Diffluent flow at 250 mb and a strong theta-E gradient at 850 mb helped spawn a lone severe storm in east Texas last night. This elevated and fast-moving storm caused multiple reports of trees and power lines down in Jasper and Newton counties around 08:00 UTC.
ProbSevere v3 (PSv3) indicated increasing intensity in this storm about 12-15 minutes before v2. PSv3 uses a different machine-learning model (gradient-boosted decision trees) and utilizes more ABI, GLM, and MRMS data. It also leverages some SPC mesoanalysis fields. UW-CIMSS is running PSv3 experimentally in near-realtime, and it will be evaluated at the 2021 HWT.
While we've found that PSv3 should be more skillful and better calibrated (i.e., the probabilities better match report occurrence frequency), it may be more difficult to attribute changes in the model probabilities to changes in its predictors. We are working on methods to convey predictor importance to users on a per-storm basis in near-realtime.
Post-mortem, we evaluated the predictor importance for this storm at 07:34 UTC. We found the most important contributors were:
1. MRMS composite reflectivity (64 dBZ)
2. Eff. bulk shear (58 kt)
3. ENI total lightning density (0.29 flashes/min/km^2)
4. MRMS 0-2 km azimuthal shear (0.011 s^-1)
5. MRMS VIL (23 g / m^2)
6. MRMS 3-6 km azimuthal shear (0.006 s^-1)
7. ABI + GLM intense convection probability (95%)
The 0-3 km lapse rate (3.7 C/km) was the predictor detracting from the probability the most.
We hope that PSv3 will give users even more confidence during severe weather warning operations.
