考虑降水预报的三峡入库洪水集合概率预报方法比较

为了考虑预见期内降水预报的不确定性对洪水预报的影响,采用中国气象局、美国环境预测中心和欧洲中期天气预报中心的TIGGE(THORPEX Interactive Grand Global Ensemble)降水预报数据驱动GR4J水文模型,开展三峡入库洪水集合概率预报,分析比较BMA、Copula-BMA、EMOS、M-BMA 4种统计后处理方法的有效性。结果表明:4种统计后处理方法均能提供一个合理可靠的预报置信区间;其期望值预报精度相较于确定性预报有所提高,尤其是水量误差显著减小;M-BMA方法概率预报效果最佳,它能够考虑预报分布的异方差性,不需要进行正态变换,结构简单,应用灵活。

Comparative study on probabilistic ensemble flood forecasting considering precipitation forecasts for the Three Gorges Reservoir

To investigate how uncertainty in precipitation forecasts impacts flood forecasting,the THORPEX Interactive Grand Global Ensemble (TIGGE) data extracted from the China Meteorological Administration (CMA),the National Center for Environmental Prediction (NCEP) and the European Center for Medium-range Weather Forecast (ECMWF) were used to establish the GR4J hydrological model such that probabilistic ensemble flood forecasting is explored for the Three Gorges Reservoir. The effectiveness of four statistical post-processing methods,including Bayesian Model Averaging (BMA),Copula-BMA,Ensemble Model Output Statistics (EMOS) and the Modified Bayesian Model Averaging (M-BMA) methods,were compared and analyzed. The results showed that each of the four methods could provide a reasonable and reliable confidence interval on prediction. Besides,compared with the raw deterministic forecasts,the forecast accuracy of expected values associated with the four methods was improved,where the forecast error in water volume was significantly reduced. Furthermore,the M-BMA method performed the best because it considered the heteroscedasticity of the predictive distribution,without conducting a normal transformation,which could be much simpler and more flexible in practice.