Salado Tornado

A supercell quickly developed on the southern flank of an arc of storms in central Texas on Tuesday, April 12. High CAPE (≥ 3000 J/kg), effective shear (≥ 50 kt) and effective SRH (≥ 170 J/kg) all contributed to an elevated probability of tornado from ProbTor v3 (PTv3) by 22:00Z. About 30 minutes later, the supercell produced an EF-3 tornado west of Salado and south of Killeen, TX. 

Figure 1: ProbSevere contours, MRMS MergedReflectivity, and NWS severe weather warnings in central Texas. The storm that produced the EF-3 tornado traveled south of Killeen, Texas. 

In the critical early stages of storm development, PTv3 probabilities exceeded PTv2 probabilities, which is noteworthy given PTv3's better-calibrated guidance. From Figure 2, we see that prior to the initial NWS tornado warning, PTv3 was consistently 10-20% greater than PTv2. Because PTv3 is better calibrated than PTv2 (i.e., probability value better match tornado occurrence), PTv3 will rarely exceed 60%, whereas PTv2 regularly hits 80-90% (but PTv2 over-predicts in that range).

Figure 2: Time series of PTv3 and PTv2 for the tornadic storm west of Salado, TX.

At 22:14Z, PTv3 = 30%, while PTv2 = 7%. In a post-mortem analysis, we found that the MESH, mid-level azimuthal shear, and effective bulk shear were the top-3 contributing predictors. The 4th leading predictor was the probability of intense convection produced by the ProbSevere IntenseStormNet. A rapid increase in this value from 29% to 99% occurred from 21:52Z to 22:14Z (see "ICP" in the meteograms). IntenseStormNet is a GOES-ABI and GOES-GLM-based convolutional neural network, which picked up an a developing cold-U signature and increasing lighting to produce a very high probability of "intense" convection (see the animation below). In this way, it provides a holistic method of leveraging important values, textures, and spatial features found in geostationary imagery. In ProbSevere v3 models, IntenseStormNet computes one value per storm per time step, which is used as a predictor. 

Figure 3: Intense convection probability contours overlaid GOES-16 "sandwich" imagery from a 1-min mesoscale scan. Note the rapidly developing supercell on the south flank of the developed convection.

ProbSevere v3 infuses spatially important satellite information into its predictions. This example shows that forecasters should pay especially close attention to developing storms when PTv3 is exceeding PTv2.

Storms pummel the Midwest

An energetic short-wave and attendant surface low rapidly intensified on April 12th, bringing quickly developing storms to a number of regions in the Midwest U.S. Large hail, severe wind gusts, and potent tornadoes were reported from Wisconsin to Texas.

Figure 1: SPC categorical outlook at 06 verification.

In the middle of the afternoon, a lone elevated storm along a stationary boundary traversed the state of Wisconsin, causing a 67-mph wind gust in La Crosse, WI, and dropping hail ranging from 1" to 1.5". ProbSevere version 3 (PSv3) had a pretty good handle on it over the course of several hours. 

Figure 2: ProbSevere storm-based contours, MRMS MergedReflectivity, and NWS severe weather warnings for a storm in Wisconsin.

While the MRMS products contributed positively to the PSv3 probabilities (e.g., MESH, Reflectivity -10C, AzShear), the IntenseStormNet probability (a predictor in PSv3 models) also contributed in the models. IntenseStormNet uses images of visible and long-wave infrared channels from GOES-R ABI, as well as images of flash-extent density from GOES-R GLM to detect intense parts of storms. From the animation below, we see that IntenseStormNet "probability of intense convection" for the storm in Wisconsin largely stayed between 50% and 90%.

The storm of the day spawned in northeast Iowa, ahead of a cold front. From ProbSevere hover-output in AWIPS, we saw that it had a strong normalized satellite growth rate at 21:31Z. PSv3 was 32% when the NWS issued its first severe thunderstorm warning, at 22:00Z. The probability of severe then soon increased to 70% by 22:18Z. The storm produced its first 1"-diameter hail report at 22:25Z. 

Figure 3: An animation of ProbSevere contours, MRMS MergedReflectivity, and NWS severe weather warnings for a tornadic storm in Iowa

A tornado warning was issued at 22:59Z, coincident with ProbTor v3 rapidly increasing to 49%. ProbTor v2 was at 13%. There was an increase in the MRMS azimuthal shears at this time, along with an increase in the significant tornado parameter (a predictor in PTv3). v2 was likely underestimating the threat due to too much contribution from stout MLCIN (-86 J/kg), dampening the probability. The machine-learning model of PTv3 (gradient-boosted decision trees) appears to better incorporate the MLCIN information than it's predecessor, in this case. 

Figure 4: ProbSevere contours, MRMS MergedReflectivity, and NWS severe weather warnings for a tornadic storm in Iowa. The ProbSevere time series window can be activated by double-clicking inside a storm object.

The IntenseStormNet's probability also contributed to the higher probability of tornado. See in the animation below how probabilities ≥ 90% are well-correlated with the most vigorous portions of the convection.

 

Later, the cold front zipped down from Nebraska into Kansas. Very strong satellite growth rates were observed, as the PSv3 values regularly exceeded 80%. The cold front was essentially warned continuously from western Iowa to southern Kansas.

Figure 5: ProbSevere contours, MRMS MergedReflectivity, and NWS severe weather warnings for a cold front from Iowa to Kansas.

The intense convection probability from IntenseStormNet quickly went from < 10% to ≥ 90% for most of the line, which later produced numerous hail and wind reports.