Post-processing Numerical Precipitation Forecasting Models Output of TIGGE Database using Bayesian Model Averaging (BMA)

Document Type : Original Article

Authors

1 Ph.D Student, Department Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Assistant Professor, Department of Water Resources Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Precipitation is one of the most important meteorological phenomena and the main parameter for streamflow forecasting. Therefore, determining the amount of precipitation in the future will help to manage water resources and predict the flood. In this regard, some of the most important meteorological centers in the world provided users with Quantitative Precipitation Forecasts (QPFs) on a global scale. The availability of global ensemble forecasting models in the TIGGE database creates new opportunities for flood forecasting. In this research, the effect of post-processing on the most important global numerical ensemble forecasting models such as UKMO, ECMWF, NCEP and CMA in the TIGGE database during the years 2007 to 2014 for the Bashar river Basin investigated. Evaluations were conducted in probabilistic and nonprobabilistic approach. Initially, the four NWP models with quantile mapping methods were bias corrected. Then, by using Bayesian model averaging (BMA), the post-processing was carried out. The results of probabilistic evaluation after post-processing showed that the skill of forecasting models for the Bashar basin increased and uniform distributions were achieved in verification rank histograms. Also, the results of the probabilistic evaluation with the BSS for the combined mode of four QPF Models with BMA method at most stations were close to 0.5 and in the simple combination was close to zero, indicating that Grand ensemble has a higher skill than single models.

Keywords

Main Subjects

References

Aminyavari S, Saghafian B and Delavar M (2018) Evaluation of TIGGE ensemble forecasts of precipitation in distinct climate regions in Iran. Advances in Atmospheric Sciences 35(4):457–468
Fraley C, Raftery AE and Gneiting T (2010) Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian model averaging. Monthly Weather Review 138(1):190–202
Fraley C, Raftery AE, Gneiting T and Sloughter JM (2013) Ensemble BMA: An R package for probabilistic forecasting using ensembles and Bayesian model averaging. University of Washington, Technical Report No. 516
Jvanmard ghassab M, Delavar M, Morid S (2018) Evaluation of medium-term forecast of TIGGE numerical weather prediction models in Karun basin. Iran Water Resources Research (In Persian)
Liu J and Xie Z (2014) BMA probabilistic quantitative precipitation forecasting over the Huaihe basin using TIGGE multimodel ensemble forecasts. Monthly Weather Review 142(4):1542–1555
Liu Y, Duan Q, Zhao L, Ye A, Tao Y, Miao C, Mu X and Schaake JC (2013) Evaluating the predictive skill of post-processed NCEP GFS ensemble precipitation forecasts in China’s Huai river basin. Hydrological Processes 27(1):57–74
Magnusson L, Leutbecher M and Källén E (2008) Comparison between singular vectors and breeding vectors as initial perturbations for the ECMWF ensemble prediction system. Monthly Weather Review 136(11):4092–4104
Qu B, Zhang X, Pappenberger F, Zhang T and Fang Y (2017) Multi-model grand ensemble hydrologic forecasting in the Fu river basin using Bayesian model averaging. Water (Switzerland) 9(2)
Raftery AE, Gneiting T, Balabdaoui F and Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review 133(5):1155–1174
Schefzik R, Thorarinsdottir TL and Gneiting T (2013) Uncertainty quantification in complex simulation models using ensemble copula coupling. Statistical Science 28(4):616–640
Schmeits MJ and Kok KJ (2010) A comparison between raw ensemble output, (modified) Bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts. Monthly Weather Review 138(11):4199–4211
Sene K (2010) Hydrometeorology: Forecasting and Applications. Springer, DOI 10.1007/978-90-481-3403-8
Sloughter JM (2009) Probabilistic weather forecasting using Bayesian model averaging. University of Washington
Sloughter JML, Raftery AE, Gneiting T and Fraley C (2007) Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Monthly Weather Review 135(9):3209–3220
Swinbank R, Kyouda M, Buchanan P, Froude L, Hamill TM, Hewson TD, Keller JH, Matsueda M, Methven J, Pappenberger F, … Yamaguchi M (2016) The TIGGE project and its achievements. Bulletin of the American Meteorological Society 97(1):49–67
Tao Y, Duan Q, Ye A, Gong W, Di Z, Xiao M and Hsu K (2014) An evaluation of post-processed TIGGE multimodel ensemble precipitation forecast in the Huai river basin. Journal of Hydrology 519(PD):2890–2905
Vogel P, Knippertz P, Fink AH, Schlueter A and Gneiting T (2018) Skill of global raw and postprocessed ensemble predictions of rainfall over northern tropical Africa. Weather and Forecasting 33(2):369–388
Vrugt JA, Clark MP, Diks CGH, Duan Q and Robinson BA (2006) Multi-objective calibration of forecast ensembles using Bayesian model averaging. Geophysical Research Letters 33(19):2–7
Wilks DS (2011) Statistical methods in the atmospheric sciences. Academic press
Zapata J (2010) Evaluation of hydrological ensemble prediction systems for operational forecasting. Université Laval
Zomerdijk L (2015) Performance of multi-model ensemble combinations for flood forecasting. university of twente
 
 
 
 
 
 
 
 
Iran-Water Resources Research
Volume 14, Issue 4
June 2018
Pages 239-252

Files

History

  • Receive Date: 08 April 2018
  • Revise Date: 06 June 2018
  • Accept Date: 16 June 2018

Share

How to cite

Statistics