Advances in Earth Science ›› 2018, Vol. 33 ›› Issue (4): 404-415. doi: 10.11867/j.issn.1001-8166.2018.04.0404

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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   

  1. 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;
  • About author:

    First author:Li Jiangfeng(1993-),male,Tongshan County,Hubei Province,Master student. Research areas include multi-mode integration

  • 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).

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[J]. Advances in Earth Science, 2018, 33(4): 404-415.

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.

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