基于机器学习的中国夏季降水延伸期预报及土壤湿度的可能贡献  

Machine Learning-Based Prediction of Summer Extended-Range Precipitation and Possible Contribution of Soil Moisture over China

作　　者：叶宇辰 陈海山[1,2] 朱司光[1,2] 董寅硕 YE Yuchen;CHEN Haishan;ZHU Siguang;DONG Yinshuo(Key Laboratory of Meteorological Disaster,Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science and Technology(NUIST),Nanjing 210044,Jiangsu,China;School of Atmospheric Sciences,Nanjing University of Information Science and Technology(NUIST),Nanjing 210044,Jiangsu,China)

机构地区：[1]南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室,江苏南京210044 [2]南京信息工程大学大气科学学院,江苏南京210044

出　　处：《高原气象》2024年第1期184-198,共15页Plateau Meteorology

基　　金：国家自然科学基金基础科学中心项目(42088101)。

摘　　要：延伸期预报准确率较低的问题仍然是目前重要的科学难题,做好延伸期预报对防灾减灾具有重要意义。本文利用机器学习方法开展了中国夏季降水延伸期(5~30天)预报试验,并探讨了土壤湿度对降水延伸期预报的可能贡献。结果表明机器学习方法的预报结果准确率要比传统线性模型方法有较大改善,且在诸多机器学习方法中,以Catboost, Lightgbm和Adaboost三个机器学习模型为最优。进一步分析发现长江流域表层土壤湿度异常导致的蒸发异常和感热异常,能够引起大气环流和垂直运动异常,最终对夏季降水产生影响。使用三个最优的机器学习方法的集合计算出模型中各个预报因子的贡献率,发现在长江流域的延伸期降水中,局地土壤湿度主要在5~10天占主导作用,而前期降水主要在10~15天占主导作用,长江流域20~30天的延伸期降水基本上受到大尺度环流控制。还评估了非局地土壤湿度在延伸期降水中的作用,发现中南半岛表层土壤湿度主要对15~30天的长江流域延伸期降水有重要贡献。将中南半岛表层土壤湿度加入到东北地区延伸期降水模型中,发现对该地区延伸期降水预报准确率并无提升作用,验证了机器学习模型的可用性。该研究为延伸期降水预测以及探究预报因子贡献率提供了一定的参考。Low accuracy of extended forecast remains an important scientific problem in the current stage,and qualified extended forecast is of great significance for disaster prevention and mitigation.In this study,the machine learning method was used to forecast the summer precipitation during the extension period(5~30 days)in China,and explore the possible contribution of soil moisture to extended forecast of precipitation.Based on the results,machine learning methods remarkably outweigh traditional linear models in terms of forecast accuracy,with Catboost,Lightgbm and Adaboost being the optimal machine learning methods.According to further analysis,the abnormal evaporation and sensible heat anomaly caused by the surface soil moisture anomaly in the Yangtze River Basin can lead to the atmospheric circulation and vertical movement anomaly,which eventually affects summer precipitation.The set of three optimal machine learning methods was applied to calculate the contribution rate of each forecasting factor in the model.It was found that the local soil moisture dominated the extended precipitation in the Yangtze River Basin from the 5th day to the 10th day,while the local soil moisture played a dominant role on previous precipitation from the 10th day to the 15th day,and the extended precipitation in the Yangtze River Basin during the period of Day 20~30 was basically controlled by large-scale circulation.Besides,the influence of non-local soil moisture on extended precipitation was evaluated,the results of which showed that the surface soil moisture in Indo-China Peninsula mainly contributed to the extended precipitation in the Yangtze River Basin from the 15th day to the 30th day.By adding the surface soil moisture of Indo-China Peninsula to the extended precipitation model in Northeast China,it was found that surface the soil moisture failed to improve the extended forecast accuracy of precipitation in this area,which verified the availability of the machine learning model.This study provides a certain reference for fore

关 键 词：机器学习 延伸期预报 夏季降水 土壤湿度 

分 类 号：P456.2[天文地球—大气科学及气象学]