改善水文气象预报的统计后处理.pdfVIP

Journal of Water Resources Research 水资源研究, 2012, 1, 161-168 doi:10.4236/jwrr.2012.14023 Published Online August 2012 (/journal/jwrr.html) Statistical Post-Processing to Improve * Hydrometeorological Forecasts # Qingyun Duan , Aizhong Ye College of Global Change and Earth System Science, Beijing Normal University, Beijing Email: #qyduan@ Received: Apr. 11th, 2012; revised: Apr. 23rd, 2012; accepted: May 2nd, 2012 Abstract: Hydrologic forecasts based on direct outputs from a hydrologic model contain significant uncer- tainty from various sources, including model inputs, initial/boundary conditions and model structure/model parameters. The uncertainty leads to various biases in the hydrologic forecasts. Before issuing final hydro- logic forecasts to the forecast users, it is necessary to remove these biases. A statistical post-processor is an effective tool than can be used to remove various biases from the hydrometeorological forecasts. In this paper, we briefly describe two practical post-processing methods: 1) The ensemble pre-processor for post-process- ing quantitative precipitation and temperature forecasts; and 2) The generalized linear models for post-proc- essing streamflow forecasts. We demonstrated the effectiveness of these two methods in China’s Huai River basin and the French Broad River basin in the US. Results clearly show that post-processing can significantly improve the raw hydrometeorological forecasts. An interesting observation is that post-processing can achi- eve the same degree of improvement in streamflow simulation as model calibration. This