Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting
DE Robertson, DL Shrestha, QJ Wang
HYDROLOGY AND EARTH SYSTEM SCIENCES | COPERNICUS GESELLSCHAFT MBH | Published : 2013
Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts wi..View full abstract
This research has been supported by the Water Information Research and Development Alliance between the Australian Bureau of Meteorology and CSIRO Water for a Healthy Country Flagship and the CSIRO OCE Science Leadership Scheme. We would like to thank David Enever and Chris Leahy from the Australian Bureau of Meteorology for providing the data for this study. We would like to acknowledge the thorough reviews by Andrew Schepen from the Australian Bureau of Meteorology, Jan Verkade and two anonymous referees.