Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging |
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| Authors: | Chi Yang Zhongwei Yan Yuehong Shao |
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| Institution: | 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
2. Key Laboratory of Regional Climate-Environment Research for Temperate East Asia,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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| 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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| Keywords: | Bayesian model averaging generalized additive model probabilistic precipitation forecasting TIGGE Tweedie distribution |
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