Handling time auto-correlated model error in the Ensemble Kalman Smoother
The explicit consideration of model error in data assimilation is increasing. While this improves the realism of the situation (i.e. models have deficiencies), it also increases the complexity of the problem. Two common situations are often explored: independent model errors every time step (easy to study in theory) and fixed model errors (easy to implement in practice). We present the solution for an (ensemble) Kalman smoother in the presence of auto-correlated model error with a general (non-zero and non-infinite) memory. Moreover, we study the consequences of using a wrongly guessed memory in the data assimilation which is different from the true memory of the system.
Dr. Javier Amezcua University of Reading