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

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