Estimating observation impact without adjoint model in an ensemble Kalman filter

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 observation impact estimated from the ensemble sensitivity method is similar to that from the adjoint method. Like the adjoint method, the ensemble sensitivity method is able to detect observations that have large random errors or biases. This sensitivity 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 impact on the forecasts. Copyright (c) 2008 Royal Meteorological Society.