A MONTE-CARLO STUDY OF RAINFALL FORECASTING WITH A STOCHASTIC-MODEL

A procedure for short-term rainfall forecasting in real-time is develo ped and a study of the role of sampling on forecast ability is conduct ed. Ground level rainfall fields are forecasted using a stochastic spa ce-time rainfall model in state-space form. Updating of the rainfall f ield in real-time is accomplished using a distributed parameter Kalman filter to optimally combine measurement information and forecast mode l estimates. The influence of sampling density on forecast accuracy is evaluated using a series of a simulated rainfall events generated wit h the same stochastic rainfall model. Sampling was conducted at five d ifferent network spatial densities. The results quantify the influence of sampling network density on real-time rainfall field forecasting. Statistical analyses of the rainfall field residuals illustrate improv ement in one hour lead time forecasts at higher measurement densities.