摘要
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。
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.
作者
巴欢欢
郭生练
钟逸轩
刘章君
吴旭树
何绍坤
BA Huanhuan;GUO Shenglian;ZHONG Yixuan;LIU Zhangjun;WU Xushu;HE Shaokun(State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 430072,China;Jiangxi Provincial Institute of Water Sciences,Nanchang 330029,China)
出处
《水科学进展》
EI
CAS
CSCD
北大核心
2019年第2期186-197,共12页
Advances in Water Science
基金
国家自然科学基金资助项目(51539009)
国家重点研发计划资助项目(2016YFC0402206)~~
关键词
数值降水预报
水文集合预报
统计后处理
概率预报
三峡水库
numerical precipitation forecast
hydrological ensemble prediction
post-processing
probabilistic forecast
Three Gorges Reservoir