Characterising flow dependent background errors in data assimilation

Sarah Dance

Department of Meteorology, University of Reading

Background error statistics are a key component of operational data assimilation systems. Unfortunately, they are also a component that is not well known. Thus, operational centres tend to use simple, tuned climatological estimates. It is widely believed that employing more appropriate, flow dependent error covariance matrices will lead to improved analyses. In this context, we take a wide definition for flow dependence, to include not only time and state dependence, but also climatological variations in flow character that are dependent on physical space e.g. the balances at the tropics are different to those in the midlatitudes. In this talk we will describe the background error statistics used at the UK Met Office. We will consider research into improved characterisation of the boundary layer, and look at alternative ways of incorporating state dependent information into the error covariances.