Post-processing quantitative precipitation forecasts using the seasonally coherent calibration model

Skilful precipitation ensemble forecasts are necessary to produce trustworthy hydrologic predictions. Raw quantitative precipitation forecasts (QPFs) from the numerical weather prediction (NWP) models are known to be error-prone. In this study, sub-basin averaged deterministic QPFs with five-day lead times from the European Centre for Medium-Range Weather Forecasts (ECMWF) are post-processed through the Seasonally Coherent Calibration (SCC) model for the Narmada and Godavari River basins of India. The SCC model incorporates seasonal climatology from long observations into forecasts and produces calibrated forecasts based on a joint probability model. The SCC model results are compared with the post-processed forecasts from the state-of-the-art Quantile Mapping (QM) method. The results suggest that the probabilistic ensemble forecasts generated from the SCC model have improved skill throughout five-day lead times. Further, the application of SCC-calibrated precipitation forecasts is demonstrated using the Soil & Water Assessment Tool (SWAT) to generate streamflow forecasts.