Numerical Weather Prediction (NWP) model background error correlations play a decisive
role in data assimilation. Their spatial structures directly influence the spatial scales on which
observations can impact the NWP analysis state. Closely spaced observations will impact
the same model state variable making their information content redundant to some extent.
Conventional observing systems usually undersample the background error structures, while
satellite observations generally oversample horizontally (but not vertically).
In order to investigate the optimized Aeolus observation size and spacing w.r.t. the NWP
model background, spatial NWP model background errors and error correlations have been
investigated. This has been done in two ways: (i) by extracting model background error
structures as currently used in state of art global and regional models and (ii) by extracting
background error (and observation error) structures from observation minus background (o-b)
and observation minus analysis (o-a) statistics of high resolution datasets of radiosonde,
scatterometer and aircraft data. The motivation for the latter is that recent observation-model
intercomparison studies have revealed that nowadays models tend to overestimate both the
background error variance, the observation error variance and observation error correlation
length scales for various observing systems. The second method is therefore
meant to further validate the currently used model background error structures that have
been obtained from ensemble and NMC methods.
The models used in both analysis include the operational ECMWF global model and mesoscale
GJ Marseille, A Stoffelen, H Schyberg. Horizontal and vertical background error correlations