IntenseStormNet and ProbSevere

The deep-learning model IntenseStormNet uses only image patches of GOES-R ABI and GLM data to detect the most intense parts of storms. Specifically, it uses the 0.64-µm reflectance, 10.3-µm brightness temperatures, and the GLM flash-extent density. In this way, IntenseStormNet tries to holistically use spatial patterns and textures to predict the probability of intense storms from a purely satellite perspective. Figure 1 shows the output of IntenseStormNet for several supercell or supercell-like storms in Texas. Notice how the model predicts high probabilities in areas of cold cloud tops, bubbly cloud-top texture, and strong overshooting tops.

Figure 1: Output from the IntenseStormNet (probability of intense convection contours), GOES-16 sandwich product (0.64-µm reflectance and 10.3-µm brightness temperature), and severe storm reports.

Though the IntenseStormNet model isn't being evaluated explicitly in the HWT this month, it serves as an input into ProbSevere v3 (PSv3), so that the PSv3 models can utilize the rich spatial and spectral information that GOES-R provides.  The IntenseStormNet output is often in the top third of contributing predictors for ProbSevere predictions.

Figure 2: ProbSevere contours, MRMS MergedRef, and NWS severe weather warnings for two supercells in Texas during the HWT. The outer contours are colored by the probability of tornado. The time series window is for the rightmost storm.