HR: 1340h
AN: H23C-1142    [Abstracts]
TI: Generation of Ensembles of Spatial Hydrologic Fields
AU: * Pan, M
EM: mpan@princeton.edu
AF: Dept of Civil & Environ Eng, Pirnceton University, Olden St, Princeton, nj 08544 United States
AU: Wood, E F
EM: efwood@runoff.princeton.edu
AF: Dept of Civil & Environ Eng, Pirnceton University, Olden St, Princeton, nj 08544 United States
AB: In ensemble prediction applications, if only deterministic forecasts are available (e.g. rainfall, temperature), an ensemble of forecasts often needs to be generated from the deterministic forecast to properly quantify the forecast uncertainty. The generation is usually done by Monte Carlo simulation and the distributional parameters needed are obtained by comparing the (deterministic) forecasts to observations in the historical records. John Schaake (NOAA/NWS) has developed procedures for generating at a point, or for an areal average forecast ensembles conditioned on the deterministic forecast. For spatial hydrologic fields like deterministic quantitative precipitation forecasts (QPF), or even temperature, the generation becomes significantly more complicated due the need to preserve the spatial correlation structure within each generated ensemble field. Spatial generation techniques like Turning Bands may fail to account for the statistical conditioning between the forecasts and observations, and therefore may not properly represent the forecast uncertainty across the ensembles. The ensemble generating procedure we describe here is utilizes the conditional generation approach within a high-dimensional joint distribution that represents the forecasted and observed hydrologic variables at all locations. In the implementation of the ensemble generation procedures, a transformation of the sample distribution to the standard multi-normal and back is used to deal with the difficulties that arise from the highly irregular marginal distribution of observed hydrologic variables (e.g. rainfall). This approach can also be viewed as a Bayesian estimation of future observations given the (deterministic) forecast. Results will be presented comparing the spatial correlations within the generated ensembles, and the variability across ensembles, with observations to determine whether the uncertainty in the hydrologic variables is preserved.
DE: 3337 Numerical modeling and data assimilation
DE: 3354 Precipitation (1854)
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting