梅雨降水季节预测的多方法比较  

A comparative study of multiple methods for seasonal prediction of Meiyu rainfall

作　　者：李琳菲 杨颖[1] 朱志伟[1,2] 王蔚 LI Linfei;YANG Ying;ZHU Zhiwei;WANG Wei(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,Nanjing 210044,China;State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100086,China;Minhang Meteorological Bureau,Shanghai 201199,China)

机构地区：[1]南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心,江苏南京210044 [2]中国气象科学研究院灾害天气国家重点实验室,北京100086 [3]上海市闵行区气象局,上海201199

出　　处：《大气科学学报》2024年第2期313-329,共17页Transactions of Atmospheric Sciences

基　　金：国家自然科学基金资助项目(42088101)。

摘　　要：基于1961—2000年逐月降水观测资料和全球大气再分析资料,分析了6—7月长江中下游(108°~123°E,27°~33°N)梅雨的时空分布特征。通过观测诊断和数值试验确定了影响梅雨异常偏多的3个前期因子:4—5月平均的西北太平洋海平面气压正异常;3月至5月北大西洋海平面气压负变压倾向;1月至4月西伯利亚的2 m温度负倾向。利用这3个具有物理意义的影响因子构建了梅雨季节预测模型,该模型在训练期(1961—2000年)和独立预测期(2001—2022年)均具有显著的预测技巧(相关系数分别为0.79和0.77,均方根误差分别为0.59和0.68)。同时,基于相似的潜在预测因子,对比了利用偏最小二乘回归方法和5种机器学习方法(随机森林、轻量级梯度提升机、自适应提升、类别型特征提升、极端梯度提升)建立的预测模型的技巧。虽然训练期(1961—2000年)偏最小二乘回归和机器学习建模拟合效果更高,但在独立预测期(2001—2022年)上述模型的预测技巧显著降低(相关系数均低于0.44,均方根误差均大于0.93),出现了明显的过拟合问题。本研究强调梅雨的短期气候预测应建立在物理机制基础之上,而使用机器学习方法需谨慎。This study elucidates the spatiotemporal characteristic of June—July mean Meiyu rainfall over the middle and lower reaches of the Yangtze River basin(27°—33°N,108°—123°E)using Chinese monthly gauge precipitation data and global atmospheric reanalysis datasets from 1961 to 2000.Three physically meaningful precursors play pivotal roles in enhancing Meiyu rainfall during June and July.First,positive sea level pressure anomalies over the subtropical western Pacific(SWP)during April—May strengthened the western North Pacific subtropical high by exciting Kelvin wave responses and enhancing Walker circulation.This phenomenon facilitates moisture transport from the tropics to the Yangtze River via southerly winds.The mechanism underlying SWP’s impact on Meiyu highlights the persistent influence of atmosphere-ocean interaction over the Indo-Pacific basin from spring to summer.Second,the negative tendency of sea level pressure over the North Atlantic from March to May(NAP)reflects the influence of North Atlantic Oscillation(NAO)-related mid-latitude wave trains on Meiyu.From spring to early summer,the evolution of NAO-related wave trains across Eurasia strengthens the Northeast Asian cyclone and enhances Meiyu rainfall.Third,the cooling tendency of surface temperature over East Siberian from January to April(EST)is closely associated with the extratropical westerly jet by amplifying the temperature gradient between the tropics and polar regions.This condition favors the maintenance of meridional circulation over East Asia and enhances Meiyu rainfall.The aforementioned mechanisms have been verified in corresponding numerical experiments based on a linear baroclinic model.

关 键 词：梅雨 季节预测 物理经验预测模型 机器学习 

分 类 号：P412.13[天文地球—大气科学及气象学] P457.6