Lightning Initiation in Texas and Florida

Forecasters are working in west Texas (Lubbock and Midland) and Tampa, Florida. Forecasters in both regions have noted 30 minutes of actionable lead time to lightning initiation in a number of these storms. Figures 1 and 2 show the evolution of LightningCast probabilities with GOES-16 ABI and Earth Networks total lightning points. Several forecasters have noted that it would be nice to have more flexibility in choosing LightningCast probability contour levels. Currently, the contours are customizable, but it requires a CAVE restart.

Flash rates are high in Florida, but quite low in Texas (especially early on in the storms' development). Forecasters have noted how ProbSevere v3 seems to be more resistant to these differences in flash rates, not overemphasizing the higher flash rates or underemphasizing the lower flash rates in the respective regions.

ProbSevere v3 in Florida

A sagging cold front provided a marginal risk of severe weather in south Florida. The experimental ProbSevere v3 (PSv3) will be evaluated at the Hazardous Weather Testbed this spring and summer. PSv3 models use a different machine-learning method, and incorporate additional MRMS, ABI, GLM, and SPC mesoanalysis fields. We've found that the models should be more skillful and better calibrated, overall.

This storm caused damage to silos and chicken barns just north of Lake Okeechobee yesterday afternoon. PSv3 showed much higher probabilities (≥ 40%), whereas PSv2 maxed out at 9% before the wind report. PSv3 should provide better guidance on severe storms for both busy severe days and marginal severe days.

By inspecting the predictor importance of this storm right before the wind report, it was found that the top-5 contributing predictors were:

1. ENI total lightning density (0.45 fl/km^2/min)
2. ABI satellite growth rate (3.8 %/min)
3. MRMS VIL (29 g/m^2)
4. Eff. shear (42 kt)
5. 0-3 km lapse rate (7.8 C/km)

We are currently working on how best to convey predictor importance to forecasters in AWIPS, which we hope will help users better understand why the model makes the predictions that it does, and ultimately better utilize ProbSevere guidance.