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地球科学进展  2018, Vol. 33 Issue (4): 404-415    DOI: 10.11867/j.issn.1001-8166.2018.04.0404
研究论文     
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
李江峰(), 蔡晓军*(), 王文, 李倩文, 雷彦森
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
Application of Partial Least Squares Regression in Multimodal Integrated Forecasting of Water Vapor and Surface Air Temperature
Jiangfeng Li(), Xiaojun Cai*(), Wen Wang, Qianwen Li, Yansen Lei
Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology/Joint Laboratory of International Cooperation on Climate and Environment Change/Center for Innovation and Coordination of Meteorological Disaster Prediction and Early Warning and Assessment, Nanjing 210044, China
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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24168 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。

关键词: 偏最小二乘回归(PLS)多模式集成预报地面气温比湿    
Abstract:

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.

Key words: Partial Least Squares regression (PLS)    Multi model ensemble forecast    Surface air temperature    Humidity.
收稿日期: 2017-09-30 出版日期: 2018-05-24
ZTFLH:  P423.4  
基金资助: *国家自然科学基金项目“长江中下游流域多尺度干旱指标的适应性研究”(编号:41279051)资助.
通讯作者: 蔡晓军     E-mail: 18751978762@163.com;wwllw003@126.com
作者简介:

作者简介:李江峰(1993-),男,湖北通山人,硕士研究生,主要从事多模式集成研究.E-mail:18751978762@163.com

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引用本文:

李江峰, 蔡晓军, 王文, 李倩文, 雷彦森. 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[J]. 地球科学进展, 2018, 33(4): 404-415.

Jiangfeng Li, Xiaojun Cai, Wen Wang, Qianwen Li, Yansen Lei. Application of Partial Least Squares Regression in Multimodal Integrated Forecasting of Water Vapor and Surface Air Temperature. Advances in Earth Science, 2018, 33(4): 404-415.

链接本文:

http://www.adearth.ac.cn/CN/10.11867/j.issn.1001-8166.2018.04.0404        http://www.adearth.ac.cn/CN/Y2018/V33/I4/404

图1  多模式集成预报流程示意图
图2  2012年12月131日每日24 h(a),48 h(b),72 h(c),96 h(d),120 h(e),144 h(f),168 h(g)地面气温预报在25°60°N,60°150°E区域的平均均方根误差
图3  2012年12月131日8种模式气温分别与FNL资料气温的日平均距平相关系数
图4  2012年12月2729日寒潮过程地面气温“实况场”与CMA(a),EC(b),UKMO(c),JMA(d),PLS(e),SUP(f),BREM(g),EMN(h)24168 h平均均方根误差的地理分布(单位:℃) 右上角RMSE表示平均均方根误差
图5  2012年6月1日至8月31日每日24 h(a),48 h(b),72 h(c),96 h(d),120 h(e),144 h(f),168 h(g)850 hPa比湿预报的25°60°N,60°150°E区域的平均均方根误差
图6  2012年6月1日至8月31日850 hPa比湿“实况场”分别与CMA(a),EC(b),JMA(c),UKMO(d), EMN(e),BREM(f),SUP(g),PLS(h)24168 h平均均方根误差的地理分布(单位:g/kg) 左上角RMSE表示平均均方根误差
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