• 研究论文 •

### 基于贝叶斯模式平均与标准化异常度的东江汛期降水预报

1. 中山大学水资源与环境系，广东 广州510275
• 收稿日期:2016-02-25 出版日期:2016-11-25 发布日期:2016-11-25

### Precipitation forecasting in flood season over the Dongjiang Basin using Bayesian model averaging and standardized anomaly

WU Yuzhen, FENG Zhizhou, WANG Dagang

1. Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China
• Received:2016-02-25 Online:2016-11-25 Published:2016-11-25

Abstract:

Bayesian Model Averaging (BMA) is applied to monthly precipitation forecasting in the flood season over the Dongjiang basin to correct the bias of Climate Forecast System version2 (CFSv2). In the meantime, Standardized Anomaly (SA) is used to quantify the precipitation abnormality and incorporated into the deterministic and ensemble forecasting. A better precipitation forecasting model is then established by the combination of BMA and SA to improve accuracy of long-term precipitation forecasting in the Dongjiang basin. Conclusions are drawn as follows: ① The 50th percentile and below of ensemble forecasting have poor skill , whereas the 75th percentile is usually in agreement with observations. However, BMA has disadvantage in that it underestimates precipitation amount when extreme events occur. ② The value of SA based on the ensemble average of CFSv2 is too small, indicating a systematic bias of CFSv2. When the CFSv2 raw forecasting is corrected by gamma function and multinomial, both Threat Score and the number of greatly increases but Bias Score increases in the meanwhile; ③ The relationship between SA and BMA can be expressed as follows: the 95th percentile of ensemble forecasting is used when SA indicates an abnormal precipitation, otherwise the 75th percentile is used.