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.

East Coast Tornadoes

The past few days have seen several strong tornadoes along the U.S. east coast. A shortwave trough with ample upper-level diffluence provided a forcing mechanism for severe storms from Florida to Virginia.

Near Juno Beach, FL, a tornado damaged power lines, homes, buildings, and cars. Maximum wind speeds were estimated at 130 mph (rated EF2). Oddly enough, this tornado was only about 20 miles north of a weaker tornado from the day before.

Figure 1: ProbSevere v3 contours, MRMS MergedReflectivity, and NWS severe weather warnings for a storm near Juno Beach, FL. Outer contours on ProbSevere objects are colored by the probability of tornado.

ProbTor v3 (PTv3) is better calibrated than its v2 counterpart. There was a distinct ramp up in the tornado probability for this storm prior to tornadogenesis, compared to PTv2 (Figure 2). Part of this ramp up was due to higher 0-1 km storm-relative helicity depicted in the HRRR (~160 J/kg), which was much higher than the RAP. Storm rotation was also slowly increasing. Interestingly, this occurred at the same time that lightning and reflectivity-based parameters were decreasing. Despite low overall probability for tornado (20-30%), the ramp up, coupled with the fact that PTv3 remains on the low end overall (max of ~60%) could perhaps have tipped off users to look more closely at this developing storm.

Figure 2: Time series of PTv3 and PTv2 for a tornadic storm on the Florida coast, along with severe reports and NWS severe weather warnings.

The next day, Virginia Beach, VA was hit with an EF3 tornado, with peak winds estimated between 140 and 150 mph. Remarkably, no injuries were reported despite damage to 100 homes. In this case, PTv3 exceeded PTv2, and even hit 60%, which is a very high value for v3. The dip in probability shortly before the tornado was likely due to a pronounced reduction in mid-level azimuthal shear, which quickly rebounded (the 1-3 km mean wind also dropped from 37 kt to 30 kt during that time). 

Figure 3: ProbSevere v3 contours, MRMS MergedReflectivity, and NWS severe weather warnings for a tornadic storm near Virigina Beach, VA. Outer contours on ProbSevere objects are colored by the probability of tornado.

Figure 4: Time series for ProbSevere v3 probabilities, along with reports and NWS severe weather warnings.

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.