Physical Consistency of Data Assimilated State Evolution; On the Significance of Smoothers and the Importance of Process Noise Modeling
Wednesday, May 1, 2002 - 3:00pm - 3:20pm
Ichiro Fukumori (National Aeronautics and Space Administration (NASA))
Because of model errors, data assimilated state estimates have physically inconsistent temporal evolution. For example, in the atmosphere and ocean, estimates often do not satisfy continuity and their energy budgets cannot be closed. Such inconsistencies render inferring mechanisms and processes of these dynamic systems difficult. Emphasis on state estimation is rooted in part in interests in forecasting. Understanding dynamic systems, however, require establishing descriptions of a physically consistent state evolution. Smoothers can be recognized as inverting estimates into such consistent results. An essential element in such inversion is estimation of process noise (or control) as opposed to errors of the state per se. Process noise is the source of model uncertainty, such as errors associated with the model's external forcings, parameters, and numerics. The distinction between estimating the state and control is illustrated and discussed using examples. The importance of identifying explicit physical models of model process noise is emphasized.