Statistical Post-Processing to Improve Hydrometeorological Forecasts *

th , 2012; revised: Apr. 23 rd , 2012; accepted: May 2 nd , 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 signifi- cantly improve the raw hydrometeorological forecasts. An interesting observation is that post-processing can achieve the same degree of improvement in streamflow simulation as model calibration. This suggests that, for basins where calibration cannot be done properly due to data issues (i.e., streamflow regulations), we can use post-processing to compensate for the lack of model calibration.