On the verification of ensemble precipitation forecasts over the Godavari River basin

•The skill of ensemble precipitation forecasts from two forecasting systems is verified.•The raw forecasts are generally skillful, but show some conditional biases.•Post-processing of forecasts improved the quality of raw forecasts.•Forecast skill declines with increasing lead times. The ensemble forecasts from Numerical Weather Prediction (NWP) models are considered as essential elements for operational hydrologic forecasting. However, these forecasts are burdened with significant inherent biases and cannot be used directly for hydrologic forecasting. Therefore, the present study examines the capability of ensemble precipitation forecasts obtained from two NWP models i.e., a 20-member ensemble from the National Center for Environmental Prediction (NCEP) and a 50-member ensemble from the European center for Medium-term Weather Forecasts (ECMWF). The skill of 70-member multi-model grand ensemble (MME) generated from the NCEP and ECMWF forecasts is also verified in this study. Furthermore, post-processing of the raw ensemble members was carried out using two methods (Quantile Mapping (QM) and Quantile Regression Forests (QRF)) in order to minimize the inherent biases. The analysis was performed over subbasins of an Indian river basin namely the Godavari River Basin. Here, a set of deterministic and probabilistic verification measures (forecast error box plots, correlation coefficient, Relative Mean Error, mean Continuous Ranked Probability Score, spread-skill relationship, rank histograms, reliability diagram, Area under Relative Operating Characteristic Curve) were employed to assess various quality attributes of the forecasts. The findings from the study suggests that QRF post-processed NCEP and MME forecasts are comparatively better than other forecasts in terms of all employed verification measures over all subbasins. The QRF post-processed forecasts were found to be superior than QM post-processed and raw forecasts over all subbasins. The adopted post-processing methods resulted in improved forecasts at shorter lead times. However, the skill of both raw and post-processed forecasts declines at higher lead times over all subbasins.