大气科学学报2025，Vol.48Issue(3)：366-376,11.DOI:10.13878/j.cnki.dqkxxb.20250401001
人工智能模型"风顺"对中国区域降水技巧检验
Skill test of the artificial intelligence model"Fengshun"for precipitation forecasting in China
摘要
Abstract
Subseasonal prediction-forecasting weather and climate phenomena 2 to 6 weeks ahead-plays a piv-otal role in sectors ranging from agriculture and disaster risk reduction to water resource management and energy planning.Accurate predictions at this timescale are critical for mitigating impacts of extreme events like floods,droughts,and heatwaves,yet they remain notoriously challenging.Traditional numerical weather prediction(NWP)models,despite advancements through ensemble systems,suffer from diminishing predictability due to rapid decay of initial condition signals.Machine learning(ML)approaches offer promise but have shown limited success in subseasonal scales,hindered by narrow variable coverage,insufficient uncertainty quantification,and re-liance on foreign datasets like ERA5,which poses risks to data sovereignty and operational autonomy.To address these gaps,this study introduces"Fengshun"(CMA-AIM-S2S-Fengshun),an artificial intelligence(AI)large model developed collaboratively by the National Climate Center and Fudan University.Leveraging domestically produced CRA-40 reanalysis data and FY-3E satellite observations,the model aims to establish a robust,autono-mous subseasonal prediction framework for China's regional precipitation.The"Fengshun"system employs a cas-caded Swin Transformer architecture to model spatiotemporal dependencies in meteorological fields.Its core inno-vation lies in a novel intelligent perturbation generation module,which integrates Kullback-Leibler(KL)diver-gence and L1 loss optimization to learn low-rank Gaussian distributions of historical data and prediction time fea-tures.During inference,this module generates probabilistic ensemble forecasts by sampling perturbation vectors,effectively mitigating error accumulation in autoregressive predictions and providing probabilistic representations of future climate states.Unlike many existing AI models dependent on foreign datasets,"Fengshun"is trained en-tirely on Chinese-controlled data,ensuring real-time data assimilation(with same-day updates,5 days faster than ERA5-based models)and operational independence.Historical hindcasts from 2017 to 2021(independent of the training dataset)were validated against ECMWF's subseasonal-to-seasonal(S2S)predictions,using CRA-40 re-analysis as ground truth.The evaluation focused on precipitation anomaly percentage over China,with metrics in-cluding temporal correlation coefficient(TCC)for skill assessment and root-mean-square error(RMSE)for magnitude accuracy.while case studies examined the model's performance during the July 2024 North China heavy precipitation event."Fengshun"outperformed ECMWF across most subseasonal ranges(15-45 days lead time),achieving an 18.6％improvement in TCC and a 7.8％reduction in RMSE for national-averaged pentad precipitation.The model demonstrated exceptional skill in South China(41.2％TCC improvement)and East China(26.5％improvement),maintaining predictability up to 8 pentads(40 days)during the summer flood sea-son,a period critical for disaster preparedness.The Madden-Julian Oscillation(MJO),a key driver of subseasonal variability,was predicted with a skill retention time of 32 days using CRA-40 data,surpassing ECMWF's 30-day benchmark.This advancement is attributed to the model's ability to capture tropical-extratropical interactions,which are fundamental to East Asian monsoon dynamics and precipitation patterns.For the mid-July 2024 event,"Fengshun"accurately predicted the spatial distribution and intensity of precipitation anomalies 3-4 pentads(15-20 days)in advance.At a 7-day lead time,its area correlation coefficient(ACC)reached 0.80,compared to ECMWF's 0.68,and threat scores(TS)for extreme precipitation(＞100％anomaly)were 0.36 versus ECMWF's 0.24,highlighting superior early-warning capabilities.By leveraging indigenous data and advanced AI architecture,"Fengshun"delivers robust subseasonal precipitation forecasts with marked improvements over tradi-tional models,particularly in regions vulnerable to monsoon-driven extremes.Its operational deployment promises to enhance proactive disaster response,agricultural planning,and water resource management,exemplifying the potential of AI to transform climate science and service.关键词
人工智能/次季节预测/降水预测/"风顺"模型Key words
artificial intelligence/subseasonal prediction/precipitation forecasting/"Fengshun"model引用本文复制引用
胡家晖,赵春燕,辛昱杭,赵阳,陆波,李昊,陈磊,仲晓辉,周辰光,吴捷,冯胤庭,徐邦琪..人工智能模型"风顺"对中国区域降水技巧检验[J].大气科学学报,2025,48(3):366-376,11.基金项目
国家重点研发计划项目(2021YFA0718000) （2021YFA0718000）
基于人工智能方法的新疆次季节网格预测平台项目(QHCX-2023-06) （QHCX-2023-06）
中国气象局复盘总结专项(FPZJ2025-168) （FPZJ2025-168）
