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Volume 20 Issue 4

Jul.  2003

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2003 >  20(4): 575-582

The 3-Hour-Interval Prediction of Ground-Level Temperature in South Korea Using Dynamic Linear Models

  • 1.

    Dept. of Statistics, Pusan National University, 30 Jangjeon-dong, Geumjung-gu, Pusan 609-735, Korea,Korea Meteorological Administration, 460-18 Shindaebang-dong, Tongjak-gu, Seoul 156-720, Korea,Korea Meteorological Administration, 460-18 Shindaebang-dong, Tongjak-gu, Seoul 156-720, Korea


doi: 10.1007/BF02915500

  • The 3-hour-interval prediction of ground-level temperature from +00 h out to +45 h in South Korea(38 stations) is performed using the DLM (dynamic linear model) in order to eliminate the systematicerror of numerical model forecasts. Numerical model forecasts and observations are used as input values ofthe DLM. According to the comparison of the DLM forecasts to the KFM (Kalman filter model) forecastswith RMSE and bias, the DLM is useful to improve the accuracy of prediction.
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    • Manuscript History

    Manuscript received: 10 July 2003
    Manuscript revised: 10 July 2003
    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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    The 3-Hour-Interval Prediction of Ground-Level Temperature in South Korea Using Dynamic Linear Models

    • 1. Dept. of Statistics, Pusan National University, 30 Jangjeon-dong, Geumjung-gu, Pusan 609-735, Korea,Korea Meteorological Administration, 460-18 Shindaebang-dong, Tongjak-gu, Seoul 156-720, Korea,Korea Meteorological Administration, 460-18 Shindaebang-dong, Tongjak-gu, Seoul 156-720, Korea
    • Manuscript received: 2003-07-10
    • Manuscript revised: 2003-07-10

    Abstract: The 3-hour-interval prediction of ground-level temperature from +00 h out to +45 h in South Korea(38 stations) is performed using the DLM (dynamic linear model) in order to eliminate the systematicerror of numerical model forecasts. Numerical model forecasts and observations are used as input values ofthe DLM. According to the comparison of the DLM forecasts to the KFM (Kalman filter model) forecastswith RMSE and bias, the DLM is useful to improve the accuracy of prediction.

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