Allendale, SC tornado

Allendale, South Carolina sustained much damage after a major tornado tore through parts of the town on April 5th, 2022. The NOAA Storm Prediction Center issued a 10%-hatched risk of tornadoes through parts of Mississippi, Georgia, and South Carolina the morning of the storms. Strong low-level flow and destabilization led to supercells forming ahead of a squall line, such as the tornadic supercell that hit Allendale.

Figure 1: SPC tornado outlook and 06Z verification. 

A ProbSevere v3 (PSv3) model, ProbTor, tracked this storm from Georgia into South Carolina. The probability of tornado rapidly increased about 30 minutes before and again 15-20 minutes before a tornado was reported around Allendale. Increasing azimuthal shear, radar reflectivity, and lightning were noted prior to tornadogenesis. See here for meteograms of different predictors for this storm.

Figure 2: ProbSevere v3 (storm contours), MRMS MergedReflectivity, and NWS severe weather warnings. The outer PSv3 contour is colored by the probability of tornado.

One new feature at the HWT last year was the ProbSevere time series or meteogram function in AWIPS, which can be activated by double-clicking a ProbSevere time object. The window displays the latest 2 hours of probability history for the featured storm for all four ProbSevere models (hail, wind, tornado, any severe). The meteogram updates automatically as new data are processed. This feature helps forecasters more quickly interrogate storm trends and will again be available to forecasters at the 2022 HWT. 

Figure 3: The ProbSevere time series window for the tornadic supercell in Allendale, SC.

ProbTor v3 uses a different machine-learning model than ProbTor v2 (gradient-boosted decision trees vs. naive Bayesian classifier). While the maximum CSI for PTv3 is about the same as PTv2, the PTv3 probabilities are much better calibrated. What this means is that the output probability values much better match the observed frequencies of tornadoes, for any given probability value. Users should see much lower false alarm rates at higher probability bins. Given the inherent noise in doppler radar velocity data (and downstream MRMS azimuthal shear), and inherent uncertainties in detecting tornadoes, this also means that PTv3 values over 60% are exceedingly rare.

Compare the attributes diagrams for PTv3 and PTv2 below. A perfectly calibrated or "reliable" model will have predictions follow the 1:1 line. Notice how PTv2 over-predicts, while PTv3 is very close to the 1:1 line, except for some under-prediction around 50-60%. The most-skillful (i.e., highest CSI) probability range for PTv3 is 20-40%. The University of Wisconsin / CIMSS is actively working on improving ProbTor, experimenting with additional data and methods that make better use of the spatial patterns found in satellite and radar data. 

Figure 4: Attributes diagrams for PTv3 and PTv2 on a validation dataset from 2021.

The IntenseStormNet detects particularly intense storms from a satellite-only perspective, using deep learning and images of ABI and GLM data. Using GOES-16 one-minute mesoscale scans, IntenseStormNet reached over 90% on this storm about 10 minutes before the first tornado report. The higher IntenseStormNet probabilities corresponded well to a GLM lightning jump and overshooting tops in ABI imagery. While a tornado warning was already in effect, seeing this feature could add confidence to the warning forecaster. The output of IntenseStormNet is also used in the ProbSevere v3 models.