Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging

@inproceedings{Qu2017MultiModelGE,
  title={Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging},
  author={Bo Qu and Xingnan Zhang and Florian Pappenberger and Tao Zhang and Yuanhao Fang},
  year={2017}
}
Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA) to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging… CONTINUE READING