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 aCooperative Institute for High-Impact and Severe Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Brandon R. Smith aCooperative Institute for High-Impact and Severe Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Jonathan G. Madden aCooperative Institute for High-Impact and Severe Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Justin W. Monroe aCooperative Institute for High-Impact and Severe Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Patrick T. Hyland aCooperative Institute for High-Impact and Severe Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Benjamin A. Schenkel aCooperative Institute for High-Impact and Severe Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Tiffany C. Meyer aCooperative Institute for High-Impact and Severe Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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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 advances 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.

Significance Statement

Before transitioning newly developed radar-based severe weather applications to forecasting operations, an experiment simulating the use of these tools by end users issuing severe weather warnings is helpful to identify both how they are best utilized and address any needed improvements to increase their operational readiness. Conducted in 2021, this study describes the forecaster evaluation of the single-radar Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) in one of the first completely virtual Hazardous Weather Testbed (HWT) experiments. Participants stated both TORP and NMDA offered marked improvement over the currently available algorithms by helping the operational forecaster build their confidence when issuing severe weather warnings and increasing their overall situational awareness of storms within their domain.

Meyer’s current affiliation: Unidata, University Corporation for Atmospheric Research, Boulder, Colorado.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Thea N. Sandmæl, thea.sandmael@noaa.gov

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 advances 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.

Significance Statement

Before transitioning newly developed radar-based severe weather applications to forecasting operations, an experiment simulating the use of these tools by end users issuing severe weather warnings is helpful to identify both how they are best utilized and address any needed improvements to increase their operational readiness. Conducted in 2021, this study describes the forecaster evaluation of the single-radar Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) in one of the first completely virtual Hazardous Weather Testbed (HWT) experiments. Participants stated both TORP and NMDA offered marked improvement over the currently available algorithms by helping the operational forecaster build their confidence when issuing severe weather warnings and increasing their overall situational awareness of storms within their domain.

Meyer’s current affiliation: Unidata, University Corporation for Atmospheric Research, Boulder, Colorado.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Thea N. Sandmæl, thea.sandmael@noaa.gov
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