{"title":"Assessing predictive attribution in NMME forecasts of summer precipitation in eastern china using deep learning","authors":"Xuan Tong, Wen Zhou","doi":"10.1038/s41612-024-00835-7","DOIUrl":null,"url":null,"abstract":"Due to systematic errors in models and the special geographic location of eastern China, most global climate models exhibit significant biases in predicting summer precipitation in this region. This study evaluates the North American Multi-Model Ensemble (NMME) forecasts for eastern China, with a lead time of six months.While NMME simulates precipitation climatology well, it poorly predicts anomalies. Using the Res34-Unet deep learning post-processing method, which has been proven to enhance NMME’s forecasts, we explore that Western Pacific Subtropical High (WPSH) and sea surface temperature (SST) are critical in enhancing forecast accuracy. Among the four models evaluated, only GEM-NEMO (correlation of 0.538 with the WPSH) and CanSIPS-IC3 (which partly captured the impact of SST anomalies on precipitation) partially reflected the key factors identified by deep learning. Simulating these factors more accurately could greatly enhance NMME’s predictive skill for summer precipitation.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-8"},"PeriodicalIF":8.5000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00835-7.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Climate and Atmospheric Science","FirstCategoryId":"89","ListUrlMain":"https://www.nature.com/articles/s41612-024-00835-7","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Due to systematic errors in models and the special geographic location of eastern China, most global climate models exhibit significant biases in predicting summer precipitation in this region. This study evaluates the North American Multi-Model Ensemble (NMME) forecasts for eastern China, with a lead time of six months.While NMME simulates precipitation climatology well, it poorly predicts anomalies. Using the Res34-Unet deep learning post-processing method, which has been proven to enhance NMME’s forecasts, we explore that Western Pacific Subtropical High (WPSH) and sea surface temperature (SST) are critical in enhancing forecast accuracy. Among the four models evaluated, only GEM-NEMO (correlation of 0.538 with the WPSH) and CanSIPS-IC3 (which partly captured the impact of SST anomalies on precipitation) partially reflected the key factors identified by deep learning. Simulating these factors more accurately could greatly enhance NMME’s predictive skill for summer precipitation.
期刊介绍:
npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols.
The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.