Evaluation of First-Guess Watch Guidance in the 2022 HWT Spring Forecasting Experiment

Details

• Personal Author:
  Harrison, David, R. ; McGovern, Amy ; Karstens, Chris D. ; 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:
  NOAA's Forecasting a Continuum of Environmental Threats (FACETs; Rothfusz et al. 2018) initiative is tasked with exploring innovative methods that can shift NWS services from static, deterministic products to a dynamic, probabilistic paradigm. One such product has been realized in the form of dynamically-updating deterministic warning polygons which automatically track along a severe storm's path in real time. These non-static tornado and severe thunderstorm warnings have been termed "Threats-in-Motion" (TIM; Stumpf et al. 2011; Stumpf and Gerard 2021) and are currently being proposed as a bridge between the deterministic WWA system and the probabilistic FACETs paradigm. Although TIM is primarily concerned with storm-scale warnings, the concept of deriving dynamically-evolving deterministic products from an underlying probabilistic paradigm can be further developed and applied to address the operational challenges and limitations of SPC Severe Thunderstorm and Tornado Watches. To this end, this research presents and demonstrates a prototype system which applies machine learning (ML) techniques to produce a dynamic, first-guess watch product.
• Keywords:
  • Machine learning
  • Storm Prediction Center
  • Watch
  • Hazardous weather testbed
  • Spring forecasting experiment
  • Severe weather
  • HREF
  • Forecast
• DOI:
  https://doi.org/10.25923/fssj-fe85
• 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/76788/noaa_76788_DS1.pdf
• File Type:
  [PDF - 1.03 MB]
• Collection(s):
  Office of Oceanic and Atmospheric Research (OAR) ; NOAA Cooperative Institutes ; National Weather Service (NWS)
• Main Document Checksum:
  urn:sha-512:0da42988775c72e96de9a672cb07990d2d5277aeff24b9baf539678032de5d1d1b9310f9a0f109f012eecd0adb5f8bd1e6c00ca1f315583822bc4b01f41f16a8