84th AMS Annual Meeting

Monday, 12 January 2004: 5:00 PM
Ensemble-based data assimilation at a coastline
Room 3AB
Altug Aksoy, Texas A&M University, College Station, TX; and F. Zhang, J. W. Nielsen-Gammon, C. Epifanio, and C. Snyder
Poster PDF (935.4 kB)
The ensemble Kalman filter (EnKF) is a relatively new technique proposed for the data assimilation schemes of atmospheric and oceanic numerical models. The advantage of this approach over conventional data assimilation systems is the fact that it produces a flow-dependent background error covariance matrix in addition to the optimal estimate of the current state. The EnKF has also the additional advantage of being much less numerically costly than the traditional Kalman filter as it uses the members of an ensemble to estimate the covariance matrix.

This study focuses on the application of the EnKF to thermally-driven boundary-layer circulations. The specific case chosen is the land/sea breeze which is the dominant summer-time atmosperic circulation for the Houston/Galveston, Texas area. This phenomenon governs the transport and recirculation of pollutants which have become a major environmental and health concern in recent years in the area of interest. From a data assimilation point of view, modeling of such thermally-driven circulations imposes challenges that have not been previously addressed in the literature. One such very important challenge is the effective use of parameterization schemes, in particular the ones related to boundary layer processes, which are believed to be a major source of error in a land/sea modeling environment. The potential advantages of using the EnKF in such an environment will be demonstrated through the background error covariance structure of a typical sea breeze circulation and preliminary data assimilation results will be presented with the help of a perfect-model analysis and simulated observations.

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