Details:

Journal Title:
Weather and Forecasting
Personal Author:
Gagne, D. J., II ; McGovern, A. ; Haupt, S. E. ; Sobash, R. A. ; Williams, J. K. ; ... More +
Gagne, D. J., II ; McGovern, A. ; Haupt, S. E. ; Sobash, R. A. ; Williams, J. K. ; Xue, M. Less -
NOAA Program & Office:
NWS (National Weather Service)
Description:
Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. Forecasts from the machine learning models are produced using two convection-allowing ensemble systems and the results are compared to other hail forecasting methods. The machine learning forecasts have a higher critical success index (CSI) at most probability thresholds and greater reliability for predicting both severe and significant hail.
Keywords:
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Convection Forecasting Hail
Source:
Weather and Forecasting, 32(5), 1819-1840
DOI:
https://doi.org/10.1175/waf-d-17-0010.1
Document Type:
Journal Article
Funding:
Grant no. NA13NWS4680001
Rights Information:
Other
Compliance:
CHORUS
Main Document Checksum:
[+]
urn:sha256:d6ed1dba46f3a7404af2e47b0f01cad91f04c57fe0aa5fcb99109055497ee01a
Download URL:
https://repository.library.noaa.gov/view/noaa/48301/noaa_48301_DS1.pdf
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