Recorded presentation9A.4
Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter
Shu-Chih Yang, Univ. of Maryland, College Park, MD; and E. Kalnay, B. Hunt, and N. Bowler
We investigate a method to substantially reduce the analysis computations within the Local Ensemble Transform Kalman Filter (LETKF) framework. Instead of computing the LETKF analysis at every model grid point, we compute the analysis on a coarser grid and interpolate onto a high-resolution grid by interpolating the analysis weights of the ensemble forecast members derived from the LETKF. Because the weights vary on larger scales than the analysis fields or analysis increments, there is little degradation in the quality of the weight-interpolated analyses compared to the analyses derived with the high-resolution grid.
Results show that the weight-interpolated analyses are more accurate than the ones derived by interpolating the analysis increments. The weight-interpolation method also shows the benefit to the analysis accuracy of the data-void region, where the standard LEKTF with a high-resolution grid gives no analysis corrections due to the lack of available observations.
Session 9A, Advanced Methods for Data Assimilation—I
Wednesday, 14 January 2009, 10:30 AM-12:00 PM, Room 130
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