Wednesday, February 28, 2024

Midwest storms...in February!

 Record-breaking high temperatures for February preceded and helped spawn storms in eastern Iowa and northern Illinois yesterday.

ProbSevere LightningCast gave a heads up on the developing convection. LightningCast uses deep-learning methods and GOES ABI data to predict the probability of lightning up to an hour in advance. LightningCast version 1 (LCv1) is being transitioned to NOAA operations in 2024. 

Development and improvement of LightningCast continues, however, based on forecaster feedback. One new product that is being evaluated at the Hazardous Weather Testbed this year is the probability of ≥ 10 fl in 60 min. Forecasters remarked that in some situations, having guidance on a more robust level of lightning would be very helpful. 

In the animation below, the P(≥ 1fl in 60 min) are depicted by contours in shades of blue (at 10%, 25%, and 50% levels). The P(≥ 10 fl in 60 min) are in red-shaded contours (10% and 25% levels).

For the three storms in Illinois, there was 13 min, 6 min, and 13 min of lead time to ≥10 flashes (when measured from the 10% contour). The storms in Iowa never got to 10 flashes, but the LightningCast probabilities only touched 25%. Users should recognize that lead time for the 10-flash product will be lower than the 1-flash product. When probabilities above 10% begin to appear (and especially above 25%), forecasters should anticipate an intensification in the lightning activity. 

Figure 1: LightningCast contours (P[≥ 1 fl] in blues, P[≥ 10 fl] in reds), GOES-16 ABI imagery, and GOES-16 GLM flash-extent density for storms in eastern Iowa and northern Illinois.


These storms developed rapidly into supercells and produced hail, severe wind gusts, and several tornadoes. ProbSevere v3 corresponded with NWS warnings quite well, even in this explosive environment. Notably, ProbTor v3 was much higher (30-45%) than ProbTor v2. While more investigation is needed to confirm this, it is likely that the HRRR model inputs in v3 were more impactful than the RAP model inputs in v2, signifying that the HRRR better depicted the environment.