1.Center for Earth System Modeling and Prediction of China Meteorological Administration CMA;2.National Oceanic and Atmospheric Administration NOAA/National Weather Service NWS/National Centers for Environmental Prediction NCEP/Environmental Modeling CenterEMC,College Park,Maryland,USA;3.Center for Earth System Modeling and Prediction of China Meteorological Administration (CMA);4.China Meteorological Administration National Meteorological Center
the Youth Fund of Numerical Weather Prediction Center of CMA (Grant 400441), National Natural Science Foundation of China (Grant 41906022)
Based on the Global Ensemble Prediction System in China Meteorological Administration (CMA-GEPS), the extreme Meiyu process over China in 2020 was evaluated. Results show that, during the Meiyu season, a strong and stable western Pacific subtropical high (WPSH) and a gradually strengthened East Asian summer monsoon provide favorable dynamic and moisture conditions for strong rainfall. For the western Pacific subtropical high, CMA-GEPS could skillfully forecast the evolution trend of WPSH index with 7-9 leading days; the CMA-GEPS prediction skills of WPSH strength and area are about the same level as the results from the NCEP ensemble prediction system, and the WPSH strength presents the weaker bias compared with the observation; the prediction skills of ridge line and western boundary index with CMA-GEPS are comparable with the results from ECMWF ensemble prediction system, and the forecasting bias is mainly attributed to the more southward location of ridge line and more eastward center position of western boundary. For the East Asian summer monsoon, CMA-GEPS could skillfully predict the index with 9 leading days, which was two days earlier than the control forecast. The CMA-GEPS control forecasting bias is mainly attributed to the weaker precipitation intensity and more southward location of strong rainfall belt, and also the control forecasting fails to predict the heavy rain in some regions in the middle and lower reaches of Yangtze river. With the time-spatial weight probability (TSWP) neighborhood scheme, the prediction skills related to the heavy rain with CMA-GEPS are improved obviously, and the scheme reduced the occurrence of precipitation missing report. The results of precipitation probability prediction were verified by observation and Brier Scores, which show that: the TSWP neighborhood scheme is superior to the original single point ensemble probability forecast method and the control forecast, and also has a well application values for the heavy storm prediction in the Meiyu period.
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