Extended Abstract (428K)6.3A
Estimation of observation impact without adjoint model in an ensemble Kalman filter
Junjie Liu, University of California, Berkeley, Berkeley, CA; and E. Kalnay
We propose an ensemble sensitivity method to calculate observation impacts similar to Langland and Baker (2004) but without the need for an adjoint model, which is not always available for numerical weather prediction models. The formulation is tested on the Lorenz-40 variable model, and the results show that the ensemble sensitivity method gives an observation impact similar to that of the adjoint method. Like the adjoint method, the ensemble sensitivity method is able to detect observations that have large random errors or biases. This method could be routinely calculated in an ensemble Kalman filter, thus providing a powerful tool to monitor the quality of observations and give quantitative estimations of observation impacts on the forecasts.
Session 6, ADVANCED METHODS FOR DATA ASSIMILATION-II
Tuesday, 22 January 2008, 11:00 AM-12:00 PM, 204
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