A STOCHASTIC APPROACH TO SHORT-TERM RAINFALL PREDICTION USING A PHYSICALLY BASED CONCEPTUAL RAINFALL MODEL

Abstract

An improved method for short-term rainfall prediction is presented. A previously proposed deterministic rainfall prediction method for real-time hydrologic applications is extended to a stochastic method. This method mainly consists of a physically based conceptual rainfall model that includes water balance and thermodynamics. The important element in this method is the translation of radar data to the model parameter of the conceptual model, which is incorporated into the numerical scheme of the mesoscale model. The extended Kalman filter is used as a state estimator to update the model parameter of the conceptual model with new radar data and with forecasts from a numerical weather prediction model. The performance of the stochastic method is examined for a radar observation area that includes a mountainous region with a rainfall event that occurred along a front. The stochastic method performed better than the deterministic method.