Sampling Issues for Ensemble Filters
Thursday, May 2, 2002 - 11:30am - 11:50am
Jeffrey Anderson (NCAR/MMM)
Methods for using ensemble integrations of prediction models as integral parts of data assimilation have been developed for both atmospheric and oceanic applications. In general, these methods can be derived from the Kalman filter and are known as ensemble Kalman filters. A slightly more general class of ensemble filters is described briefly. These ensemble filter methods make a (local) least squares assumption about the relation between the prior distributions of an observation variable and model state variables. The update procedure applied when a new observation becomes available can be split into two parts: a scalar update increment computation for the prior ensemble estimate of the observation variable; a linear regression of the prior ensemble sample of each state variable on the observation variable. These methods have been applied successfully in atmospheric GCMs but a number of issues related to sampling errors remain. An overview of the implications of sampling error and possible solutions will be presented.