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

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.