Thunderstorms were slowly but surely edging their way dangerously close to Raymond James Stadium in Tampa, FL, on April 2nd. The New York Yankees and Atlanta Braves had just completed a spring training game at the stadium, when two people were struck by lightning in one of the parking lots surrounding the stadium (they were hospitalized but reported to be in stable condition).

ProbSevere LightningCast is an experimental deep-learning model that is running in near-real time at CIMSS. It uses images of GOES-R Advanced Baseline Imager (ABI) visible, near-IR, and longwave-IR channels to predict the probability of lightning (as observed by the GOES-R Geostationary Lightning Mapper [GLM]) in the next 60 minutes.

Below is a time series of the LightningCast probability and GLM-observed lightning at and near Raymond James Stadium (left panel), along with an animation of LightningCast probability contours, GOES-16 0.64-µm reflectance (from a 1-minute mesoscale sector), and GLM flash-extent density (right panel) near the stadium (red circle). In this way, users can see how the model’s probabilities evolved over time at a specific location and within the vicinity.

Police officers responded to the two individuals struck by lightning at 3:45 PM local time (19:45 UTC). Based on the footprint of the GLM flash-extent density, they were struck at approximately 3:32 PM. The LightningCast probability of lightning was 75% 30 minutes before the lightning strike (remaining mostly above 50% between 3:00 and 3:32 PM). The probability of lightning first reached 50% about 1 hour before the lightning strike, and lightning started occurring within the vicinity (within 25 km) about 45 minutes before the strike.

Output from LightningCast, which leverages the high spatial, temporal, and spectral information found in GOES-R ABI, can help objectively quantify the short-term threat of convective hazards such as lightning. The model could perhaps be used by forecasters to advise outdoor venues such as stadiums to take mitigating actions sooner, or by individuals to help make safe decisions.

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The tropical disturbance (formerly 95W) in the western Pacific south of Guam (discussed here) has strengthened and become the second western Pacific named storm of the year: Malakas. The storm is about halfway between the islands of Guam and New Guinea. The side-by-side imagery above, showing Himawari Band 3 and Band 8 imagery (courtesy JMA), shows deep convection near the center of the storm that was at fairly low latitudes: around 6^o N at 0600 UTC on 8 April, the times of the imagery above. In addition, the storm is far from dry air. A 24-hour animation of the Band 3 (0.64 µm)/Band 13 (10.41 µm) sandwich product on 7 April 2022 below, taken from this site (see this blog post), shows the rotation of the system and the abundance of convection at the center.

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The forecast at 1200 UTC on 8 April from the Joint Typhoon Warning Center take this strengthening storm northwest, between Guam and Yap.

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Imagery from the SSEC/CIMSS Tropical Weather website, below, shows the storm in a region of low shear. An excellent ASCAT overpass at 1126 UTC on 8 April 2022 showed a closed-off center.

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Instrument Flight Rules (IFR) are rules and guidelines put in place by the Federal Aviation Administration that govern conditions in which “flight by outside visual reference is not safe”, whether by low clouds or fog. Cloud and reflectance data from geostationary satellites GOES-16 and GOES-17 provide a satellite IFR Probability product, by detecting fog and low stratus. More information on this product can be found here.

An example of GOES-17 IFR Probability is shown below in Southern California on April 7, 2022 at 1546UTC, displaying GOES-17 IFR Probability, IFR advisories issued by the Aviation Weather Center, and GOES-17 red band reflectance.

The full domain of GOES-17 IFR Probability may be useful for ocean prediction. GOES-16/17 IFR products are available on RealEarth.

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1-minute Mesoscale Domain Sector GOES-16 (GOES-East) “Red” Visible (0.64 µm) and Shortwave Infrared (3.9 µm) images along with 5-minute Fire Power and Fire Temperature products (above) displayed the smoke plume and thermal signature of a grassland fire that rapidly intensified and spread across parts of western Beaver County (located in the Oklahoma Panhandle) on 05 April 2022. The Fire Temperature and Fire Power derived products are components of the GOES Fire Detection and Characterization Algorithm FDCA. Thermal signatures became evident around 1700 UTC or Noon CDT; within about 2 hours this fire was already burning very hot, with 3.9 µm Shortwave Infrared brightness temperatures reaching 138.71ºC — the saturation temperature of ABI Band 7 detectors — around 1915 UTC.

A strong cold front was moving southward across the High Plains during the day (surface analyses) — and arrived at the grass fire’s location just after 2000 UTC (causing a brief flare-up of the fire thermal signatures, and a final pyrocumulus pulse). The surge of colder air behind the cold front showed up as darker shades of green in the 3.9 µm images. Th initial east-southeastward expansion of the hot thermal 3.9 µm signature quickly transitioned to a south-southwestward expansion in the wake of the frontal passage.

1-minute GOES-16 True Color RGB images created using Geo2Grid (below) showed the initial eastward spread of the smoke plume prior to the arrival of the cold front — followed by a pronounced south-southwestward transport of smoke from the fire source region after the cold front moved across the area.

The polar-orbiting Suomi-NPP satellite passed over that region around 1923 UTC — a toggle between True Color and False Color RGB images is shown below. The data to produce these images were downloaded and processed by the SSEC/CIMSS Direct Broadcast ground station (and are available for display in AWIPS via an LDM feed).

Incidentally, Beaver County in Oklahoma experienced another fast-moving grass fire in March 2020.

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