  The feature based spatial verification method SAL is applied to cloud data,
i.e. two-dimensional spatial fields of total cloud cover and spectral radiance.
Model output is obtained from the COSMO-DE forward operator SynSat and compared
to SEVIRI satellite data. The aim of this study is twofold. First, to assess
the applicability of SAL to this kind of data, and second, to analyze the role
of external object identification algorithms (OIA) and the effects of
observational uncertainties on the resulting scores.
  As a feature based method, SAL requires external OIA. A comparison of three
different algorithms shows that the threshold level, which is a fundamental
part of all studied algorithms, induces high sensitivity and unstable behavior
of object dependent SAL scores (i.e. even very small changes in parameter
values can lead to large changes in the resulting scores). An in-depth
statistical analysis reveals significant effects on distributional quantities
commonly used in the interpretation of SAL, e.g. median and interquartile
distance. Two sensitivity indicators based on the univariate cumulative
distribution functions are derived. They allow to asses the sensitivity of the
SAL scores to threshold level changes without computationally expensive
iterative calculations of SAL for various thresholds. The mathematical
structure of these indicators connects the sensitivity of the SAL scores to
parameter changes with the effect of observational uncertainties.
  Finally, the discriminating power of SAL is studied. It is shown, that - for
large-scale cloud data - changes in the parameters may have larger effects on
the object dependent SAL scores (i.e. the S and L2 scores) than a complete loss
of temporal collocation.
