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《Journal of Arid Meteorology》 2015-06
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Application of Partial Least Square Regression on Precipitation Prediction During the Flood Season in Liaoning Province

ZHAO Zhongjun;LIU Shanliang;YOU Daming;WANG Xuezhong;HU Banghui;CHENG Yifan;Troop Unit 92493 of PLA;Troop Unit 61741 of PLA;Institute of Meteorology and Oceanography,PLAUST;College of Atmospheric Science,Lanzhou University;  
Based on the monthly mean temperature and precipitation at 160 weather stations in China during 1951- 2011,74 circulation indexes from National Climate Center and the monthly SST from NCEP / NCAR,combined with mean generating function( MGF),the prediction models of precipitation during the flood season in 5 stations of Liaoning Province were respectively established by using the partial least square regression( PLSR). And the effects predicted by PLS model on precipitation were tested. The result showed that the periodic factor of the prediction founded by MGF could weaken the correlation of the predictors in the statistic model to some extent. The root mean square error( RMSE) of mean precipitation during the flood season in Liaoning Province predicted by PLSR model considering the periodicity from 2002 to 2011 was reduced by 10. 0 mm. The effect predicted by PLSR model on precipitation during the flood season was much more efficient in comparison with that predicted by step wise regression( SWR) model owing to better resolve about the multi- correlation problem. The mean prediction skill score of precipitation during the flood season in 5 stations from2002 to 2011 was 72. 6%,which improved by 10. 3% than that of SWR model.
【Fund】: 公益性行业(气象)专项(GYHY201306047 GYHY201206004)资助
【CateGory Index】: P426.6;P457.6
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【Citations】
Chinese Journal Full-text Database 1 Hits
1 JIN Long1,LUO Ying2,WANG Yehong3,LI Yonghua4(1. Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022, China; 2. Guangxi Climate Center, Nanning 530022, China; 3. Nanjing Institute of Meteorology, Nanjing 210044, China;4. Meteorological Observation of Chongqing, Chongqing 400039, China);Study on Mixed Forecast Model of Neural Network of Monthly Precipitation[J];Plateau Meteorology;2003-06
【Co-citations】
Chinese Journal Full-text Database 10 Hits
1 JIN Long ~(1), WU Jiang-sheng~(2), LIN Kai-ping~(3), CHEN Bing-lian~4(1.Guangxi Research Institute of Meteorological Disasters Mitigation,Nanning 530022,China;2.Guangxi Normal University,Guiling 541001,China;3.Meteorological Bureau of Nanning,Nanning 530022,China;4.Department of Information Technology,Guangxi Normal College,Nanning 530021, China);Short-Term Climate Prediction Model of Neural Network Based on Genetic Algorithms[J];Plateau Meteorology;2005-06
2 MA Xue-kuan1,PUBu CiRen2,TANG Shu-yi2,LIN Zhi-qiang2(1.National Meteorological Center,China Meteorological Administation,Beijing 100081,China;2.Tibet Meteorological Bureau,Lhasa 850000,China);The Application of Artificial Neural Network to Short and Mid-Range Temperature Forecast in Tibet[J];Plateau Meteorology;2007-03
3 LI Hong-bin1,FANG De-xian2,HOU Guo-cheng3,ZHANG Dian-gang1,CHEN Nong4,PU Wen-yao1(1.Dalian Weather Modification Office,Dalian 116001,China;2.Weather Bureau of Chongqing,Chongqing 401147,China;3.Weather Bureau of Dalian,Dalian 116001,China;4.Heilongjiang Provincial Weather Modification Office,Harbin 150003,China);Automatic Command System of Artificial Precipitation Based on Modularizing Design[J];Plateau Meteorology;2008-06
4 SHAO Yue-hong1,ZHANG Wan-chang2,LIU Yong-he2,SUN Cheng-wu3,FU Cheng-yu3(1.International Institute for Earth System Science,Nanjing University,Nanjing 210093,China;2.START Regional Center for Temperate East Asia,Institute of Atmospheric Physics,Chinese Academyof Sciences,Beijing 100029,China;3.Linyi Meteorological Bureau,Linyi 276004,China);Application of Back-Propagation Neural Network in Precipitation Estimation with Doppler Radar[J];Plateau Meteorology;2009-04
5 HUANG Xiao-yan~1,SHI X-ming~2,LIU Su-dong~3,JIN Long~2 (1.Guangxi Meteorological Observatory,Nanning 530022,China; 2.Guangxi Research Institute of Meteorological Disasters Mitigation,Nanning 530022,China; 3.Guizhou Immigration Bureau of Water Conservancy and Hydropower Engineering,Guiyang 550001,China);Research and Applying of Forecasting Tropical Cyclone Intensity Based on Fuzzy Neural Network[J];Plateau Meteorology;2009-06
6 ZENG Xiao-qing~1,WANG Shi-gong~1,LIU Huan-zhu~2,SHANG Ke-zhen~1 (1.College of Atmospheric Sciences,Lanzhou University/Gansu Key Laboratory of Arid Climate Change and Reducing Disaster,Lanzhou 730000,China; 2.National Meteorological Center,China Meteorological Administration,Beijing 100081,China);Application of Statistical Downscaling in Minimum Temperature Prediction over Qinghai Province in Winter[J];Plateau Meteorology;2009-06
7 HU Chun-mei 1,CHEN Dao-jin 1,YU Run-ling 2 (1.Chongqing Meteorological Office,Chongqing 401147,China;2.Shanghai Typhoon Institute,China Meteorological Administration,Shanghai 200030,China);Application of BP Artificial Neural Network and Support Vector Machines to Ultraviolet Radiation Prediction[J];Plateau Meteorology;2010-02
8 LIU Honglan;ZHANG Junguo;QUE Longkai;ZHENG Xuejin;BAO Jiazhi;Institute of Arid Meteorology,China Meteorological Administration/Key Laboratory of Arid Climatic Change and Disaster Reduction of Gansu Province /Key Laboratory of Arid Climate Change and Disaster Reduction of China Meteorological Administration;Zhangye Meteorological Bureau;Zhangye Middle school;;Forecasting Model for Ice Thickness in Zhangye National Wetland Park Watershed Based on BP Neural Network[J];Plateau Meteorology;2014-03
9 QIN Wei-jian;HUANG Zhi;LI YAO-XIAN;Guangxi Climate Center;Guangxi Meteorological Information center;Guangxi Institute of Meteorology and Disaster-Mitigation Research;;Short-term climate forecast method research on the start of cold dew wind in Guangxi[J];Journal of Meteorological Research and Application;2014-03
10 Qin Wei-jian;Li Yao-xian;Chen Si-rong;Xie Min;Guangxi Climate Center;Guangxi Meteorological Disaster Reduction;;Application on the prediction of the summer precipitation in South China basing on PSO-Artificial Neutral Network[J];Journal of Meteorological Research and Application;2015-02
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