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

作　　者：曹欣沛 梁萍 黄文娟 张志琦 巩远发[1] CAO Xinpei;LIANG Ping;HUANG Wenjuan;ZHANG Zhiqi;GONG Yuanfa(School of Atmospheric Sciences,Chengdu University of Information Technology,Chengdu 610225,China;Key Laboratory of Cities’Mitigation and Adaptation to Climate Change in Shanghai,Shanghai Regional Climate Center,Shanghai 200030,China)

机构地区：[1]成都信息工程大学大气科学学院,四川成都610225 [2]上海市气候中心/中国气象局上海城市气候变化应对重点开放实验室,上海200030

出　　处：《热带气象学报》2024年第6期1045-1062,共18页Journal of Tropical Meteorology

基　　金：国家自然科学基金项目(42175056、U2342208);上海市自然科学基金项目(24ZR1492500、23YF1440100);中国气象局重点创新团队(CMA2023ZD03)共同资助。

摘　　要：目前,人工智能大模型对长江中下游降水的次季节预测效果尚不清楚。采用三个人工智能气象大模型(Pangu-weather、Fuxi和FourCastNet)与欧洲中心次季节-季节模式(EC-S2S)预测资料,以2024年长江中下游梅雨为例,在诊断其降水及其环流演变的基础上,利用相关技巧、功率谱分析等方法,对比评估了气象大模型与欧洲中心S2S模式(EC-S2S)对该年梅雨降水、背景场变量及其低频振荡分量的预测效果,并与传统的EC-S2S模式进行了比较。结果表明:(1)2024年6月第4候,受西太平洋副热带高压北抬和北侧西风槽发展南伸影响,长江中下游进入梅雨期。此后,梅雨及其相关联的夏季风、冷空气影响和湿度变化均呈现显著的准双周振荡。(2)三个大模型与EC-S2S模式都能提前10 d较好地预测梅雨相联系的副高和西风槽的演变活动。当预测时效超前11 d时,三个大模型与EC-S2S模式预测对梅雨环流形势的预测不确定性增加,其中仅Pangu模型和ECS2S模式超前16—20 d的预测能反映出长江中下游南北两侧的冷暖空气活动。(3)FourCastNet、Fuxi两个模型和EC-S2S模式能提前11—15 d给出有显著相关技巧的梅雨降水预测,也能提前11—15 d反映梅雨区降水及其相关联环流的准双周振荡特征。EC-S2S模式对降水量的预测优于大模型,但其准双周振荡功率谱值弱于大模型。Pangu、FourCastNet和EC-S2S模式能提前16—20 d预报长江中下游南侧的夏季风和北侧西风槽活动的准双周振荡影响。尽管大模型在超前半月以上的梅雨环流预测上存在挑战,但对其低频分量的有效超前预测时效更长,部分要素(如经向风和比湿)预测优于EC-S2S模式,表明从低频振荡这一途径出发开展大模型的次季节预测,可为后续应用和改进大模型的次季节预测提供新思路。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 osci

关 键 词：人工智能大模型 梅雨 次季节预测 准双周振荡 

分 类 号：P456.3[天文地球—大气科学及气象学]