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Deep learning for Seasonal Precipitation Prediction over China

Journal of Meteorological Research 2022年第2期36卷 271-281页

作者： Weixin JIN Yong LUO Tongwen WU Xiaomeng HUANG Wei XUE Chaoqing YU Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084 Beijing Climate Center China Meteorological AdministrationBeijing 100081 AI for Earth Lab the Joint Laboratory of Cross-Strait Tsinghua Research InstituteXiamen 361006

Despite significant progress having been made in recent years,the forecast skill for seasonal precipitation over China remains *** this study,a deep-learning-based statistical prediction model for seasonal precipitation over China was *** model was trained to learn the distribution of the seasonal precipitation using simultaneous general circulation ***,it was pre-trained with the hindcasts of several general circulation models(GCMs),and evaluation of the test set suggested that the pre-trained model could basically reproduce the GCM-predicted precipitation,with the anomaly pattern correlation coefficients(PCCs)greater than ***,transfer learning was applied by using ECMWF Reanalysis v5(ERA5)data and gridded precipitation observational data over China,to further correct the systemic errors in the *** a result,using general circulation fields from reanalysis as the input,this hybrid model performed reasonably well in simulating the seasonal precipitation over China,with the PCC reaching *** addition,the results using the circulation fields predicted by GCMs as the input were also *** general,the proposed model improves the PCC over China by 0.10-0.13,as compared to the raw GCM outputs,for lead times of 1-4 *** deep learning model has been used at the National Climate Center of China Meteorological Administration for the past two years to provide guidance for summer precipitation prediction over China and has performed extremely well.

关键词： seasonal precipitation seasonal prediction statistical downscaling deep learning

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