Pre-emptive forecasts from an ensemble Kalman filter

Ensemble Kalman Filters estimate the error covariance matrix of a model first guess field using ensembles. An ensemble Kalman Filter can be used for data assimilation � using the ensemble based error statistics to interpolate the difference between observations and the first guess field through out the model domain, creating an �increment' to the first guess field. An ensemble Kalman Filter can be used for targeting � propagating error statistics forward in time. Combining these two uses of an ensemble Kalman filter � the creation of an increment and the propagation of error statistics, a �pre-emptive forecast' can be generated. In a pre-emptive forecast, the increment to the first guess field at the analysis time is, using ensembles, propagated to some future time.

In an OSSE, a barotropic vorticity model was run to produce a 300-day �nature-run'. The same model, run at lower resolution and with a different relaxation scheme, served to forecast the nature-run. The forecast model was re-started every 24 hours, assimilating observations of the nature-run using a hybrid Ensemble Kalman Filter / 3D-Var data assimilation scheme. The forecast model produced 24-hour and 48-hour forecasts for each of the 300 days. In addition, 24-hour forecasts were made by using the ensemble Kalman Filter (EnKF) to re-weight members of a 64-member forecast ensemble to produce a �pre-emptive forecast'. The pre-emptive forecasts were more accurate than the 48-hour forecast, though not as accurate as the conventional 24-hour forecast. Issues of covariance localization and model error will be addressed.