Evaluation of parametric postprocessing of ensemble precipitation forecasts of the NCMRWF for the Vishwamitri River Basin

Abstract

Abstract Postprocessing of the ensemble precipitation data improves the bias and uncertainty induced in the numerical weather prediction (NWP) due to perturbations of the initial condition of atmospheric models. The evaluation of the NWP of short-range quantitative weather forecasts provided by the NCMRWF archived in TIGGE, for the Vishwamitri River Basin. The aim of the study is to perform univariate statistical postprocessing using six parametric methods and compare to find the best suitable approach among censored non-homogeneous logistic regression (cNLR), Bayesian model averaging (BMA), logistic regression (logreg), heteroscedastic logistic regression (hlogreg), heteroscedastic extended logistic regression (HXLR), and ordered logistic regression (OLR) methods for the Vishwamitri River Basin. The Brier score (BS), the area under curve (AUC) of receiver operator characteristics (ROC) plots, Brier decomposition, and reliability plots were used for the verification of the probabilistic forecasts. In agreement with the BS and AUC, the cNLR approach for postprocessing performed very well for calibration at all five grids and is preferred, whereas BMA and hlogreg approaches showed relatively poor performance for the Vishwamitri basin. The best post-processed ensemble precipitation will further be employed as an input for the generation of the hydrological forecasts in operational flood forecasting in the Vishwamitri River Basin.