84th AMS Annual Meeting

Tuesday, 13 January 2004: 9:15 AM
4D Ensemble Kalman filtering for assimilation of asynchronous observations
Room 605/606
T. D. Sauer, George Mason University, Fairfax, VA; and B. R. Hunt, J. A. Yorke, A. V. Zimin, E. Ott, E. J. Kostelich, I. Szunyogh, G. Gyarmati, E. Kalnay, and D. J. Patil
Poster PDF (76.6 kB)
Ensemble Kalman filters are easily adapted to the assimilation of observations that are asynchronous with the analysis cycle (denoted 4D). In the ideal case of linear dynamics between consecutive analyses, the algorithm is equivalent to Kalman filtering assimilation at each observation time. Tests of the 4D Ensemble Kalman Filtering method on the Lorentz 40 variable model are conducted. Observations are at time intervals dt and the 4D analyses are at longer time intervals dT. For dT/dt less than a critical value the results obtained doing the analyses (i) at the cycle times dT, and (ii) at every observation time ndt are essentially the same. Past the (fairly large) critical value, there is a catastrophic sudden jump in the error of the 4D method. We also hope to have results applying the 4D Ensemble method to the NCEP GFS model.

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