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

doi:10.11867/j.issn.1001-8166.2018.04.0404

地球科学进展 ›› 2018, Vol. 33 ›› Issue (4): 404 -415. doi: 10.11867/j.issn.1001-8166.2018.04.0404

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

收稿日期:2017-09-30 修回日期:2018-01-08 出版日期:2018-04-20
通讯作者: 蔡晓军 E-mail:18751978762@163.com;wwllw003@126.com
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.

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

Received:2017-09-30 Revised:2018-01-08 Online:2018-04-20 Published:2018-05-24
Contact: Xiaojun Cai E-mail:18751978762@163.com;wwllw003@126.com
About author:
First author:Li Jiangfeng(1993-),male,Tongshan County,Hubei Province,Master student. Research areas include multi-mode integration research.E-mail:18751978762@163.com
Supported by:
Project supported by the National Natural Science Foundation of China “Study on the adaptability of multi-scale drought indexes in the middle and lower reaches of the Yangtze River”(No.41279051).

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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

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.

中图分类号:

P423.4

图1 多模式集成预报流程示意图

Fig.1 Schematic diagram of multi-mode integrated forecasting process

图1 多模式集成预报流程示意图

Fig.1 Schematic diagram of multi-mode integrated forecasting process

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

Fig.2 December 1,2012 to December 31,2012 daily 24 h(a),48 h(b),72 h(c),96 h(d),120 h(e),144 h(f),168 h(g) surface air temperature in 25°60°N,60°150°E area average Root-Mean-Square Error

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

Fig.2 December 1,2012 to December 31,2012 daily 24 h(a),48 h(b),72 h(c),96 h(d),120 h(e),144 h(f),168 h(g) surface air temperature in 25°60°N,60°150°E area average Root-Mean-Square Error

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

Fig.3 Daily average Anomaly correlation coefficient between FNL air temperature and eight modes air temperature from December 1, 2012 to December 31, 2012

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

Fig.3 Daily average Anomaly correlation coefficient between FNL air temperature and eight modes air temperature from December 1, 2012 to December 31, 2012

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

Fig.4 December 27, 2012 to December 29, 2012 cold-air outbreak surface air temperature “real ” and CMA(a), EC(b),UKMO(c),JMA(d),PLS(e),SUP(f),BREM(g),EMN(h)24168 h average Root-Mean-Square Error geographical distribution(unit:℃)
In the upper right corner RMSE express the average Root-Mean-Square Error

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

Fig.5 June 1, 2012 to August 31, 2012 daily 24 h(a),48 h(b),72 h(c),96 h(d),120 h(e),144 h(f),168 h(g) specific humidity in 25°60°N,60°150°E area average Root-Mean-Square Error

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

Fig.5 June 1, 2012 to August 31, 2012 daily 24 h(a),48 h(b),72 h(c),96 h(d),120 h(e),144 h(f),168 h(g) specific humidity in 25°60°N,60°150°E area average Root-Mean-Square Error

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

Fig.6 June 1, 2012 to August 31, 2012 specific humidity “real” and CMA(a),EC(b),JMA(c),UKMO(d),EMN(e), BREM(f),SUP(g),PLS(h) 24168 h average Root-Mean-Square Error geographical distribution(unit:g/kg)
In the upper right corner RMSE express the average Root-Mean-Square Error

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