Optimizing localization for ensemble data assimilation using partial analysis increments

Partial analysis increments (PAI) are an efficient diagnostic to determine the influence individual observations or groups of observations on the analysis (Diefenbach et al.). Addittionally, the diagnostic can be used to approximate the influence that observations would have had for different assimilation settings. We use PAI to investigate whether observations could be more beneficial if they were assimilated using different localization settings. Localization is an essential component of any ensemble-based data-assimilation system, necessary to mitigate the effects of a limited ensemble size and to reduce the computational cost. Localization for satellite observations, which lack a constant or well-defined observation location, is particularly challenging, and numerous approaches have been proposed. Using PAI, we can estimate the performance of many different localization approaches without needing to rerun experiments and determine settings that lead to an optimal analsis using independent observations for verification. In this poster, we present results from a one-month-long cycled forecast of the regional modelling system of Deutscher Wetterdienst, in which the PAIs are compared against non-assimilated radiosondes. PAI is used to optimise and understand localisation by (1) considering a range of localisation functions over a normalised metric; (2) studying different combinations of parameters for a Gaspari-Cohn localization function and (3) optimising localization over a set of idealised functions. The current settings of DWD for vertical localisation of satellite radiances in the visible 0.6µm and infrared 6.2µm 7.3µm channels - which were originally designed to improve estimates of cloud-related variables - perform well against our metrics but could be improved upon by using a multi-peaked localisation function.