大气科学学报2025，Vol.48Issue(3)：377-388,12.DOI:10.13878/j.cnki.dqkxxb.20250109002
基于人工智能大模型的上海2024年极端高温事件次季节预测
Sub-seasonal prediction of extreme heatwave events in Shanghai for 2024 using artificial intelligence-driven large models
摘要
Abstract
With the intensification of global warming,extreme heatwave events are occurring with increasing fre-quency and intensity,posing severe impacts to human society and ecosystems.Accurate prediction of extreme heatwaves is essential for urban resilience,as megacities are particularly vulnerable due to impacts on public health,energy supply,and transportation.Sub-seasonal prediction,which bridges short-term weather forecasting and seasonal prediction,plays a critical role in mitigating the effects of extreme heatwaves.However,traditional sub-seasonal prediction studies have primarily focused on temperature anomalies or their low-frequency compo-nents.Recent advancements in meteorological artificial intelligence(AI)models have led to the rapid develop-ments in weather prediction,but their capability for sub-seasonal heatwave prediction in megacities remains un-clear.This study evaluates the performance of three AI-driven meteorological models(Pangu,FuXi,and FourCast-Net)in predicting heatwave events in Shanghai during midsummer 2024.The evaluation is based on correlation skill,power spectrum analysis,and other diagnostic methods.Analysis of heatwaves and their associated circulation patterns indicates that the number of high-temperature days in Shanghai during midsummer is significantly and positively correlated with the strength of the subtropical high aloft.The timing of heatwave occurrences aligns closely with the positive phases of the 10-20 d quasi-periodic oscillation and is simultaneously influenced by the 30-60 d low-frequency oscillation.On the quasi-biweekly timescale,extreme heatwave events in Shanghai are linked to both the northwestward propagation of wave trains triggered by convective anomalies in the tropical western Pacific and the eastward propagation of circulation anomalies along the Silk Road teleconnection pattern in the middle and high latitudes.Among the three AI models,both Pangu and FuXi demonstrate skillful high-tem-perature predictions up to 15 d in advance.Certain models,such as Pangu,can effectively predict the evolution of the subtropical high associated with heatwaves at lead times of 16-20 d.Differences in sub-seasonal prediction performance among the AI models are associated with their ability to capture low-frequency evolution of high temperatures and atmospheric circulation.Models with stronger predictive capabilities for low-frequency circulation anomalies tend to produce more accurate sub-seasonal forecasts of the subtropical high and high-tem-perature events.Furthermore,AI models exhibit greater predictive skill for sub-seasonal heatwaves during periods not affected by the end of Meiyu season or typhoon activity.This study also highlights that the differences in sub-seasonal prediction skill among the three AI models for low-frequency circulation at lead times of 11-15 d are influenced by the intensity of the initial low-frequency state.The uncertainty in prediction skill caused by initial-state variability warrants further investigation.Given that this study evaluates AI model performance primarily u-sing 2024 as a case study,more comprehensive assessments are needed.Additionally,when applying AI models to enhance sub-seasonal heatwave prediction in megacities,the influence of surface heterogeneity,such as urbaniza-tion effects,should be considered to improve forecast accuracy.关键词
高温/人工智能大模型/次季节预测/低频振荡Key words
heatwave/artificial intelligence-driven large models/sub-seasonal prediction/low-frequency oscillation引用本文复制引用
梁萍,张志琦,曹欣沛,黄文娟..基于人工智能大模型的上海2024年极端高温事件次季节预测[J].大气科学学报,2025,48(3):377-388,12.基金项目
国家自然科学基金项目(U2342208) （U2342208）
国家重点研发计划项目(2024YFC3013100) （2024YFC3013100）
上海市自然科学基金项目(24ZR1492500) （24ZR1492500）
中国气象局复盘总结项目(FPZJ2025-043) （FPZJ2025-043）
中国气象局重点创新团队项目(CMA2023ZD03) （CMA2023ZD03）
中国气象局青年创新团队项目(CMA2024QN06) （CMA2024QN06）
