A Local Least Squares Framework for Ensemble Filtering

Abstract

Many methods using ensemble integrations of prediction models as integral parts of data assimilation have appeared in the atmospheric and oceanic literature. In general, these methods have been derived from the Kalman filter and have been known as ensemble Kalman filters. A more general class of methods including these ensemble Kalman filter methods is derived starting from the nonlinear filtering problem. When working in a joint state–observation space, many features of ensemble filtering algorithms are easier to derive and compare. The ensemble filter methods derived here make a (local) least squares assumption about the relation between prior distributions of an observation variable and model state variables. In this context, the update procedure applied when a new observation becomes available can be described in two parts. First, an update increment is computed for each prior ensemble estimate of the observation variable by applying a scalar ensemble filter. Second, a linear regression of the prior ensemble sample of each state variable on the observation variable is performed to compute update increments for each state variable ensemble member from corresponding observation variable increments. The regression can be applied globally or locally using Gaussian kernel methods. Several previously documented ensemble Kalman filter methods, the perturbed observation ensemble Kalman filter and ensemble adjustment Kalman filter, are developed in this context. Some new ensemble filters that extend beyond the Kalman filter context are also discussed. The two-part method can provide a computationally efficient implementation of ensemble filters and allows more straightforward comparison of methods since they differ only in the solution of a scalar filtering problem.