HR: 08:45h
AN: H41I-04 INVITED    [Abstracts]
TI: Dealing with systematic errors in land surface modeling, soil moisture observations and assimilation
AU: * De Lannoy, G J
EM: Gabrielle.delannoy@UGent.be
AF: Laboratory of Hydrology and Water Management, Bioscience Engineering, Ghent University, Coupure links 653, Ghent, B-9000, Belgium
AU: Houser, P R
EM: Houser@iges.org
AF: George Mason University & Center for Research on Environment and Water, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705-3106, United States
AU: Reichle, R H
EM: Reichle@gmao.gsfc.nasa.gov
AF: Global Modeling and Assimilation Office (Code 610.1), NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States
AU: Reichle, R H
EM: Reichle@gmao.gsfc.nasa.gov
AF: Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, MD 21250, United States
AU: Pauwels, V R
EM: Valentijn.Pauwels@UGent.be
AF: Laboratory of Hydrology and Water Management, Bioscience Engineering, Ghent University, Coupure links 653, Ghent, B-9000, Belgium
AU: Verhoest, N E
EM: Niko.Verhoest@UGent.be
AF: Laboratory of Hydrology and Water Management, Bioscience Engineering, Ghent University, Coupure links 653, Ghent, B-9000, Belgium
AB: A fundamental assumption of the Kalman filter is that both the observations and model predictions are unbiased. Bias in observations typically reflects instrumental inaccuracies, representativeness errors, or, in the case of remote sensing observations, errors in the retrieval algorithm. This bias is typically (at best) removed prior to assimilation. Land surface models are usually biased in at least a subset of the simulated variables even after calibration. On-line forecast bias estimation may therefore be needed for data assimilation. Here, in situ soil moisture observations in a small agricultural field (OPE3) were merged with Community Land Model (CLM2.0) simulations using different algorithms for state and bias estimation with and without bias correction feedback. The different bias correction schemes were tested to study the impact of the state correction on depending model fluxes. The best variant for state and bias estimation depends on the nature of the model bias: an improper bias correction scheme could distort the water balance. The lack of knowledge of the bias `dynamics' in time and space and the approximation of the bias uncertainty structure limit successful bias estimation and correction to directly observed state variables. However, all assimilation schemes including bias correction algorithms yield far improved state analysis results compared to standard state filter analyses.
DE: 1805 Computational hydrology
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting