The role of observation and background errors for reconstructing localized features from non-local observations

The findings of this work generally demonstrate that large observation errors do not only decrease the overall weight which the respective observations obtain in the DA process, they especially reduce the DA systems capability to obtain spatially localized information. Small observation errors are particularly important when processing strongly non-local observations as they are typically obtained from passive remote sensing measurements. These have the potential to smear out signals from localized sources over large regions in model space. Generally, observation errors have to be smaller the more the respective observation operators overlap.