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Volume 22 Issue 5

Sep.  2005

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2005 >  22(5): 703-710

Statistical Prediction of Heavy Rain in South Korea

  • 1.

    Pusan National University, Busan 609-735, Korea,Dong-A University, Busan 604-714, Korea,Chosun University, Gwangju 501-759, Korea,Chosun University, Gwangju 501-759, Korea


doi: 10.1007/BF02918713

  • This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km× 10 km area-mean amount of rainfall at 6 stations (Seoul, Daejeon, Gangreung, Gwangju, Busan, and Jeju) in South Korea are used. And the corresponding 45 synoptic factors generated by the numerical model are used as potential predictors. Four statistical forecast models (linear regression model, logistic regression model, neural network model and decision tree model) for the occurrence of heavy rain are based on the model output statistics (MOS) method. They are separately estimated by the same training data. The thresholds are considered to forecast the occurrence of heavy rain because the distribution of estimated values that are generated by each model is too skewed.The results of four models are compared via Heidke skill scores. As a result, the logistic regression model is recommended.
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    [2] Chaoqun MA, Tijian WANG, Zengliang ZANG, Zhijin LI, 2018: Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 813-825.  doi: 10.1007/s00376-017-7179-y
    [3] Xu Liren, Zhao Ming, 2000: The Influences of Boundary Layer Parameterization Schemes on Mesoscale Heavy Rain System, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 458-472.  doi: 10.1007/s00376-000-0036-3
    [4] SUN Li, SHEN Baizhu, SUI Bo, 2010: A Study on Water Vapor Transport and Budget of Heavy Rain in Northeast China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1399-1414.  doi: 10.1007/s00376-010-9087-2
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    • Manuscript History

    Manuscript received: 10 September 2005
    Manuscript revised: 10 September 2005
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Statistical Prediction of Heavy Rain in South Korea

    • 1. Pusan National University, Busan 609-735, Korea,Dong-A University, Busan 604-714, Korea,Chosun University, Gwangju 501-759, Korea,Chosun University, Gwangju 501-759, Korea
    • Manuscript received: 2005-09-10
    • Manuscript revised: 2005-09-10

    Abstract: This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km× 10 km area-mean amount of rainfall at 6 stations (Seoul, Daejeon, Gangreung, Gwangju, Busan, and Jeju) in South Korea are used. And the corresponding 45 synoptic factors generated by the numerical model are used as potential predictors. Four statistical forecast models (linear regression model, logistic regression model, neural network model and decision tree model) for the occurrence of heavy rain are based on the model output statistics (MOS) method. They are separately estimated by the same training data. The thresholds are considered to forecast the occurrence of heavy rain because the distribution of estimated values that are generated by each model is too skewed.The results of four models are compared via Heidke skill scores. As a result, the logistic regression model is recommended.

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