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Title:   Author  item
                                            Crow, Wadeitem
                                            Bolten, John
  Submitted to: American Geophysical Union
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 4/12/2007
                                    Publication  Date: 5/22/2007
                                    Citation: Crow, W.T., Bolten, J.D. 2007. Towards the development on an on-line model error identification system [abstract].  American Geophysical Union Spring Meeting.  EOS Trans. 88(23). 2007 CDROM.Interpretive Summary:
                                Technical Abstract: Due to the complexity of potential error sources in land surface models, the accurate specification of model error parameters has emerged as a major challenge in the development of effective land data assimilation systems. While several on-line procedures for estimating model error parameters - based on the statistical analysis of filtering innovations - have been introduced for geophysical models, such procedures have not been widely applied to land surface models. A frequently cited concern is the computation burden of iteratively adjusting model error parameters until theoretical expectations for innovation statistics (e.g. temporally white and normalized variance of one) are met. Classical, closed-form approaches for the on-line identification of linear model error could greatly reduce this computational burden; however their applicably to nonlinear land surface models is unclear. Using a series of synthetic twin experiments and an Ensemble Kalman filter, this paper will present a framework for diagnosing the magnitude of error in hydrologic model forecasts and/or hydrologic remote sensing retrievals. Results will demonstrate the potential of applying classical adaptive filtering approaches (originally derived for purely linear systems) to land surface models. Particular attention will be paid to potential differences between highly nonlinear and chaotic atmospheric models and nonlinear, but ultimately dissipative, land surface model and the implications of these differences on the development of an on-line system for identification of model error parameters. Preliminary real data results (based on the assimilation of remotely-sensed surface soil moisture retrievals into a land surface model forced by satellite-based precipitation) will be used to underscore the potential value of the approach in an operational setting.


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