热带气象学报2024，Vol.40Issue(6)：1045-1062,18.DOI:10.16032/j.issn.1004-4965.2024.092
人工智能大模型对2024年长江中下游梅雨的预测评估
Evaluation of AI Model Predictions for the 2024 Meiyu Season in the Middle and Lower Reaches of the Yangtze River
摘要
Abstract
Currently,the performance of AI models in forecasting sub-seasonal precipitation in the middle and lower reaches of the Yangtze River remains unclear.This study used the Meiyu process in the middle and lower Yangtze River region in 2024 as an evaluation case to assess the prediction performance of three AI meteorological models(i.e.,Pangu-weather,Fuxi,and FourCastNet)and Sub-seasonal to seasonal(S2S)Prediction data from the ECMWF.Moreover,based on an analysis of precipitation and circulation evolution using methods such as correlation skills and power spectrum analysis,we evaluated Meiyu precipitation,background field variables,and their low-frequency components and compared them with those from the conventional EC-S2S model.The results are as follows:(1)In the 4th pentad of June 2024,the Meiyu season commenced in the middle and lower reaches of the Yangtze River.It was influenced by the northward extension of the Western Pacific Subtropical High and the southward development of the westerly trough.Subsequently,the Meiyu and its associated summer monsoon,cold air influences,and humidity changes exhibited significant quasi-biweekly variations.(2)All three models and EC-S2S successfully captured the evolution of the subtropical anticyclone and the westerly trough within a 10-day lead time.Prediction uncertainties increased for all three models as well as EC-S2S after 11 days of lead time.Only Pangu-weather and EC-S2S continued to provide valuable references for predictions beyond 15 days.(3)FourCastNet,Fuxi,and EC-S2S provided skillful predictions of Meiyu precipitation with significant correlations 11-15 days in advance.They also accurately reflected the quasi-biweekly oscillation characteristics of precipitation and associated circulation in the Meiyu region within the same lead time.The EC-S2S model demonstrated high accuracy in precipitation prediction but had a weaker ability to predict the significance of quasi-biweekly characteristics.Pangu,FourCastNet,and EC-S2S were able to forecast the quasi-biweekly oscillation of the summer monsoon in the south and the westerly trough in the north of the Yangtze River Basin 16-20 days in advance.Although large models faced challenges in forecasting Meiyu circulation beyond a lead time of half a month,their effective lead time for predicting low-frequency components was longer,and their forecasts of some elements(e.g.,V-wind and specific were better than those of EC-S2S.The results suggest that exploring sub-seasonal forecasting of large models from the perspective of low-frequency oscillations may provide new insights for subsequent applications and improvements in sub-seasonal forecasting.关键词
人工智能大模型/梅雨/次季节预测/准双周振荡Key words
AI models/Meiyu season/subseasonal forecasting/quasi-biweekly oscillation分类
天文与地球科学引用本文复制引用
曹欣沛,梁萍,黄文娟,张志琦,巩远发..人工智能大模型对2024年长江中下游梅雨的预测评估[J].热带气象学报,2024,40(6):1045-1062,18.基金项目
国家自然科学基金项目(42175056、U2342208) （42175056、U2342208）
上海市自然科学基金项目(24ZR1492500、23YF1440100) （24ZR1492500、23YF1440100）
中国气象局重点创新团队(CMA2023ZD03)共同资助 （CMA2023ZD03）
