Improving Operational Ensemble Streamflow Forecasting with Conditional Bias-Penalized Post-Processing of Precipitation Forecast and Assimilation of Streamflow Data

This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). To assess the potential impact of the CBPR-aided MEFP and streamflow DA, or CBPR-DA, 20-yr hindcast experiments were carried out using the Global Ensemble Forecast System version 12 reforecast dataset for 46 locations in the service areas of 11 River Forecast Centers of the US NWS. The results show that, relative to the current practice of using the MEFP and no DA, or MEFP-NoDA, CBPR-DA improves the accuracy of ensemble forecasts of 3-day flow over lead times of 0 to 3 days by over 40% for 4 RFCs and by over 20% for 9 of the 11 RFCs. The margin of improvement is larger where the predictability of precipitation is larger and the hydrologic memory is stronger. As the lead time increases, the margin of improvement decreases but still exceeds 10% for the prediction of 14-day flow over lead times of 0 to 14 days for all but 3 RFCs.