{"title":"Evaluating ensemble predictions of South Asian monsoon low pressure system genesis","authors":"D. Suhas, William R. Boos","doi":"10.1175/waf-d-24-0044.1","DOIUrl":null,"url":null,"abstract":"\nSynoptic-scale vortices known as monsoon low pressure systems (LPS) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–2022. The GEFS successfully predicted about half the observed LPS genesis events one to two days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a False Alarm Ratio (FAR) around 50% for one- to two-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing that of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPS form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias-correction.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Weather and Forecasting","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/waf-d-24-0044.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Synoptic-scale vortices known as monsoon low pressure systems (LPS) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–2022. The GEFS successfully predicted about half the observed LPS genesis events one to two days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a False Alarm Ratio (FAR) around 50% for one- to two-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing that of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPS form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias-correction.
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.