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

IF 3 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Weather and Forecasting Pub Date : 2023-05-19 DOI:10.1175/waf-d-23-0042.1

T. Sandmæl, Brandon R. Smith, Jonathan G. Madden, Justin W. Monroe, P. Hyland, B. Schenkel, T. Meyer

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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 radar 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 United States 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.

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2021年危险天气试验台实验预警项目雷达对流应用实验:对龙卷风概率算法和新型中气旋探测算法的预报员评价

该系统是美国国家气象局(NWS)雷达作战中心为实现WSR-88D雷达网络单雷达恶劣天气算法套件现代化而进行的更大努力的一部分。在2021年美国国家海洋和大气管理局(NOAA)危险天气试验台(HWT)实验预警计划雷达对流应用实验期间，业务预报员对龙卷风概率算法(TORP)和新型中气旋检测算法(NMDA)进行了评估。TORP和NMDA都利用雷达技术的新产品和进步来创建基于旋转的对象，这些对象可以查询单雷达数据，提供重要的摘要和趋势信息，帮助预报员发布时间关键和可能挽救生命的天气产品。利用b谷歌工作空间和亚马逊网络服务上的云实例等虚拟资源，来自NOAA NWS和美国空军的18名预报员在2021年春季进行了为期三周的远程参与，就算法的有效性及其在作战预警环境中的显示提供了有价值的反馈，这是开发TORP和NMDA从研究到作战过程的关键一步。本文将讨论虚拟HWT实验的细节和每个算法在测试台上的评估结果。

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来源期刊

Weather and Forecasting 地学-气象与大气科学

CiteScore

5.20

自引率

17.20%

发文量

131

审稿时长

6-12 weeks

期刊介绍： Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.