Weight interpolation for efficient data assimilation with the Local Ensemble Transfom Kalman Filter

Shu Chih Yang, Eugenia Kalnay, Brian Hunt, Neill E. Bowler

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)251-262
Number of pages12
JournalQuarterly Journal of the Royal Meteorological Society
Volume135
Issue number638
DOIs
StatePublished - 2009

Keywords

  • 3D-Var
  • 4D-Var
  • LETKF

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