Nonglobal parameter estimation using local ensemble Kalman filtering

Abstract

The authors study parameter estimation for nonglobal parameters in a low-dimensional chaotic model using the local ensemble transform Kalman filter (LETKF). By modifying existing techniques for using observational data to estimate global parameters, they present a methodology whereby spatially varying parameters can be estimated using observations only within a localized region of space. Taking a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics as a numerical test bed, the authors show that this parameter estimation methodology accurately estimates parameters that vary in both space and time, as well as parameters representing physics absent from the model.