HR: 11:50h
AN: H22A-07 [Abstracts]
TI: A Bayesian Methodology for Ensemble Forecasting
AU: * Herr, H D
EM: hank.herr@noaa.gov
AF: Office of Hydrologic Development, Building SSMC 2
1325 East-West Highway, Silver Spring, MD 20910
United States
AU: Krzysztofowicz, R
EM: rk@virginia.edu
AF: University of Virginia, Dept. of Systems and Information Engineering
151 Engineer's Way, Charlottesville, VA 22904
United States
AB:
A Bayesian methodology for probabilistic forecasting of a future river stage time series in terms of an ensemble is
presented. The methodology derives from the theory of the Bayesian Forecasting System (BFS). Within the BFS, uncertainty
due to future precipitation is quantified in a precipitation uncertainty processor independently of other uncertainties. The
other uncertainties are aggregated and quantified in a hydrologic uncertainty processor. Then the precipitation uncertainty
and the hydrologic uncertainty are integrated together in an integrator. The resultant probabilistic forecast quantifies
the total uncertainty. This paper presents two algorithms for generating an ensemble forecast using the BFS. The first
algorithm uses output distributions from the analytic-numerical BFS to recursively generate a river stage time series; this
algorithm is suited to headwater basins. The second algorithm implements the precipitation uncertainty processor, hydrologic
uncertainty processor, and integrator as Monte Carlo generators, which sequentially process a precipitation time series into
a river stage time series; this algorithm is suited to complex river basins. The algorithms are illustrated with numerical
examples of Bayesian ensemble forecasts for several different forecasting scenarios. Properties and advantages of the
Bayesian forecasts for decision making are highlighted. Sample sizes required for correct representation of uncertainty are
examined.
DE: 1821 Floods
DE: 1860 Runoff and streamflow
DE: 1869 Stochastic processes
DE: 1894 Instruments and techniques
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting