基于组合统计降尺度的华西南部地区冬季气温预测技术研究

Study on Winter Temperature Prediction Technology in Southwest China Based on Combined Statistical Downscaling

利用1982—2017年华西南部地区冬季气温和NCEP再分析资料以及CFS模式实时预测资料,通过SVD诊断分析,选取影响华西南部地区冬季气温的同期关键区大气环流和前期海温及OLR因子场,建立预测与观测场相结合的组合统计降尺度预测模型。该统计降尺度预测模型对1982—2017年的回报结果显示:与观测场的空间相关系数较CFS模式原始预测结果有显著提高,多年均值从-0.06提升到0.38,最高可达0.85。同时,此降尺度预测模型可较好地回报出华西南区冬季气温的空间分布型。

Based on the winter temperature and NCEP reanalysis data and the real-time prediction data of the CFS model in Southwest China,the SVD diagnostic analysis is used to select the atmospheric circulation and early sea temperature and OLR factor fields in the key areas affecting the winter temperature in Southwest China,and to establish a combined statistical drop scale prediction model combining prediction and observation fields.The results of this statistical downscale forecast model for 1982-2017 show that the spatial correlation coefficient with the observation field is significantly higher than the original results of the CFS model,with the multi-year mean increasing from-0.06 to 0.38,up to 0.85.At the same time,this scale prediction model can better restore the spatial distribution of winter temperature in Southwest China.