ISSN:
1436-3259
Keywords:
Rainfall
;
Precipitation
;
Forecasting
;
Real-time
;
Modeling
;
Simulation
;
Stochastic
;
Prediction
;
Sampling
;
Measurement
;
Updating
;
Filtering
;
Estimation
;
Short-term
Source:
Springer Online Journal Archives 1860-2000
Topics:
Architecture, Civil Engineering, Surveying
,
Energy, Environment Protection, Nuclear Power Engineering
,
Geography
,
Geosciences
Notes:
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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01581673
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