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Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts
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DOI：10.1016/j.jhydrol.2021.127301.png)
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Abstract
En 中文
Raw forecasts from numerical weather prediction models suffer from systematic bias and cannot be directly used in applications such as hydrological forecasting. Statistical post-processing methods can be used to remove the bias and achieve reliable ensemble forecasts. However, traditional post-processing methods generally use local raw forecasts as the only predictor, which limits their ability to extract spatial information from raw forecasts. In this paper, we develop a convolutional neural network (CNN)-based post-processing method for precipitation forecasts to make use of spatial information and atmospheric circulation variables as auxiliary predictors. The results show that the proposed CNN-based post-processing model outperforms traditional methods in forecast accuracy and reliability, especially for heavy rain. The improvements of the CNN-based model relative to a state-of-the-art joint probability model can reach 5% and 8% in terms of Brier skill score for heavy rain and rainstorms at the lead time of 1 day. The improvements can be attributed to the use of auxiliary predictors such as total column water forecasts in the proposed model. Moreover, the CNN-based model transcends a fully connected network (FCN)-based post-processing model, which illustrates the benefits of using CNNs to extract spatial information. The results illustrate the proposed CNN-based post-processing model is able to utilize spatial information and various auxiliary predictors to improve forecast skill and quantify forecast uncertainty, which is important for further applications such as hydrological forecasting.
Keywords:
Statistical post-processing
Precipitation forecast
Convolutional neural network
Hydrological ensemble forecasting
Journal
IF:
6.3
Papers:
2.3W
Citations:
9.8W
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