Predicting Probabilistic Lightning Flash Density from the HREF Calibrated Thunder Guidance

Details

• Personal Author:
  Harrison, David, R. ; Elliott, Matthew S. ; Jirak, Israel L. ; Marsh, Patrick T.
• Corporate Authors:
  Cooperative Institute for Severe and High-Impact Weather Research and Operations. ; Storm Prediction Center (U.S.) ; University of Oklahoma.
• NOAA Program & Office:
  CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations) ; NWS (National Weather Service) ; SPC (Storm Prediction Center) ; OAR (Oceanic and Atmospheric Research)
• Description:
  This study expands upon the existing HREFCT product suite to provide explicit probabilistic forecasts of ≥25, 50, 100, or 200 CG flashes within 20 km of a point location during a rolling 4-hour period. This guidance was developed by applying traditional machine learning techniques to train and calibrate four gradientboosted classifier models. This paper will document the development of the new lightning density guidance (section 2) before demonstrating 3 preliminary results and verification of the products.
• Keywords:
  • Lightning
  • Machine learning
  • Thunderstorm
  • Storm Prediction Center
  • HREF
• DOI:
  https://doi.org/10.25923/b2b3-m644
• Format:
  pdf
• Document Type:
  Non-series Report
• Funding:
  Grant no. NA21OAR4320204
• License:
  https://creativecommons.org/publicdomain/zero/1.0/
• Rights Information:
  CC0 Public Domain
• Compliance:
  Submitted
• Download URL:
  https://repository.library.noaa.gov/view/noaa/76789/noaa_76789_DS1.pdf
• File Type:
  [PDF - 1.25 MB]
• Collection(s):
  Office of Oceanic and Atmospheric Research (OAR) ; NOAA Cooperative Institutes ; National Weather Service (NWS)
• Main Document Checksum:
  urn:sha-512:3a12f2c10df9c8c1e7d6830694053746d7972230ad7919eb725985bf686e8c1a9b30e86b13781f34a7d09da87d80523148af1e7e711c921569abe1436aa0671f