Gridded Severe Hail Nowcasting Using 3D U-Nets, Lightning Observations, and the Warn-on-Forecast System

Schmidt, Tobias G.; McGovern, Amy; Allen, John T.; Potvin, Corey K.; Chase, Randy J.; Wiley, Chad M.; McGovern-Fagg, William R.; Flora, Montgomery L.; Homeyer, Cameron R.; Williams, John K.

doi:10.1175/aies-d-24-0026.1

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Gridded Severe Hail Nowcasting Using 3D U-Nets, Lightning Observations, and the Warn-on-Forecast System

2024
By Schmidt, Tobias G. ; McGovern, Amy ; Allen, John T. ; ...
Source: Artificial Intelligence for the Earth Systems (2024)

Public Access Version Available on: May 13, 2025, 12:00 AM

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Details:

Journal Title:

Artificial Intelligence for the Earth Systems
Personal Author:

Schmidt, Tobias G. ; McGovern, Amy ; Allen, John T. ; Potvin, Corey K. ; Chase, Randy J. ; ... More +

Schmidt, Tobias G. ; McGovern, Amy ; Allen, John T. ; Potvin, Corey K. ; Chase, Randy J. ; Wiley, Chad M. ; McGovern-Fagg, William R. ; Flora, Montgomery L. ; Homeyer, Cameron R. ; Williams, John K. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CIRA (Cooperative Institute for Research in the Atmosphere) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations) ; NSSL (National Severe Storms Laboratory)

OAR (Oceanic and Atmospheric Research) ; CIRA (Cooperative Institute for Research in the Atmosphere) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations) ; NSSL (National Severe Storms Laboratory) Less -
Description:

Hailstorms cause billions of dollars in damage across the United States each year. Part of this cost could be reduced by increasing warning lead times. To contribute to this effort, we developed a nowcasting machine learning model that uses a 3D U-Net to produce gridded severe hail nowcasts for up to 40 minutes in advance. The three U-Net dimensions uniquely incorporate one temporal and two spatial dimensions. Our predictors consist of a combination of output from the National Severe Storms Laboratory Warn-on-Forecast System (WoFS) numerical weather prediction ensemble and remote sensing observations from Vaisala’s National Lightning Detection Network (NLDN). Ground truth for prediction was derived from the Maximum Expected Size of Hail calculated from the gridded NEXRAD WSR-88D radar (GridRad) dataset. Our U-Net was evaluated by comparing its test set performance against rigorous hail nowcasting baselines. These baselines included WoFS ensemble HAILCAST and a logistic regression model trained on WoFS 2-5 km updraft helicity. The 3D U-Net outperformed both these baselines for all forecast period timesteps. Its predictions yielded a neighborhooded maximum critical success index (max CSI) of ~0.48 and ~0.30 at forecast minutes 20 and 40, respectively. These max CSIs exceeded the ensemble HAILCAST max CSIs by as much as ~0.35. The NLDN observations were found to increase the U-Net performance by more than a factor of 4 at some timesteps. This system has shown success when nowcasting hail during complex severe weather events, and if used in an operational environment, may prove valuable.
Source:

Artificial Intelligence for the Earth Systems (2024)
DOI:

https://doi.org/10.1175/aies-d-24-0026.1
ISSN:

2769-7525
Format:

pdf
Publisher:

American Meteorological Society
Document Type:

Journal Article
Funding:

Grant no. NA21OAR4320204
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Main Document Checksum:

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urn:sha-512:81357d3ba9845bda6826bc94fa12187582bb5e865c1409e47281078bd50db7b1a0695cca71aaaff75ef095d8129b23da07835975c8b83bc85b9044be7846691a
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