Journal of Arid Meteorology

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Application of Partial Least Square Regression on Precipitation Prediction During the Flood Season in Liaoning Province


  1. 1. Troop Unit 92493 of PLA, Huludao 125000, China;
    2. Troop Unit 61741 of PLA, Beijing 100081, China;
    3. Institute of Meteorology and Oceanography, PLAUST, Nanjing 211101, China;
    4. College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China
  • Online:2015-12-31 Published:2015-12-31



  1. 1. 中国人民解放军92493部队,辽宁葫芦岛125000;
    2. 中国人民解放军61741部队,北京100081;
    3. 解放军理工大学气象海洋学院,江苏南京211101;
    4. 兰州大学大气科学学院,甘肃兰州730000
  • 通讯作者: 程一帆(1983- ),男,甘肃榆中人,工程师,博士,主要从事现代天气预报技术研究工作
  • 作者简介:赵中军(1965- ),男,黑龙江五常人,高级工程师,硕士,主要从事水文气象预报技术和保障相关工作
  • 基金资助:



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 from 2002 to 2011 was 72.6%, which improved by 10.3% than that of SWR model.

Key words: partial least square regression, mean generating function, precipitation prediction during the flood season


利用1951~2011年中国160个气象站逐月降水、温度、74项环流指数和NCEP再分析海表温度资料,采用偏最小二乘回归(PLSR)方法,结合均生函数构造预报量周期性因子,建立辽宁省汛期平均降水量及其5站(沈阳、朝阳、营口、丹东和大连)汛期降水量预测模型,并进行预测效果检验分析。结果表明:采用均生函数构造预报量周期性因子,在一定程度上弥补了气候预测统计模型高相关性因子的不足,从而使辽宁汛期平均降水量PLSR模型的试报均方根误差降低约10 mm。PLSR模型由于较好地解决了预报因子之间的多重相关性问题,其预测效果较逐步回归模型有明显提高,对2002~2011年辽宁5站汛期降水量试报的Ps评分平均值为72.6%,比逐步回归模型提高了10.3%。

关键词: 偏最小二乘回归, 均生函数, 汛期降水预测

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