{"id":62924,"date":"2025-01-31T20:39:29","date_gmt":"2025-01-31T20:39:29","guid":{"rendered":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/?p=62924"},"modified":"2025-01-31T21:04:02","modified_gmt":"2025-01-31T21:04:02","slug":"lightningcast-version-2","status":"publish","type":"post","link":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/archives\/62924","title":{"rendered":"LightningCast version 2"},"content":{"rendered":"\n

The ProbSevere LightningCast<\/a> version 1 (v1) model uses machine learning and GOES-R Advanced Baseline Imager (ABI) data to predict the probability of lightning in the next 60 minutes. This version will be operational at NOAA later in 2025.<\/p>\n\n\n\n

As research and development continues, a new version of the model (LightningCast v2) adds Multi-Radar Multi-Sensor (MRMS) Reflectivity at -10o<\/sup>C as a predictor. Reflectivity at -10o<\/sup>C is well correlated with imminent lightning activity due to its ability to depict hydrometeors in the mixed-phase region of convection (generally 0o<\/sup>C to -20o<\/sup>C). Other radar-derived parameters are being investigated as well.<\/p>\n\n\n\n

Preliminary results demonstrate that the radar-derived predictor adds value to the problem of short-term lightning prediction, without diminishing the power of the satellite predictors—that is, the model appears to have learned and uses the strengths of each data source for making better predictions.<\/strong> <\/p>\n\n\n\n

Here are two recent examples. First, in Ohio and Pennsylvania, warm air advection at 850 and 700 mb forced some elevated but shallow thunderstorms. <\/p>\n\n\n\n

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Figure 1: 850 mb (left) and 700 mb (right) analyses from the Storm Prediction Center’s mesoscale analysis page (from 16-17 UTC on 01\/31\/2025). The filled red areas are regions with warm air advection.<\/figcaption><\/figure>\n\n\n\n

A short-term model profile from the NAM 3-km model also shows the elevated but abbreviated extent of CAPE.<\/p>\n\n\n

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Figure 2: A skew-T log-P chart showing the thermal and moisture profile from southwest Pennsylvania at 18Z on 01\/31\/2025. The profile is from www.pivotalweather.com<\/a> and powered by SHARPpy<\/a>.<\/figcaption><\/figure>\n<\/div>\n\n\n

The first animation below is from LightningCast v1. The shallow nature of the storms and the thick layer of ice clouds above the convection obscures key signatures for the satellite-only model, resulting in poor probabilistic guidance. <\/p>\n\n\n\n

Figure 3: Animation of LightningCast v1 (contours), GOES-16 day-cloud-phase-distinction RGB (background), and GLM flash-extent density (foreground).<\/figcaption><\/figure>\n\n\n\n

The animation below uses the LightningCast v2 model. While not a cure-all, the reflectivity at -10o<\/sup>C clearly helps the model provide better guidance to lightning, albeit with little lead time to lightning initiation in this case. <\/p>\n\n\n\n

Figure 4: Animation of LightningCast 2 (contours), GOES-16 day-cloud-phase-distinction RGB (background), and GLM flash-extent density (foreground).<\/figcaption><\/figure>\n\n\n\n

The day prior, weak 850 mb warm air advection forced convective development in northeast Colorado and southwest Nebraska. The thermal profile appeared to be too cold to generate much CAPE or lightning.<\/p>\n\n\n

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Figure 5: 850 mb analysis from the Storm Prediction Center’s mesoscale analysis page<\/figcaption><\/figure>\n<\/div>\n\n\n

However, from a satellite perspective, the convection certainly looks<\/em> like it could produce lightning. Thus, the LightningCast v1 output shows high probabilities of lightning (animation below).<\/p>\n\n\n\n