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Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter
Author(s) -
Yang ShuChih,
Kalnay Eugenia,
Hunt Brian,
E. Bowler Neill
Publication year - 2008
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.353
Subject(s) - grid , data assimilation , interpolation (computer graphics) , ensemble kalman filter , kalman filter , computation , computer science , algorithm , multivariate interpolation , mathematics , statistics , meteorology , bilinear interpolation , extended kalman filter , geography , artificial intelligence , geometry , motion (physics)
We have investigated 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 increments, there is little degradation in the quality of the weight‐interpolated analyses compared to the analyses derived with the high‐resolution grid. The weight‐interpolated analyses are more accurate than the ones derived by interpolating the analysis increments. Additional benefit from the weight‐interpolation method includes improving the analysis accuracy in the data‐void regions, where the standard LEKTF with the high‐resolution grid gives no analysis corrections due to a lack of available observations. Copyright © Royal Meteorological Society and Crown Copyright, 2008