  We study parameter estimation for non-global 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, we 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 our numerical testbed, we show that this parameter estimation methodology
accurately estimates parameters which vary in both space and time, as well as
parameters representing physics absent from the model.
