Extended Abstract (84K)J5.4
4D Ensemble Kalman filtering for assimilation of asynchronous observations
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
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.
Joint Session 5, Data Assimilation and observational network design: Part IV (Joint between the Symposium on Forecasting the Weather and Climate of the Atmosphere and Ocean and the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction) (ROOM 605/606)
Tuesday, 13 January 2004, 8:30 AM-9:45 AM, Room 605/606
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