Through breaks in the cloud cover, GOES-16 (GOES-East) Day Cloud Phase Distinction Red-Green-Blue (RGB) images on 30 September 2019 (above) showed the bright green signature of fresh snow cover across northern Montana and southern Alberta in the wake of a record-setting snowfall event that occurred during the previous 2-3 days (NWS Great Falls summary). Note that the surface air temperatures over the areas of fresh snow cover only rose into the upper 20s and low 30s F, in contrast to 40s F in adjacent areas with minimal or no snow cover — in fact, many locations set daily record low maximum temperatures.

New Weather Graphic Issued: Snow Reports. More info at https://t.co/9noRXI71Sk. #mtwx pic.twitter.com/WlJ5aC4JIy

— NWS Great Falls (@NWSGreatFalls) September 30, 2019

The 36″ of snow at Galata 16SW, MT, Coop station on Sep 29, 2019, is a North America calendar day record for AN OFFICIAL CLIMATE STATION in September. Baseline, Alberta, had 31.9″ on 9/14/2006 and Cokedale, MT, had 29″ on 9/21/1895. More data may come in tomorrow. @NWSGreatFalls pic.twitter.com/69C7EHxt9b

— Brian Brettschneider (@Climatologist49) October 1, 2019

GOES-16 Mid-level Water Vapor (6.9 µm) images from 0001 UTC on 28 September to 0901 UTC on 30 September (below) covered the duration of the winter storm — the circulation of an anomalously-deep mid-tropospheric low over the Pacific Northwest was evident, in addition to a long fetch of middle/high-level moisture from the southwestern US toward Montana. Another notable feature included widespread mountain waves over Colorado beginning on 29 September, which eventually extended downwind over western Nebraska/Kansas; Colorado had a peak wind gust of 81 mph during this event (WPC storm summary).

===== 01 October Update =====

With less cloud cover on 01 October, a comparison of GOES-16 Day Cloud Phase Distinction and Day Snow-Fog RGB images (above) provided a better view of the areal coverage of snow cover. Note that while the Day Cloud Phase Distinction RGB (snow=green) produces “sharper” imagery — since it uses the higher spatial resolution of the 0.64 µm Visible data — the Day Snow-Fog RGB (snow=red) does a better job at highlighting thin supercooled cloud features (shades of white) over snow cover.  The combination of fresh snow cover, light winds and minimal cloudiness allowed Cut Bank to record the coldest official temperature in the US at +1ºF (although a couple of sites unofficially dropped below 0ºF).

In a toggle between GOES-16 Day Cloud Phase Distinction RGB and Topography images (below), note the darker blue gaps in snow cover in Montana and Alberta – with easterly/northeasterly winds during the snow event (Cut Bank | Havre | Great Falls), those areas experienced downslope flow which warmed the boundary layer air and minimized snow accumulation.

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GOES-16 (GOES-East) “Clean” Infrared Window (10.35 µm) images (above) showed Hurricane Lorenzo during the time it intensified to a Category 5 storm around 0130 UTC on 29 September 2019. A plot of the CIMSS Advanced Dvorak Technique (below) indicated a peak intensity estimate of 143 knots from 0220-0820 UTC.

#Lorenzo has strengthened into a Category 5 hurricane at 45°W – the farthest east Category 5 #hurricane in the Atlantic on record. Previous record was Hurricane Hugo in 1989 at 54.6°W. pic.twitter.com/FXGgKFPc3m

— Philip Klotzbach (@philklotzbach) September 29, 2019

 

A toggle between NOAA-20 VIIRS Day/Night Band (0.7 µm) and Infrared Window (11.45 µm) images at 0425 UTC is shown below.

Lorenzo was moving through an environment characterized by low values of deep-layer vertical wind shear (above). In addition, Lorenzo was moving over water having warm Sea Surface Temperatures but only modest Ocean Heat Content (below).

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Researchers from NOAA and UW-CIMSS have developed an experimental model that predicts the “probability of intense convection” inferred from GOES ABI and GLM fields. The model is a convolutional neural network, which carries the assumption that the inputs are images and have spatial context. It is a great tool for image classification.

GOES-16 ABI CH02 reflectance (a visible channel), ABI CH13 brightness temperature (an infrared window channel [IR]), and GLM flash extent density (FED; generated using glmtools),  were used as inputs to the model. The model learned important features that have been traditionally difficult or expensive to code into an algorithm, such as pronounced overshooting tops (OTs), enhanced-V features, thermal couplets, above-anvil cirrus plumes (AACPs), strong brightness temperature gradients, cloud-top divergence, and texture from visible reflectance.

It is hoped that such a model may be able to one day:

• provide earlier notice of developing or decaying intense convection
• provide guidance in regions with no weather radars
• provide a quantitative way to leverage 1-min mesoscale scans
• ultimately improve the accuracy and lead time of severe weather warnings

 

The next example is at a higher satellite viewing angle in western Nebraska, western South Dakota, and eastern Wyoming. The model again does a good job highlighting the strongest areas of storms. It should be noted that not every identified region has severe reports and not every severe report has a probability of intense convection ? 25%, but that there is generally good correspondence between reports and the model probabilities nonetheless.

 

This example is from the Southeast U.S. in more of a low-shear “microburst” environment instead of a high-shear “supercell” environment. You can see that instead of predicting high probabilities for all of the convective storms, the model exhibits the highest probabilities for the storm clusters that at least subjectively look the strongest.

 

This next example from the Central Plains demonstrates the ability to discern decaying convection, as the first storm moves into Missouri and then quickly diminishes in appearance and in probability. It also demonstrates the model’s ability to pick out multiple threat areas within a large cloud mass at night.

 

This is an example using mesoscale scans. Despite not being trained with 1-min data, the model predictions still look very fluid and reasonable. This could be an excellent way for scientists and forecasters to leverage 1-min observations in a quantitative manner.

 

Another example using GOES-East 1-min mesoscale scans. The model generally picks out the strongest portions of a MCS in Illinois and Indiana.

 

At a very high viewing angle, the model predicts probabilities of ?90% for a storm in Arizona. The storm did not generate severe reports, but was warned on by the NWS multiple times.

 

The model is deployed during an early autumn severe weather outbreak.

This example shows the model deployed on 1-min mesoscale scans over very intense thunderstorms in Argentina. It demonstrates that the model is generally applicable to anywhere in the world where advanced imager and GLM-like observations are present.

 

Here, a model was trained without GLM data and deployed for an example in the Alaska Panhandle, where GLM data is not available. This storm prompted a severe thunderstorm warning from the Juneau, AK NWS office. Note the change in the values of the probability contours. The maximum probability for this storm was 36% at 02:43 UTC. In a relative sense, the intense convection probability product could still be useful in unconventional regions, such as Alaska.

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GOES-16 (GOES-East) “Clean” Infrared Window (10.35 µm) images (above) showed Hurricane Lorenzo as it rapidly intensified from a Category 2 storm at 00 UTC to a Category 4 storm by 15 UTC (ADT | SATCON) on 26 September 2019.

A toggle between VIIRS True Color Red-Green-Blue (RGB) and Infrared Window (11.45 µm) images from Suomi NPP and NOAA-20 as viewed using RealEarth (below) provided a detailed view of the eye and eyewall region of Lorenzo at 1542 UTC and 1632 UTC. On the Suomi NPP Infrared image, note the transverse banding northwest of the eye, and a small packet of gravity waves southwest of the eye.

A DMSP-18 SSMIS Microwave (85 GHz) image from the CIMSS Tropical Cyclones site (below) revealed a well-defined eyewall wrapping around the southern, eastern and northern periphery of the eye.

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