DATA ASSIMILATION USING AN ENSEMBLE KALMAN FILTER TECHNIQUE

The possibility of performing data assimilation using the flow-depende nt statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman Altering) is examined in an i dealized environment. Using a three-level. quasigeostrophic, T21 model and simulated observations, experiments are performed in a perfect-mo del context. By using forward interpolation operators from the model s tate to the observations, the ensemble Kalman Alter is able to utilize nonconventional observations. In order to maintain a representative s pread between the ensemble members and avoid a problem of inbreeding, a pair of ensemble Kalman filters is configured so that the assimilati on of data using one ensemble of short-range forecasts as background f ields employs the weights calculated from the other ensemble of short- range Forecasts. This configuration is found to work well: the spread between the ensemble members resembles the difference between the ense mble mean and the true state, except in the case of the smallest ensem bles, A series of 30-day data assimilation cycles is performed using e nsembles of different sizes, The results indicate that (i) as the size of the ensembles increases, correlations are estimated more accuratel y and the root-mean-square analysis error decreases, as expected-and ( ii) ensembles having on the order of 100 members are sufficient to acc urately describe local anisotropic, baroclinic correlation structures. Due to the difficulty of accurately estimating the small correlations associated with remote observations, a cutoff radius beyond which obs ervations are not used. is implemented. It is found that (a) for a giv en ensemble size there is an optimal value of this cutoff radius. and (b) the optimal cutoff radius increases as the ensemble size increases .