Abstract. Satellite-based aerosol products are routinely validated against
ground-based reference data, usually obtained from sun photometer networks
such as AERONET (AEROsol RObotic NETwork). In a typical validation exercise a
spatial sample of the instantaneous satellite data is compared against a
temporal sample of the point-like ground-based data. The observations do not
correspond to exactly the same column of the atmosphere at the same time, and
the representativeness of the reference data depends on the spatiotemporal
variability of the aerosol properties in the samples. The associated
uncertainty is known as the collocation mismatch uncertainty (CMU). The
validation results depend on the sampling parameters. While small samples
involve less variability, they are more sensitive to the inevitable noise in
the measurement data. In this paper we study systematically the effect of the
sampling parameters in the validation of AATSR (Advanced Along-Track Scanning
Radiometer) aerosol optical depth (AOD) product against AERONET data and the
associated collocation mismatch uncertainty. To this end, we study the
spatial AOD variability in the satellite data, compare it against the
corresponding values obtained from densely located AERONET sites, and assess
the possible reasons for observed differences. We find that the spatial AOD variability in the satellite data is
approximately 2 times larger than in the ground-based data, and the spatial
variability correlates only weakly with that of AERONET for short distances.
We interpreted that only half of the variability in the satellite data is due
to the natural variability in the AOD, and the rest is noise due to retrieval
errors. However, for larger distances (∼ 0.5∘) the correlation is
improved as the noise is averaged out, and the day-to-day changes in regional
AOD variability are well captured. Furthermore, we assess the usefulness of
the spatial variability of the satellite AOD data as an estimate of CMU by
comparing the retrieval errors to the total uncertainty estimates including
the CMU in the validation. We find that accounting for CMU increases the
fraction of consistent observations.