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.

View only this post Read Less

 

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.

View only this post Read Less

1-minute Mesoscale Domain Sector GOES-16 (GOES-East) “Clean” Infrared Window (10.35 µm) images (above) showed the development of severe thunderstorms across parts of the Upper Midwest on 24 September 2019 — these storms produced hail as large as 2.5 inches in diameter in Nebraska, a wind gust to 80 mph in Minnesota and an EF3-rated tornado in Wisconsin (SPC Storm Reports | NWS Twin Cities | NWS La Crosse).

The corresponding 1-minute GOES-16 “Red” Visible (0.64 µm) images leading up to sunset are shown below.

A “probability of intense convection” model was run for this particular event (below).

View only this post Read Less

NOAA-20 has viewed the launch from Tanegashima Island of a Japanese Spacecraft (NASA Blog Coverage; YouTube video, launch is at minute 35 in the video). Brandon Aydlett, NWS Guam, noted the appearance of a very bright spot in the Day Night Band imagery from NOAA-20 at 1602 UTC on 24 September (and a hot spot as well in the infrared imagery shown below). (NOAA-20 and Suomi-NPP data in this blog post were downloaded at the Direct Broadcast Antenna at the Forecast Office in Guam). NOAA-20 Orbital passes (from this site) show an overpass near the island at 1605 UTC; Suomi NPP had a more direct overpass over the island around 1657 UTC. Compare the NOAA-20 image, above, timestamped 1602 UTC, to the Suomi NPP image, below, timestamped at 1654 UTC. The bright signal over Tanegashima at 1602 UTC is missing from the 1654 UTC Suomi NPP imagery.

Infrared Imagery captured the thermal signature of this launch as well. The hot spots in VIIRS imagery are obvious at 1602 UTC from NOAA-20, but not at 1654 UTC from Suomi NPP, at both 3.74 and 11.45, as shown below.

 

Himawari-8 shortwave infrared imagery also captured the launch, with a hot spot in a Japan Sector image at 1605 UTC on 24 September 2019, below.

There is a considerable parallax shift in the NOAA-20 imagery, as the VIIRS instrument is scanning at the limb in the image, and the rocket at the time was very high in the atmosphere. The parallax shift in the Himawari-8 imagery is less noticeable.

View only this post Read Less