考虑降水预报的三峡入库洪水集合概率预报方法比较
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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 GR4 J 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.
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 GR4 J 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.
引文
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[14] LI L,XU C Y,XIA J,et al.Uncertainty estimates by Bayesian method with likelihood of AR(1) plus Normal model and AR(1) plus Multi-Normal model in different time-scales hydrological models [J].Journal of Hydrology,2011,406(1/2):54- 65.
[15] 邓鹏鑫,王银堂,胡庆芳,等.GR4J模型在赣江流域日径流模拟中的应用[J].水文,2014,34(2):60- 65.(DENG P X,WANG Y T,HU Q F,et al.Application of GR4J in daily runoff simulation for Ganjiang river Basin [J].Journal of China Hydrology,2014,34(2):60- 65.(in Chinese))
[16] ZHAO T,WANG Q J,BENNETT J C,et al.Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model[J].Journal of Hydrology,2015,528:329-340.
[17] VOGEL P,KNIPPERTZ P,FINK A H,et al.Skill of global raw and postprocessed ensemble predictions of rainfall over Northern Tropical Africa [J].Weather and Forecasting,2018,33(2):369-388.
[2] 陆桂华,吴娟,吴志勇.水文集合预报试验及其研究进展[J].水科学进展,2012,23(5):728-734.(LU G H,WU J,WU Z Y.Advances in hydrologic ensemble prediction experiment [J].Advances in Water Science,2012,23(5):728-734.(in Chinese))
[3] 彭勇,徐炜,王萍,等.耦合TIGGE降水集合预报的洪水预报[J].天津大学学报(自然科学与工程技术版),2015(2):177-184.(PENG Y,XU W,WANG P,et al.Flood forecasting coupled with TIGGE ensemble precipitation forecasts [J].Journal of Tianjin University (Science and Technology),2015(2):177-184.(in Chinese))
[4] 李麒崙,张万昌,易路,等.GPM与TRMM降水数据在中国大陆的精度评估与对比[J].水科学进展,2018,29(3):303-313.(LI Q L,ZHANG W C,YI L,et al.Accuracy evaluation and comparison of GPM and TRMM precipitation product over Mainland China [J].Advances in Water Science,2018,29(3):303-313.(in Chinese))
[5] 梁忠民,蒋晓蕾,曹炎煦,等.考虑降雨不确定性的洪水概率预报方法[J].河海大学学报(自然科学版),2016(1):8-12.(LIANG Z M,JIANG X L,CAO Y X,et al.Probabilistic flood forecasting considering rainfall uncertainty[J].Journal of Hohai University (Natural Sciences),2016(1):8-12.(in Chinese))
[6] 代刊,朱跃建,毕宝贵.集合模式定量降水预报的统计后处理技术研究综述[J].气象学报,2018,76(4):493-510.(DAI K,ZHU Y J,BI B G.The review of statistical post-process technologies for quantitative precipitation forecast of ensemble prediction system[J].Acta Meteorologica Sinica,2018,76(4):493-510.(in Chinese))
[7] BARAN S,LERCH S.Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting [J].Quarterly Journal of the Royal Meteorological Society,2015,141(691):2289-2299.
[8] ZHONG Y X,GUO S L,BA H H,et al.Evaluation of the BMA probabilistic inflow forecasts using TIGGE numeric precipitation predictions based on artificial neural network [J].Hydrology Research,2018,49(5):1417-1433.
[9] SLOUGHTER J M,GNEITING T,RAFTERY A E.Probabilistic wind speed forecasting using ensembles and Bayesian Model Averaging [J].Journal of the American Statistical Association,2010,105(489):25-35.
[10] MADADGAR S,MORADKHANI H.Improved Bayesian multimodeling:integration of copulas and Bayesian model averaging [J].Water Resources Research,2014,50(12):9586- 9603.
[11] SCHEUERER M.Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics[J].Quarterly Journal of the Royal Meteorological Society,2014,140(680):1086-1096.
[12] 冯平,李新.基于Copula函数的非一致性洪水峰量联合分析[J].水利学报,2013,44(10):1137-1147.(FENG P,LI X.Bivariate frequency analysis of non-stationary flood time series based on Copula methods [J].Journal of Hydraulic Engineering,2013,44(10):1137-1147.(in Chinese))
[13] GNEITING T,BALABDAOUI F,RAFTERY A E.Probabilistic forecasts,calibration and sharpness[J].Journal of the Royal Statistical Society,2010,69(2):243-268.
[14] LI L,XU C Y,XIA J,et al.Uncertainty estimates by Bayesian method with likelihood of AR(1) plus Normal model and AR(1) plus Multi-Normal model in different time-scales hydrological models [J].Journal of Hydrology,2011,406(1/2):54- 65.
[15] 邓鹏鑫,王银堂,胡庆芳,等.GR4J模型在赣江流域日径流模拟中的应用[J].水文,2014,34(2):60- 65.(DENG P X,WANG Y T,HU Q F,et al.Application of GR4J in daily runoff simulation for Ganjiang river Basin [J].Journal of China Hydrology,2014,34(2):60- 65.(in Chinese))
[16] ZHAO T,WANG Q J,BENNETT J C,et al.Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model[J].Journal of Hydrology,2015,528:329-340.
[17] VOGEL P,KNIPPERTZ P,FINK A H,et al.Skill of global raw and postprocessed ensemble predictions of rainfall over Northern Tropical Africa [J].Weather and Forecasting,2018,33(2):369-388.
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