The 2021 Hazardous Weather Testbed Experimental Warning Program Radar Convective Applications Experiment: A Forecaster Evaluation of the Tornado Probability Algorithm and the New Mesocyclone Detection Algorithm

  • Thea N. Sandmæl
  • , Brandon R. Smith
  • , Jonathan G. Madden
  • , Justin W. Monroe
  • , Patrick T. Hyland
  • , Benjamin A. Schenkel
  • , Tiffany C. Meyer

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Developed as part of a larger effort by the National Weather Service (NWS) Radar Operations Center to modernize their suite of single-radar severe weather algorithms for the WSR-88D network, the Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) were evaluated by operational forecasters during the 2021 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) Experimental Warning Program Radar Convective Applications experiment. Both TORP and NMDA leverage new products and advan-ces in radar technology to create rotation-based objects that interrogate single-radar data, providing important summary and trend information that aids forecasters in issuing time-critical and potentially life-saving weather products. Utilizing virtual resources like Google Workspace and cloud instances on Amazon Web Services, 18 forecasters from the NOAA/ NWS and the U.S. Air Force participated remotely over three weeks during the spring of 2021, providing valuable feedback on the efficacy of the algorithms and their display in an operational warning environment, serving as a critical step in the research-to-operations process for the development of TORP and NMDA. This article will discuss the details of the virtual HWT experiment and the results of each algorithm’s evaluation during the testbed.

Original languageEnglish
Pages (from-to)1125-1142
Number of pages18
JournalWeather and Forecasting
Volume38
Issue number7
DOIs
StatePublished - Jul 2023

Keywords

  • Algorithms
  • Nowcasting
  • Operational forecasting
  • Radars/radar observations
  • Severe storms

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