Date and Time: Wednesday, 27 February, 11.00-12.00 Location: Slingo Lecture Theatre, JJ Thompson Building, Whiteknights Campus Speaker: Javier Amezcua (University of Reading) Title: Time structures in model error within data assimilation Abstract:
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.
We also provide some insight into the situations when model error with different time-scales may arise. We provide a simple analysis for a linear two-scale problem with a fast and a slow component. We show how the interactions can lead to three elements in the slow scale: direct, memory and noise (simple and complex). We discuss how these elements are addressed in sequential DA and provide some ideas of how to improve this treatment.