改善水文气象预报的统计后处理
Statistical Post-Processing to Improve Hydrometeorological Forecasts

作者: 段青云 * , 叶爱中 :北京师范大学全球变化与地球系统科学研究院;

关键词: 水文集合预报统计后处理方法集合前处理器广义线性模型Ensemble Hydrological Forecast Statistical Post-Processing Methods Ensemble Pre-Processor Generalized Linear Regression Model (GLM)

摘要: 水文预报存在由模型输入、初始与边界条件、模型参数与结构等因素带来的不确定性,这些不确定性的存在给基于模型输出结果而得出的预报带来预报均值和区间的偏差,在发布最终水文预报前我们必须要消掉这些偏差,统计水文气象后处理器是对水文气象模型直接输出结果进行后处理、从而达到消掉预报偏差的一个有效方法。本文简单介绍两个实用的统计后处理方法:1) 对降水或气温集合预报进行后处理的集合预报处理器(Ensemble Pre-Processor)2) 适合于径流预报后处理的广义线性模型方法(Generalized Linear Model),并将这些方法在我国淮河流域和美国French Broad河流域进行展示。研究结果表明,统计后处理方法能够很大程度提高原始降水与水文预报的精度,一个很有意义的结果是统计后处理改善原始水文预报的程度与水文模型参数率定对原始水文预报的改善程度相当,这意味着在受人类活动影响很大而不能率定水文模型的情况下,水文统计后处理方法能够起到模型率定相同的效果。

Abstract: Hydrologic forecasts based on direct outputs from a hydrologic model contain significant uncertainty 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 hydrologic 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 significantly improve the raw hydrometeorological forecasts. An interesting observation is that post-processing can achi- eve 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.

文章引用: 段青云 , 叶爱中 (2012) 改善水文气象预报的统计后处理。 水资源研究, 1, 161-168. doi: 10.12677/JWRR.2012.14023

参考文献

[1] GLAHN, B., PEROUTKA, M., WIEDENFELD, J., WAGNER, J., ZYLSTRA, G. and SCHUKNECHT, B. MOS uncertainty estimates in an ensemble framework. Monthly Weather Review, 2008, 137: 246-268.

[2] KLEIN, W. H., LEWIS, N. and ENGER, I. Objective prediction of five-day mean temperatures during winter. Journal of Atmospheric Sciences, 1959, 16(6): 672-682.

[3] GLAHN, H. R., LOWRY, D. A. The use of model output statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology, 1972, 11(8): 1203-1211.

[4] RAFTERY, A. E., GNEITING, T., BALABDAOUI, F. and POLAKOWSKI, M. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 2005, 133: 1155- 1174.

[5] HALL, T., BROOKS, H. E. and POSWELL, C. A. Precipitation forecasting using a neural network. Monthly Weather Review, 1999, 14: 338-345.

[6] KULIGOWSKI, R. J., BARROS, A. P. Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and Forecasting, 1998, 13: 1194- 1204.

[7] BRIGGS, W. M., LEVINE, R. A. Wavelets and field forecast verification. Monthly Weather Review, 1997, 125: 1329-1341.

[8] HAMILL, T. M., COLUCCI, S. J. Verification of eta-RSM shortrange ensemble forecasts. Monthly Weather Review, 1997, 125, 1312-1327.

[9] KRISHNAMURTI, T. N., KISHTAWAL, C. M., ZHANG, Z., LAROW, T., BACHIOCHI, D., WILLIFORD, E., GADGIL, S. and SURENDRAN, S. Multimodel ensemble forecasts for weather and seasonal climate. Journal of Climate, 2000, 13: 4196-4216.

[10] SLOUGHTER, J. M., RAFTERY, A. E. and GNEITING, T. Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Monthly Weather Review, 2007, 135: 3209-3220.

[11] FRALEY, C., RAFTERY, A. E. and GNEITING, T. Calibrating multi-model forecast ensembles with exchangeable and missing members using Bayesian model averaging. Monthly Weather Review, 2010, 138: 190-202.

[12] CLARK, M., GANGOPADHYAY, S., HAY, L., RAJAGO- PALAN, B. and WILBY, R. The Schaake Shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 2004, 5(1): 243-262.

[13] KRZYSZTOFOWICZ, R., SIGREST, A. A. Calibration of pro- babilistic quantitative precipitation forecasts. Weather and Fore- casting, 1999, 14(3): 427-442.

[14] KRZYSZTZYFOWICZ, R., KELLY, K. S. Hydrologic uncertainty processor for probabilistic river stage forecasting. Water Resources Research, 2000, 36: 3265-3277.

[15] SEO, D.-J., HERR, H. D. and SCHAAKE, J. C. A statistical post-processor for accounting of hydrologic uncertainty in short- range ensemble streamflow prediction. Hydrology and Earth System Sciences Discussion, 2006, 3: 1987-2035.

[16] SCHAAKE, J. C., HAMILL, T. M., BUIZZA, R. and CLARK, M. HEPEX: The hydrological ensemble prediction experiment. Bulletin of the American Meteorological Society, 2007, 88(10): 1541.

[17] BOGNER, K., KALAS, M. Error-correction methods and evaluation of an ensemble based hydrological forecasting system for the Upper Danube catchment. Atmospheric Science Letters, 2008, 9: 95-102.

[18] REGGIANI, P., RENNER, M., WEERTS, A. H. and VAN GELDER, P. Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system. Water Resources Research, 2009, 45: W02428.

[19] RENNER, M., WERNER, M., RADEMACHER, S. and SPROK- KEREEF, E. Verification of ensemble flow forecasts for the River Rhine. Journal of Hydrology, 2009, 376(3-4): 463-475.

[20] CLOKE, H. L., PAPPENBERGER, F. Ensemble flood forecasting: A review. Journal of Hydrology, 2009, 375(3-4): 613-626.

[21] BROWN, J. D., SEO, D. A nonparametric post-processor for bias correction of hydrometeorological and hydrologic ensemble forecasts. Journal of Hydrometeorology, 2010, 11(3): 642-665.

[22] COCCIA, G., TODINI, E. Recent developments in predictive uncertainty assessment based on the model conditional processor approach. Hydrology and Earth System Sciences Discussion, 2010, 7: 9219-9270.

[23] WEBSTER, P. J., JIAN, J., HOPSON, T. M., HOYOS, C. D., AGUDELO, P. A., CHANG, H. R., CURRY, J. A., GROSSMAN, R. L., PALMER, T. N. and SUBBIAH, A. R. Ex-tended-range probabilistic forecasts of Ganges and Brahmaputra floods in Bangladesh. Bulletin of the American Meteorological Society, 2010, 91(11): 1493-1514.

[24] PAPPENBERGER, F., THIELEN, J. and DEL MEDICO, M. The impact of weather forecast improvements on large scale hydrology: Analysing a decade of forecasts of the European Flood Alert System. Hydrological Processes, 2011, 25(7): 1091-1113.

[25] WEERTS, A. H., WINSEMIUS, H. C. and VERKADE, J. S. Estimation of predictive hydrological uncertainty using quantile regression: Examples from the National Flood Forecasting System (England and Wales). Hydrology and Earth System Sciences, 2011, 15(1): 255-265.

[26] WOOD, A. W., SCHAAKE, J. C. Correcting errors in stream- flow forecast ensemble mean and spread. Journal of Hydrometeorology, 2008, 9(1): 132-148.

[27] WU, L., SEO, D.-J., DEMARGNE, J., BROWN, J. D., CONG, S. and SCHAAKE, J. Generation of ensemble precipitation fore- cast from single-valued quantitative precipitation forecast for hydrologic ensemble prediction. Journal of Hydrology, 2011, 399(3-4): 281-298.

[28] ZHAO, L., DUAN, Q., SCHAAKE, J., YE, A. and XIA, J. A hydrologic postprocessor for ensemble streamflow predictions. Advance in Geosciences, 2011, 29: 51-59.

[29] VALENCIA, R. D., SCHAAKE, J. C. Disaggregation processes in stochastic hydrology. Water Resources Research, 1973, 9(3): 580-585.

[30] DUAN, Q., SCHAAKE, J., ANDREASSIAN, V., FRANKS, S., GUPTA, H. V., GUSEV, Y. M., HABETS, F., HALL, A., HAYS, L., HOGUE, T., HUANG, M., LEAVESLEY, G., LIANG, X., NASONOVA, O. N., NOILHAN, J., OUDIN, L., SOROO- SHIAN, S., WAGENER, T. and WOOD, E. F. Model parameter estimation experiment (MOPEX): Overview and summary of the second and third workshop results. Journal of Hydrology, 2006, 320(1-2): 3-17.

[31] SCHAAKE, J., DEMARGNE, J., HARTMAN, R., MULLUSKY, M., WELLES, E., WU, L., HERR, H., FAN, X. and SEO, D. J. Precipitation and temperature ensemble forecasts from single- value forecasts. Hydrology and Earth System Sciences Discus- sion, 2007, 4: 655-717.

分享
Top