Nocturnal hailstorms in the Upper Midwest and ProbSevere v3

A cold front spawned numerous hail-producing storms in the Upper Midwest during the evening and overnight hours last night. ProbSevere version 3 (PSv3), which uses gradient-boosted decision trees with MRMS, ABI, GLM, Earth Networks, and NWP/SPC mesoanalysis data, was able to improve guidance over version 2 (PSv2) for a number of storms.

One of the more prolific hail-producing storms of the day formed west of Minneapolis, MN. PSv3 improved upon PSv2 with higher probabilities at a critical time before the first NWS warning was issued. 

Figure 1: ProbSevere contours, MRMS MergedRef, and NWS severe weather warnings for a storm west of Minneapolis, MN.

Figure 2: Time series of PSv3 and PSv2 for a select time period for the highlighted storm in Figure 1.

Looking at 01:50 UTC from the time series in Figure 2, we found that the top five predictors aiding to higher severe probabilities were:

1. ENI max total lightning density (0.28 flashes/min/km^2)
2. Eff. bulk shear (41 kt)
3. MRMS max 3-6 km azshear (0.008 /s)
4. MRMS max composite reflectivity (60.5 dBZ)
5. ABI+GLM intense convection probability (98%)

The intense convection probability (ICP) is an ABI + GLM based deep-learning model used to diagnose intense convection, and is a predictor in PSv3. 

Another storm to the northeast of the storm above also briefly produced severe hail. While the PSv3 probability was relatively low, it was still much higher than PSv2, which is an improvement that may be enough to bring a forecaster's attention to the storm.

Figure 3: ProbSevere contours, MRMS MergedRef, and NWS severe weather warnings for a storm WNW of Minneapolis, MN.

Figure 4: Time series of PSv3 and PSv2 for a select time period for the highlighted storm in Figure 2.

PSv3 peaked at 37% at 02:08 UTC, 6 minutes before the hail report. Performing a predictor importance analysis at this time, we found that the top five predictors aiding to higher severe probabilities were:

1. MRMS max composite reflectivity (65.5 dBZ)
2. Eff. bulk shear (47 kt)
3. MRMS max VIL (25 g/m^2)
4. MRMS max 3-6 azshear (0.007 /s)
5. MRMS max MESH (0.56 in)

A storm in northwestern Wisconsin fluctuated in probability, but eventually produced 1.25-inch hail.

At the time of the report (03:00 UTC), PSv3 was 47% and PSv3 was 13%. 

Figure 5: ProbSevere contours and MRMS MergedRef for a hail-producing storm in northwest Wisconsin.

At the time of the report, we found the top five predictors in PSv3 were:

1. MRMS max MESH (0.76 in)
2. MRMS max VIL (31 g/m^2)
3. Eff. bulk shear (52 kt)
4. MRMS max composite reflectivity (61.5 dBZ)
5. ENI total lightning density (0.21 flashes/min/km^2)

PSv3 should also help reduce false alarms around the country, relative to PSv2. This warned storm in northern Nebraska serves as an example. The maximum probability for PSv3 was 17%, compared to 49% for PSv2. There were no reports recorded for this storm.

Figure 6: ProbSevere contours, MRMS MergedRef, and NWS severe weather warnings for a storm in northern Nebraska.

One more example, just west of Duluth, MN, where PSv3 was about 20% higher than PSv2 at the time of a 1.25-inch hail report. At the time of the report, we found the top five predictors in PSv3 were:

1. MRMS max MESH (1.19 in)
2. MRMS max composite reflectivity (67.5 dBZ)
3. MRMS max VIL (26 g/m^2)
4. ENI total lightning density (0.23 flashes/min/km^2)
5. MRMS max 3-6 km azshear (0.007 /s)

Figure 7: ProbSevere contours and MRMS MergedRef for a storm west of Duluth, MN.

We hope these examples illustrate the improvement that forecasters will experience in using PSv3 at the Hazardous Weather Testbed. We also hope that they illustrate the benefit of intelligently fusing data together from the different observation systems we have available. We are actively working on ways to help users better interpret model predictions using analyses such as predictor importance ranking.