{"title":"通过条件偏差惩罚回归提高气象集合预报处理器的大到暴雨集合预报质量","authors":"Sunghee Kim , Ali Jozaghi , Dong-Jun Seo","doi":"10.1016/j.jhydrol.2024.132363","DOIUrl":null,"url":null,"abstract":"<div><div>The River Forecast Centers (RFC) in the US use the Meteorological Ensemble Forecast Processor (MEFP) to generate bias-corrected ensemble precipitation forecasts for hydrologic forecasting. Operational experience and verification indicate that the MEFP tends to under-forecast heavy-to-extreme precipitation significantly. To address the above, a conditional bias-penalized regression (CBPR) method has been developed as an enhancement to the ordinary least squares regression method currently used in the MEFP in the bivariate normal space. The two methods are then comparatively evaluated for 277 locations in 13 RFCs for the wettest consecutive 2 months using the ensemble mean precipitation forecasts from the Global Ensemble Forecast System version 12 reforecast dataset as input. The results show that the proposed method improves probabilistic forecasts for heavy-to-extreme precipitation by widely-varying margins for all 13 RFCs. The margin of improvement as measured by the continuous ranked probability skill score ranges up to about 5 to 40 % for Day-1 and Day-2 precipitation exceeding 50.8 mm for 24-hr amounts for different RFCs, and is generally larger for heavier precipitation, shorter lead times, the western RFCs and 0–96 hr precipitation. This improvement in conditional performance, however, is achieved at the expense of slight deterioration in unconditional performance and moderate wet bias. With urbanization and climate change, addressing Type-II error and conditional bias is an increasingly important topic in hydrologic forecasting. For the above, CBPR is particularly appealing in that its positive impact is likely larger for larger events.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"647 ","pages":"Article 132363"},"PeriodicalIF":5.9000,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving ensemble forecast quality for heavy-to-extreme precipitation for the Meteorological Ensemble Forecast Processor via conditional bias-penalized regression\",\"authors\":\"Sunghee Kim , Ali Jozaghi , Dong-Jun Seo\",\"doi\":\"10.1016/j.jhydrol.2024.132363\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The River Forecast Centers (RFC) in the US use the Meteorological Ensemble Forecast Processor (MEFP) to generate bias-corrected ensemble precipitation forecasts for hydrologic forecasting. Operational experience and verification indicate that the MEFP tends to under-forecast heavy-to-extreme precipitation significantly. To address the above, a conditional bias-penalized regression (CBPR) method has been developed as an enhancement to the ordinary least squares regression method currently used in the MEFP in the bivariate normal space. The two methods are then comparatively evaluated for 277 locations in 13 RFCs for the wettest consecutive 2 months using the ensemble mean precipitation forecasts from the Global Ensemble Forecast System version 12 reforecast dataset as input. The results show that the proposed method improves probabilistic forecasts for heavy-to-extreme precipitation by widely-varying margins for all 13 RFCs. The margin of improvement as measured by the continuous ranked probability skill score ranges up to about 5 to 40 % for Day-1 and Day-2 precipitation exceeding 50.8 mm for 24-hr amounts for different RFCs, and is generally larger for heavier precipitation, shorter lead times, the western RFCs and 0–96 hr precipitation. This improvement in conditional performance, however, is achieved at the expense of slight deterioration in unconditional performance and moderate wet bias. With urbanization and climate change, addressing Type-II error and conditional bias is an increasingly important topic in hydrologic forecasting. For the above, CBPR is particularly appealing in that its positive impact is likely larger for larger events.</div></div>\",\"PeriodicalId\":362,\"journal\":{\"name\":\"Journal of Hydrology\",\"volume\":\"647 \",\"pages\":\"Article 132363\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydrology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022169424017591\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022169424017591","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Improving ensemble forecast quality for heavy-to-extreme precipitation for the Meteorological Ensemble Forecast Processor via conditional bias-penalized regression
The River Forecast Centers (RFC) in the US use the Meteorological Ensemble Forecast Processor (MEFP) to generate bias-corrected ensemble precipitation forecasts for hydrologic forecasting. Operational experience and verification indicate that the MEFP tends to under-forecast heavy-to-extreme precipitation significantly. To address the above, a conditional bias-penalized regression (CBPR) method has been developed as an enhancement to the ordinary least squares regression method currently used in the MEFP in the bivariate normal space. The two methods are then comparatively evaluated for 277 locations in 13 RFCs for the wettest consecutive 2 months using the ensemble mean precipitation forecasts from the Global Ensemble Forecast System version 12 reforecast dataset as input. The results show that the proposed method improves probabilistic forecasts for heavy-to-extreme precipitation by widely-varying margins for all 13 RFCs. The margin of improvement as measured by the continuous ranked probability skill score ranges up to about 5 to 40 % for Day-1 and Day-2 precipitation exceeding 50.8 mm for 24-hr amounts for different RFCs, and is generally larger for heavier precipitation, shorter lead times, the western RFCs and 0–96 hr precipitation. This improvement in conditional performance, however, is achieved at the expense of slight deterioration in unconditional performance and moderate wet bias. With urbanization and climate change, addressing Type-II error and conditional bias is an increasingly important topic in hydrologic forecasting. For the above, CBPR is particularly appealing in that its positive impact is likely larger for larger events.
期刊介绍:
The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.