{"title":"Spatial and Temporal Variation of Subseasonal-to-Seasonal (S2S) Precipitation Reforecast Skill Across CONUS","authors":"Jessica Rose Levey, A. Sankarasubramanian","doi":"10.1175/jhm-d-23-0159.1","DOIUrl":null,"url":null,"abstract":"\nPrecipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resources management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash-Sutcliffe Efficiency (NSE) coefficient, has been analyzed towards understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components – correlation, conditional bias, and unconditional bias – by four seasons, three lead times (1–12-day, 1–22 day, and 1–32 day), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Center for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the Conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 inches per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Post-processing using simple Model Output Statistics could reduce both unconditional and conditional bias to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"107 12","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/jhm-d-23-0159.1","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resources management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash-Sutcliffe Efficiency (NSE) coefficient, has been analyzed towards understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components – correlation, conditional bias, and unconditional bias – by four seasons, three lead times (1–12-day, 1–22 day, and 1–32 day), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Center for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the Conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 inches per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Post-processing using simple Model Output Statistics could reduce both unconditional and conditional bias to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
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