A Monte Carlo study of rainfall forecasting with a stochastic model |
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| Authors: | M N French R L Bras W F Krajewski |
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| Institution: | (1) Iowa Institute of Hydraulic Research, University of Iowa, 52242 Iowa City, IA;(2) Massachusetts Institute of Technology, 02139 Cambridge, MA |
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| Abstract: | A procedure for short-term rainfall forecasting in real-time is developed and a study of the role of sampling on forecast ability is conducted. Ground level rainfall fields are forecasted using a stochastic space-time rainfall model in state-space form. Updating of the rainfall field in real-time is accomplished using a distributed parameter Kalman filter to optimally combine measurement information and forecast model estimates. The influence of sampling density on forecast accuracy is evaluated using a series of a simulated rainfall events generated with the same stochastic rainfall model. Sampling was conducted at five different 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 improvement in one hour lead time forecasts at higher measurement densities. |
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| Keywords: | Rainfall Precipitation Forecasting Real-time Modeling Simulation Stochastic Prediction Sampling Measurement Updating Filtering Estimation Short-term |
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