent trough centered over California and Nevada was helping to force benign convection along the Laramie Mountains, which run roughly from Laramie to Casper, Wyoming. The ProbSevere LightningCast model, which uses AI and GOES-R data to predict the probability of lightning in the next hour, was able to highlight... Read More

A long-wave diffluent trough centered over California and Nevada was helping to force benign convection along the Laramie Mountains, which run roughly from Laramie to Casper, Wyoming.

The ProbSevere LightningCast model, which uses AI and GOES-R data to predict the probability of lightning in the next hour, was able to highlight this convection before the first flashes occurred. Lead time to the initial flashes ranged from 10 to 30 minutes, measured from the 25% probability of lightning contour.

Figure 1: Animation of ProbSevere LightningCast probabilities of lightning (blue=10%, cyan=25%; green=50%; magenta=75%), GOES-16 GLM flash-extent density (blue foreground pixels), and GOES-16 ABI day land cloud convection RGB, all along the Laramie Mountains in Wyoming.

One interesting feature in this animation is the packing of the probability contours. The storms are moving generally from south to north. The contours are much more packed along the south and west edges of the region of convection (i.e., where convection is moving away from), while they are more diffuse along the north and east edges (i.e., where the convection is moving towards. This indicates that the model is (at least in part) accounting for the motion of the storms in the next hour. LightningCast expects the storms to move north, which is what they are indeed doing. This was a very surprising result, since LightningCast was trained with samples that used only one snapshot or timestamp of satellite data.

From GOES-West (GOES-18) in Figure 2, we can see the same effect, though perhaps not as pronounced. But keep in mind the satellite viewing geometry is much different in Wyoming from GOES-East versus GOES-West. And while next-hour motion of storms is not always well predicted, it is nevertheless encouraging that LightningCast is able to discern motion at times from only one snapshot of data. Forecasters often look at animations of satellite data to make nowcasts, and we believe that training the model with “videos” of data rather than images will further enhance its ability to project lightning threats in the near future. One downside is that this greatly increases the complexity and computational cost to create such a model. However, recent developments in AI/ML modeling show that training with video imagery (and predicting video imagery) is becoming more feasible.

Figure 2: ProbSevere LightningCast contours computed with GOES-18 data. Blue foreground pixels are GOES-18 GLM flash-extent density and background is GOES-18 ABI day cloud phase distinction RGB.

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Polar Hyperspectral Sounding data in a model simulation: Virginia Beach Tornado

The mp4 animation above, downloaded from the CSPP Geosphere site, shows true-color imagery in the 90 minutes surrounding an EF-3 tornado in Virginia Beach, VA shortly before 2200 UTC on 30 April 2023 (CIMSS Satellite Blog post; SPC Storm reports; ProbSevere imagery for the storm is here). Note the overshooting tops moving from southeastern Virginia northeastward to... Read More

GOES-16 True-Color imagery from CSPP Geosphere site, 2051 – 2226 UTC on 30 April 2023

The mp4 animation above, downloaded from the CSPP Geosphere site, shows true-color imagery in the 90 minutes surrounding an EF-3 tornado in Virginia Beach, VA shortly before 2200 UTC on 30 April 2023 (CIMSS Satellite Blog post; SPC Storm reports; ProbSevere imagery for the storm is here). Note the overshooting tops moving from southeastern Virginia northeastward to the extreme southern Delmarva peninsula, likely associated with the strongest thunderstorms. This blog post considers how the addition of Polar Hyperspectral Sounding (PHS) data into a numerical model affected the model simulation of this tornadic event (model output is available here). This PHS modeling system is being demonstrated at the Hazardous Weather Testbed late in May, and in early June (PHS model output was also available at last year’s HWT!)


3-km WRF (initial time: 1600 UTC) estimates of MUCAPE, 5-h forecast valid 2100 UTC (Left) and 6-h forecast valid 2200 UTC (right) on 30 April 2023 (Click to enlarge)

The two-panel image above shows 5- and 6-h MUCAPS distributions from a WRF simulation (with 3-km resolution) initialized with conventional data, and the image below shows 5- and 6-h forecast MUCAPS from a HRRR simulation (with 4-km resolution) that includes as part of its assimilation cycle temperature and moisture fields that include information from Polar Hyperspectral Soundings (in this case, from the morning overpasses from IASI on Metop-B and Metop-C). Compare the fields above and below. The HRRR simulation that includes the influence of Polar Hyperspectral Sounding data leads to a MUCAPE field that extends more seamlessly north towards the Delmarva peninsula.

4-km HRRR (initial time: 1600 UTC) estimates of MUCAPE, 5-h forecast valid 2100 UTC (Left) and 6-h forecast valid 2200 UTC (right) on 30 April 2023 (Click to enlarge)

The PHS simulation from 1700 UTC, below, shows good consistency with the 1600 UTC run shown above. In particular, both show the most unstable CAPE lingering over extreme southeastern Virginia at 2100 UTC and offshore at 2200 UTC.

4-km HRRR (initial time: 1700 UTC) estimates of MUCAPE, 4-h forecast valid 2100 UTC (Left) and 5-h forecast valid 2200 UTC (right) on 30 April 2023 (Click to enlarge)

HRRR data was also used to compute Significant Tornado Parameter (STP), and the animation below shows parameter forecasts (from the model initialized at 1700 UTC) for 1900, 2000, 2100 and 2200 UTC.

Significant Tornado Parameter from a HRRR simulation intialized at 1700 UTC with PHS data; forecasts valid hourly from 1900-2200 UTC on 30 April 2023 (Click to enlarge)

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Record snowpack eases long-term water woes while raising short-term flooding risks

From coastal California to the Sierra Nevadas to the Rocky Mountains, precipitation totals this past winter reached historically high levels, especially when it comes to snowfall and mountain snowpack. The health of western watersheds in terms of mountain snow is readily apparent in VIIRS True and False Color imagery.  ... Read More

From coastal California to the Sierra Nevadas to the Rocky Mountains, precipitation totals this past winter reached historically high levels, especially when it comes to snowfall and mountain snowpack. The health of western watersheds in terms of mountain snow is readily apparent in VIIRS True and False Color imagery.  

NOAA-20 VIIRS True Color image from April 29, 2023
NOAA-20 VIIRS False Color image from April 29, 2023

Significant drought relief occurred over the winter months as well, as seen in these graphics from the U.S. Drought Monitor comparing April 2023 to November 2022. Atmospheric Rivers played a huge part in the turn-around. California is nearly drought free!

And according to the USDA, many snowpack levels were still at or above 200% as of May 1st. For states like Utah, where 95% of municipal water comes from melting snow, this is great news.

It seemed somewhat intuitive that Lake Powell and Lake Mead, two of the largest reservoirs out west, would benefit from the wet winter.  Now a recent report from the Bureau of Reclamation confirms this scenario. Record snowpack in Utah, Colorado and Wyoming are expected to boost lake levels with inflow (melting snow) projected to be 177% of average during the April-July runoff period. Graphs included in the report (below) show rebounding lake levels benefiting from the historically high snowpack through 2024. But they also show that lake levels will likely remain below historical averages.

Meanwhile in the short term the National Weather Service has issued numerous Flood Watches, Advisories and Warnings from the Canada-U.S. border to southern Utah due to rapidly melting snow. The water is freezing cold rushing in high, fast flows. Along with avoiding flooded areas, the NWS is advising people to stay away from river banks where erosion could result in sudden surprise submersion and nearly instant hypothermia.

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