偏最小二乘回归在水汽和地面气温多模式集成预报中的应用研究

Application of Partial Least Squares Regression in Multimodal Integrated Forecasting of Water Vapor and Surface Air Temperature

使用一种新的多模式集成方法偏最小二乘回归(PLS),利用其能完全消除多重共线性的特征来改善比湿和地面气温多模式集成预报的效果。基于TIGGE资料集下的欧洲中期天气预报中心(ECMWF)、中国气象局(CMA)、日本气象厅(JMA)和英国气象局(UKMO)4个中心集合预报结果,建立2012年多模式(25°~60°N,60°~150°E)区域24~168 h预报时效(间隔24 h)比湿和地面气温的多模式集成模型,分别使用消除偏差集合平均(BREM)、简单集合平均(EMN)、超级集合预报(SUP)以及偏最小二乘回归(PLS)4种方法对地面气温和水汽多模式集成,利用均方根误差(RMSE)和距平相关系数(cor)来判定多模式集成的效果并且针对性地预报了一次短期寒潮过程。2次预报结果均表明:偏最小二乘回归(PLS)方法的多模式集成效果最好,不但优于4种单一模式而且表现出比其他3种方法更好的预报性能,具有一定的价值以及应用前景。

The use of a new multi model integration method of Partial Least Squares regression（ PLS） can completely eliminate the multicollinearity features to improve multi model＇s integrated forecasting results of the humidity and temperature. Based on the four centers＇ ensemble forecast results,namely,the European Center for Medium-Range Weather Forecasts（ ECMWF）,Chinese Meteorological Administration（ CMA）,the Japan Meteorological Agency（ JMA） and the UK Met Office（ UKMO）,we built a 2012 multi mode（ 25° - 60°N,60° - 150°E）24 - 168 hours forecast time（ interval 24 hours） multi model for humidity and temperature and used the four methods,like ensemble average（ BREM） for eliminating the deviation,a simple set of average（ EMN）,Super Ensemble（ SUP） and Partial Least Squares regression（ PLS） for ground temperature multi model integration. We used the Root-Mean-Square Error（ RMSE） and anomaly correlation coefficient（ cor） to determine the effect of more modes of integration and to predict a short course of cold. The two prediction results showed that the Partial Least Squares regression（ PLS） was the best multi model integrated method,more superior than the other three single modes and compared with the other three methods,it showed better prediction performance,which has certain value and application prospect.