Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

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  • Funds:

    Supported by the National Basic Research and Development (973) Program of China (2010CB428402) and China Meteorological Administration Special Public Welfare Research Fund (GYHY200706001).

  • doi: 10.1007/s13351-012-0101-8

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  • A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and application of this method to the downscaling of climate change scenarios were discussed.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

  • 1. College of Global Change and Earth System Science,Beijing Normal University,Beijing 100875 Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029;
    Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029;
    Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029
Funds: Supported by the National Basic Research and Development (973) Program of China (2010CB428402) and China Meteorological Administration Special Public Welfare Research Fund (GYHY200706001).

Abstract: A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and application of this method to the downscaling of climate change scenarios were discussed.

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