Windstorm in east Texas

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. 

Strong tornadoes in the Deep South

A seasonally strong shortwave trough tapped into abundant Gulf of Mexico moisture forcing severe and tornadic storms across Louisiana, Mississippi, and Alabama. The Storm Prediction Center issued a Moderate Risk outlook with tornadoes and strong wind gusts being the primary threats (see Figure 1).

Fig. 1: SPC outlook with 06Z 12/17 verification (dots).

ProbSevere/ProbTor models show the evolution of storms throughout the afternoon (Figure 2). The outer contours represent the ProbTor probability and are only present if ProbTor is ≥ 15%, here.

This environment was characterized by 1000 - 2500 J/kg of MLCAPE, 45-60 kts of effective bulk shear, and 1-3km AGL mean wind of 40-55 kts. The ProbSevere models generally track and discern the most dangerous threats well (see the NWS warning polygons). However, there are several storms with erroneously high ProbTor values (outer polygons with high probabilities) that quickly appear and disappear during the animation. Most of these false alarms are due to spurious MRMS azimuthal shear values which are produced by noisy Doppler velocity data. Work is ongoing to mitigate these errors in ProbTor.

Fig. 2: ProbSevere and ProbTor contours, MRMS MergedReflectivity, and NWS severe weather warnings.

A storm that formed in east Texas/west Louisiana spawned numerous tornadoes and prompted a tornado emergency for Alexandria, LA at 18:41 UTC. This storm was in a primed thermodynamic and kinematic environment, with 250-300 J/kg of 0-1km storm-relative helicity. The low-level and mid-level MRMS azimuthal shear values increased the ProbTor probabilities from 30% to 91% in about 20 minutes. You can see the saved time series of attributes for this storm here. Figure 3 shows how the ProbSevere products evolved for this storm in comparison to NWS severe weather warnings and local storm reports.

Fig. 3: Time series of ProbSevere probabilities for tornadic storm in Louisiana. The bottom axis plots durations for NWS severe weather warnings and times of preliminary LSRs.

At least one this this storm's tornadoes was expected to be rated as significant (EF3+).