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海南秋季暴雨日数异常的环流特征及预测模型构建

Circulation characteristics of autumn rainstorm days anomaly and construction of its prediction model in Hainan

  • 摘要: 利用1982—2013年海南岛18个自动站日降水量、NCEP/NCAR逐月2.5°×2.5°再分析资料、NOAA海温及CFSv2模式的历史回报数据,分析海南秋季暴雨异常的同期环流特征及其与海温的关系。并利用模式预测较好的与秋季暴雨日数密切相关的环流因子、海温构建秋季暴雨日数预测模型。结果表明:(1)秋季暴雨多寡与环流异常关系密切。秋季暴雨偏多年,海南附近盛行偏东风;热带西太平洋-南海气压偏低,热带系统趋于活跃,且该区为东南风异常,带来充沛水汽;西太平洋纬向风切变偏弱,易形成暖心结构,对应有台风的发生发展。另一方面,海温强迫影响显著,热带中东太平洋海温异常影响着大气环流和热带对流活动,造成秋季降水异常。(2)热带太平洋地区中低层高度场、海平面气压、低层风及纬向风切变与秋季暴雨日数关系密切,且CFSv2模式能较好预测这些环流场上的高影响区。(3)利用最优子集回归构建基于模式有效信息的秋季暴雨日数模型,交叉检验和独立样本试验均表明,该预测方法与模型整体预测效果较好,可为秋季暴雨日数的预测提供参考。

     

    Abstract: By using daily precipitation at 18 meteorological automatic stations in Hainan, NCEP/NCAR monthly reanalysis data with a reso-lution of 2.5 °×2.5 °, NOAA SST and historical data of CFSv2 model, the corresponding atmospheric circulation characteristics of Hainan au-tumn rainstorm anomaly and its relationship to SST are analyzed for the period from 1982 to 2013. The circulation factors closely related to au-tumn rainstorm days, which are predicted well by CFSv2, and SST are extracted as predictors to build a predictive model of autumn rainstorm days. Results show the following. (1) Autumn rainstorm days in Hainan are closely related to atmospheric circulation. In more autumn rainstormyears, there is prevailing easterly wind near Hainan. Atmospheric pressure in the tropical western Pacific to south China sea is lower, the tropi-cal vortex systems tend to be more active, and there is a southeast wind anomaly in the region, which provides abundant moisture. The zonalwind shear in western Pacific is weaker, a warm core structure easily forms, corresponding to the occurrence and development of typhoon. On the other hand, the SST forcing effect is significant. In the context of the tropic central and eastern Pacific SST anomaly, atmospheric circula-tion and tropical convection activities are affected, resulting in autumn rainfall anomaly. (2) The autumn rainstorm days are closely associated with the lower level height field, sea level pressure, low level wind and zonal wind shear in the tropical Pacific. CFSv2 has good prediction skillon the high-impact areas of such circulation fields. (3) Predictive model of autumn rainstorm days is built by using optimal subset regression based on effective model information. The results of cross-validation and independent samples test demonstrate that the predictive method and model have good prediction skill, which can provide an effective reference for short-term climate prediction of autumn rainstorm days.

     

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