HR: 0800h
AN: H31H-0765    [Abstracts]
TI: Comparison of two perturbation methods to estimate the land surface modeling uncertainty
AU: * Su, H
EM: hongbo@iges.org
AF: Center for Research on Environment, Institute of Global Environment and Society, Calverton, MD 20705,
AU: Houser, P
EM: houser@iges.org
AF: Center for Research on Environment, Institute of Global Environment and Society, Calverton, MD 20705,
AU: Tian, Y
EM: yudong@hsb.gsfc.nasa.gov
AF: University of Maryland Baltimore County, (UMBC), Baltimore, MD 21250,
AU: Kumar, S
EM: sujay@hsb.gsfc.nasa.gov
AF: Center for Research on Environment, Institute of Global Environment and Society, Calverton, MD 20705,
AU: Geiger, J
EM: james.v.geiger@nasa.gov
AF: NASA Goddard Space Flight Center, (GSFC), Greenbelt, MD 20771,
AU: Belvedere, D
EM: debbie@iges.org
AF: Center for Research on Environment, Institute of Global Environment and Society, Calverton, MD 20705,
AB: In land surface modeling, it is almost impossible to simulate the land surface processes without any error because the earth system is highly complex and the physics of the land processes has not yet been understood sufficiently. In most cases, people want to know not only the model output but also the uncertainty in the modeling, to estimate how reliable the modeling is. Ensemble perturbation is an effective way to estimate the uncertainty in land surface modeling, since land surface models are highly nonlinear which makes the analytical approach not applicable in this estimation. The ideal perturbation noise is zero mean Gaussian distribution, however, this requirement can't be satisfied if the perturbed variables in land surface model have physical boundaries because part of the perturbation noises has to be removed to feed the land surface models properly. Two different perturbation methods are employed in our study to investigate their impact on quantifying land surface modeling uncertainty base on the Land Information System (LIS) framework developed by NASA/GSFC land team. One perturbation method is the built-in algorithm named "STATIC" in LIS version 5; the other is a new perturbation algorithm which was recently developed to minimize the overall bias in the perturbation by incorporating additional information from the whole time series for the perturbed variable. The statistical properties of the perturbation noise generated by the two different algorithms are investigated thoroughly by using a large ensemble size on a NASA supercomputer and then the corresponding uncertainty estimates based on the two perturbation methods are compared. Their further impacts on data assimilation are also discussed. Finally, an optimal perturbation method is suggested.
UR: http://crew.iges.org/research/LISW
DE: 1719 Hydrology
DE: 3275 Uncertainty quantification (1873)
DE: 3315 Data assimilation
SC: Hydrology [H]
MN: 2007 Fall Meeting