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
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摘要
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.
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.
引文
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[18]Li Qian,Hu Banghui,Wang Xuezhong,et al.Multi model integrated prediction experiment of regional temperature based on BP artificial neural network[J].Arid Meteorology,2011,29(2):231-235.[李倩,胡邦辉,王学忠,等.基于BP人工神经网络的区域温度多模式集成预报试验[J].干旱气象,2011,29(2):231-235.]
[19]Lin Chunze,Zhi Xiefei,Han Yan,et al.Multi model super set forecast of surface temperature based on TIGGE data[J].Journal of Applied Meteorological Science,2009,20(6):706-712.[林春泽,智协飞,韩艳,等.基于TIGGE资料的地面气温多模式超级集合预报[J].应用气象学报,2009,20(6):706-712.]
[2]Du Jun.The status and prospect of ensemble forecast[J].Journal of Applied Meteorology,2002,13(1):16-28.[杜钧.集合预报的现状和前景[J].应用气象学报,2002,13(1):16-28.]
[3]Epstein E S.Stochastic dynamic prediction[J].Tellus,1969,21:739-759.
[4]Leith C E.Theoretical skill of Monte Carlo forecasts[J].Monthly Weather Review,1974,102:409-418.
[5]Thompson P D.How to improve accuracy by combining independent forecasts[J].Monthly Weather Review,1977,105(2):228-229.
[6]Krishnamurti T N,Kishtawal C M,La Row T E,et al.Improved weather and seasonal climate forecasts from multimodel SUPerensemble[J].Science,1999,285(5 433):1 548-1 550.
[7]Yang Xuesheng.The present situation and prospect of service ensemble forecasting system[J].Meteorological Monthly,2001,27(6):3-9.[杨学胜.业务集合预报系统的现状及展望[J].气象,2001,27(6):3-9.]
[8]Vijaya Kumar T S V,Krishnamurti T N,Fiorino M,et al.Multimodel superensemble forecasting of tropical cyclones in the Pacific[J].Monthly Weather Review,2003,131(3):574-583.
[9]Ross R S,Krishnamurti T N.Reduction of forecast error for global numerical weather prediction by the Florida State University(FSU)superensemble[J].Meteorology and Atmospheric Physics,2005,88(3):215-235.
[10]Krishnamurti T N,Sagadevan A D,Chakraborty A,et al.Improving multimodel weather forecast of monsoon rain over China using FSU superensemble[J].Advances in Atmospheric Sciences,2009,26(5):813-839.
[11]Bao Hongjun.Inspection and application of multi-model integrated quantitative precipitation forecast in a storm flood forecast[C]∥S12 Meteorological,Geo-hazard Meteorological Forecasting and Services.Chinese Meteorological Society,2012.[包红军.多模式集成定量预报降水在一次暴雨洪水预报中的检验与应用[C]∥S12水文气象、地质灾害气象预报与服务.中国气象学会,2012.]
[12]Chen Chaohui,Wang Tie,Tan Yanke,et al.Study on multimode short-term ensemble forecasting method for Jianghuai flood season in 2003[J].Journal of Tropical Meteorology,2009,25(4):449-457.[陈超辉,王铁,谭言科,等.2003年江淮汛期多模式短期集合预报方法研究[J].热带气象学报,2009,25(4):449-457.]
[13]Zhang Yazhou,Li Shenglin,Deng Wenbin,et al.Experiment of tropical cyclone precipitation forecast based on multi-mode shortterm ensemble forecasting[J].Meteorology and Disaster Reduction Research,2011,34(2):26-33.[张亚洲,李盛林,邓文彬,等.基于多模式短期集合预报技术的热带气旋降水预报试验[J].气象与减灾研究,2011,34(2):26-33.]
[14]Tang Shengjun,Wang Donghai,Du Jun,et al.Experiment of mixed ensemble forecasting method for short term Rainstorm Forecast in Southern China[J].Journal of Applied Meteorological Science,2015,26(6):669-679.[唐圣钧,王东海,杜钧,等.混合集合预报法在华南暴雨短期预报中的试验[J].应用气象学报,2015,26(6):669-679.]
[15]Wei Fengying.Integrated weighted prediction experiment of regional summer rainfall in China[J].Journal of Applied Meteorological Science,1999,10(4):402-409.[魏凤英.全国夏季降水区域动态权重集成预报试验[J].应用气象学报,1999,10(4):402-409.]
[16]Zhi Xiefei,Ji Xiaodong,Zhang Jing,et al.Multimodal integrated forecasting of ground temperature and precipitation based on TIGGE data[J].Journal of Nanjing Institute of Meteorology,2013,36(3):257-266.[智协飞,季晓东,张璟,等.基于TIGGE资料的地面气温和降水的多模式集成预报[J].大气科学学报,2013,36(3):257-266.]
[17]Yu Shuiyan,Zhi Xiefei.Multimodal Integrated Forecasting of Water Vapor in Eastern China[C]∥The Thirty-first Session of the Annual Meeting.Beijing,2014.[于水燕,智协飞.中国东部水汽的多模式集成预报研究[C]∥第31届中国气象学会年会S2灾害天气监测,分析与预报.北京,2014.]
[18]Li Qian,Hu Banghui,Wang Xuezhong,et al.Multi model integrated prediction experiment of regional temperature based on BP artificial neural network[J].Arid Meteorology,2011,29(2):231-235.[李倩,胡邦辉,王学忠,等.基于BP人工神经网络的区域温度多模式集成预报试验[J].干旱气象,2011,29(2):231-235.]
[19]Lin Chunze,Zhi Xiefei,Han Yan,et al.Multi model super set forecast of surface temperature based on TIGGE data[J].Journal of Applied Meteorological Science,2009,20(6):706-712.[林春泽,智协飞,韩艳,等.基于TIGGE资料的地面气温多模式超级集合预报[J].应用气象学报,2009,20(6):706-712.]