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Volume 11, issue 2 | Copyright
Atmos. Meas. Tech., 11, 925-938, 2018
https://doi.org/10.5194/amt-11-925-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 Feb 2018

Research article | 16 Feb 2018

Collocation mismatch uncertainties in satellite aerosol retrieval validation

Timo H. Virtanen, Pekka Kolmonen, Larisa Sogacheva, Edith Rodríguez, Giulia Saponaro, and Gerrit de Leeuw Timo H. Virtanen et al.
  • Finnish Meteorological Institute, Helsinki, Finland

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.

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We study the collocation mismatch uncertainty related to validating coarse-resolution satellite-based aerosol data against point-like ground based measurements. We use the spatial variability in the satellite data to estimate the upper limit for the uncertainty and study the effect of sampling parameters in the validation. We find that accounting for the collocation mismatch uncertainty increases the fraction of consistent data in the validation.
We study the collocation mismatch uncertainty related to validating coarse-resolution...
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