Collocation Mismatch Uncertainties in Satellite Aerosol Retrieval Validation

Abstract

Abstract. Satellite based aerosol products are routinely validated against ground based reference data, usually obtained from sunphotometer 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 representativiness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as 5 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 10 located AERONET sites, and assess the possible reasons for observed differences.We find that the spatial AOD variability in the satellite data is approximately two times larger than in the ground based data, and the local AOD variability values correlate only weakly for short distances. We interprete 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 15 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 in the validation. We find that accounting for CMU increases the fraction of consistent observations.

High-resolution post-process corrected satellite AOD

“…This study develops and validates the post-process correction and downscaling approach for MODIS AOD over Washington D.C. -Baltimore area during the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) campaign in 2011 (e.g. Garay et al (2017); Virtanen et al (2018)). The DRAGON campaign provides very dense coverage of accurate ground-based AERONET AOD measurements for validation of highresolution AOD products.…”