Journal article
A Monte Carlo study of rainfall forecasting with a stochastic model
Stochastic Hydrology and Hydraulics, Vol.6(1), pp.27-45
03/1992
DOI: 10.1007/BF01581673
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.
Details
- Title: Subtitle
- A Monte Carlo study of rainfall forecasting with a stochastic model
- Creators
- M. N FrenchR. L BrasW. F Krajewski
- Resource Type
- Journal article
- Publication Details
- Stochastic Hydrology and Hydraulics, Vol.6(1), pp.27-45
- DOI
- 10.1007/BF01581673
- ISSN
- 0931-1955
- eISSN
- 1436-3259
- Language
- English
- Date published
- 03/1992
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9983991985802771
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