Applying a precipitation error model to numerical weather predictions for probabilistic flood forecasts

Abstract This work investigates the use of a stochastic error model (the 2-Dimensional Satellite Rainfall Error Model-SREM2D) to generate an ensemble of rainfall fields, based on the forecasts from the Eta regional weather forecast model. To evaluate the usefulness of this approach against traditional techniques, streamflow probabilistic forecasts from a distributed hydrological model forced with two sources of rainfall data are compared in the Tocantins-Araguaia basin in Brazil. The first dataset is an empirical rainfall ensemble produced by the SREM2D model applied to the Eta model, and the second is a state-of-the-art rainfall ensemble produced by the ECMWF model. Results show the potential of the stochastic error model to generate precipitation ensemble fields from a regional numerical weather forecasting model removing around 60% and 12% of the systematic and random error, respectively. Moreover, SREM2D is proven to be an efficient technique that involves a low computational cost when compared to the more sophisticated ensemble techniques used by the ECMWF model.