{"id":51808,"date":"2023-04-20T20:14:05","date_gmt":"2023-04-20T20:14:05","guid":{"rendered":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/?p=51808"},"modified":"2023-04-20T20:27:50","modified_gmt":"2023-04-20T20:27:50","slug":"probsevere-in-the-oklahoma-severe-weather-outbreak","status":"publish","type":"post","link":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/archives\/51808","title":{"rendered":"ProbSevere in the Oklahoma severe-weather outbreak"},"content":{"rendered":"\n

There was a conditional outlook for severe storms in Oklahoma on April 19th, owing to uncertainty in the forcing for ascent and a stout warm-sector cap. Well, the cap broke, and mayhem ensued in central Oklahoma.

A break in the cirrus deck in southwest Oklahoma likely contributed to surface heating and enhanced mixing of the boundary layer, allowing the cap to break. ProbSevere LightningCast<\/a>, an AI model that uses solely images of ABI data to predict next-hour lightning at any given point, was able to capture lightning initiation (Figure 1). Glaciating cloud tops are a prime signal of imminent lightning, and the one-minute scans from the GOES-16 mesoscale sector helped the model maximize lead time (Figure 2).<\/p>\n\n\n\n

Figure 1: ProbSevere LightningCast contours, GOES-16 ABI day-cloud-land RGB, and GOES-16 GLM flash-extent density.<\/figcaption><\/figure>\n\n\n
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Figure 2: Electrified storms with annotations of lead time to lightning initiation or forecasts produced by LightningCast<\/figcaption><\/figure>\n<\/div>\n\n\n

ProbSevere version 3<\/a>, a set of machine-learning models that predicts probabilities of severe weather hazards in the near future, was able to track these storms as they became severe (Figure 3). These storms produced huge hail (up to 3″ in diameter), straight-line winds exceeding 80 mph, and deadly tornadoes (Figure 4). One aspect of ProbSevere’s automated guidance that forecasters have frequently reported is that during busy situations, ProbSevere helps them quickly triage which storms or threats to prioritize and investigate further. In this outbreak, most storms had at least severe thunderstorm warnings, while several were tornado-warned. Both ProbSevere v3 and LightningCast will be evaluated by forecasters at NOAA’s Hazardous Weather Testbed this spring.<\/p>\n\n\n\n

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Figure 3: ProbSevere v3 storm contours (inner contour is colored by the probability of any severe; outer contour is colored by the probability of tornado), MRMS MergedReflectivity, and NWS severe weather warnings. <\/figcaption><\/figure>\n\n\n
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Figure 4: Preliminary storm reports from the Storm Prediction Center. <\/figcaption><\/figure>\n<\/div>\n\n\n

Just a little further north in Kansas, LightningCast was able to correctly predict lightning initiation (with about 16 minutes of lead time) for the storm despite moderate overlapping cirrus clouds (Figure 5). Despite the obscuring ice clouds, LightningCast was able to discern elevated lightning potential with the aid of the visible red band (0.64-\u00b5m reflectance), snow-ice band (1.6-\u00b5m reflectance), and long-wave infrared bands (10.3-\u00b5m and 12.3-\u00b5m brightness temperatures) and the spatial patterns evident in the growing cumuliform clouds.<\/p>\n\n\n\n