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Title:   Author  item
                                            Crow, Wadeitem
                                            Yilmaz, Mustafaitem
                                            HAN, E - Science Systems, Inc
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 6/2/2012
                                    Publication  Date: N/A
                                    Citation: N/AInterpretive Summary:
                                Technical Abstract: Data assimilation concerns itself primarily with the impact of random stochastic errors on state estimation. However, the developers of land data assimilation systems are commonly faced with systematic errors arising from both the parameterization of a land surface model and the need to pre-process observations to remove systematic differences commonly found in modeled versus observed land surface states. This poster will provide and illustrate two examples of these challenges. First, the analytical basis underlying the pre-assimilation rescaling of soil moisture observations will be developed and compared to empirical re-scaling approaches commonly applied (in a pre-processing step) during the assimilation of remotely-sensed surface soil moisture retrievals into land surface models. Conditions under which these empirical re-scaling procedures do and do not conform to this analytical basis will be clarified and the impact of such non-conformity on assimilation results quantified. Second, the impact of uncertainty in two key parameters: surface soil moisture storage capacity and the effective water-flux vertical diffusivity will be examined to clarify the impact of errors in these parameters on the ability of surface soil moisture retrievals to constrain deeper soil moisture states using sequential data assimilation. These results will lead to specific conclusions regarding strategies for minimizing the impact of miss-specifying either parameter on land data assimilation results. Combined, results from both cases will be used to define new land data assimilation approaches which are most robust to the potential degrading impact of systematic errors in land surface models and observations.


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