Katie A. Wilson, P. Burke, Burkely T. Gallo, Patrick S. Skinner, T. T. Lindley, Chad M. Gravelle, Stephen W. Bieda, Jonathan G. Madden, Justin W. Monroe, Jorge E. Guerra, Dale A. Morris
{"title":"为 NWS 预报业务提供风暴尺度概率指导的合作探索","authors":"Katie A. Wilson, P. Burke, Burkely T. Gallo, Patrick S. Skinner, T. T. Lindley, Chad M. Gravelle, Stephen W. Bieda, Jonathan G. Madden, Justin W. Monroe, Jorge E. Guerra, Dale A. Morris","doi":"10.1175/waf-d-23-0174.1","DOIUrl":null,"url":null,"abstract":"\nThe operational utility of the NOAA National Severe Storm Laboratory’s storm-scale probabilistic Warn-on-Forecast System (WoFS) was examined across the watch-to-warning time frame in a virtual NOAA Hazardous Weather Testbed (HWT) experiment. Over four weeks, 16 NWS forecasters from local Weather Forecast Offices, the Storm Prediction Center, and the Weather Prediction Center participated in simulated forecasting tasks and focus groups. Bringing together multiple NWS entities to explore new guidance impacts on the broader forecast process is atypical of prior NOAA HWT experiments. This study therefore provides a framework for designing such a testbed experiment, including methodological and logistical considerations necessary to meet the needs of both local office and national center NWS participants. Furthermore, this study investigated two research questions: (1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and (2) How could WoFS guidance be used most effectively across the current watch-to-warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters’ trust in new guidance and openness to change. The solutions identified to these challenges will take WoFS one step closer to transition, and in the meantime, improve the capabilities of WoFS for experimental use within the operational community.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"49 8","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations\",\"authors\":\"Katie A. Wilson, P. Burke, Burkely T. Gallo, Patrick S. Skinner, T. T. Lindley, Chad M. Gravelle, Stephen W. Bieda, Jonathan G. Madden, Justin W. Monroe, Jorge E. Guerra, Dale A. 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Furthermore, this study investigated two research questions: (1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and (2) How could WoFS guidance be used most effectively across the current watch-to-warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters’ trust in new guidance and openness to change. 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Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations
The operational utility of the NOAA National Severe Storm Laboratory’s storm-scale probabilistic Warn-on-Forecast System (WoFS) was examined across the watch-to-warning time frame in a virtual NOAA Hazardous Weather Testbed (HWT) experiment. Over four weeks, 16 NWS forecasters from local Weather Forecast Offices, the Storm Prediction Center, and the Weather Prediction Center participated in simulated forecasting tasks and focus groups. Bringing together multiple NWS entities to explore new guidance impacts on the broader forecast process is atypical of prior NOAA HWT experiments. This study therefore provides a framework for designing such a testbed experiment, including methodological and logistical considerations necessary to meet the needs of both local office and national center NWS participants. Furthermore, this study investigated two research questions: (1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and (2) How could WoFS guidance be used most effectively across the current watch-to-warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters’ trust in new guidance and openness to change. The solutions identified to these challenges will take WoFS one step closer to transition, and in the meantime, improve the capabilities of WoFS for experimental use within the operational community.
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.