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Improving the ensemble transform Kalman filter using a second-order Taylor approximation of the nonlinear observation operator

 

G. Wu¹, X. Yi¹, L. Wang², X. Liang³, S. Zhang¹, X. Zhang¹, and X. Zheng¹

 

1State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

2Department of Statistics, University of Manitoba, Winnipeg, Canada

3National Meteorological Information Center, China Meteorological Administration, Beijing, China

 

ABSTRACT

The ensemble transform Kalman filter (ETKF) assimilation scheme has recently seen rapid development and wide application. As a specific implementation of the ensemble Kalman filter (EnKF), the ETKF is computationally more efficient than the conventional EnKF. However, the current implementation of the ETKF still has some limitations when the observation operator is strongly nonlinear. One problem in the minimization of a nonlinear objective function similar to 4D-Var is that the nonlinear operator and its tangent-linear operator have to be calculated iteratively if the Hessian is not preconditioned or if the Hessian has to be calculated several times. This may be computationally expensive. Another problem is that it uses the tangent-linear approximation of the observation operator to estimate the multiplicative inflation factor of the forecast errors, which may not be sufficiently accurate.

 

PUBLISHED BY: NONLINEAR PROCESSES IN GEOPHYSICS, 2014, 21 (5): 955-970

 

SOURCE: http://www.nonlin-processes-geophys.net/21/955/2014/npg-21-955-2014.html