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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 10, 491-505, 2017
http://www.atmos-meas-tech.net/10/491/2017/
doi:10.5194/amt-10-491-2017
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
13 Feb 2017
Post-processing to remove residual clouds from aerosol optical depth retrieved using the Advanced Along Track Scanning Radiometer
Larisa Sogacheva1, Pekka Kolmonen1, Timo H. Virtanen1, Edith Rodriguez1, Giulia Saponaro1, and Gerrit de Leeuw1,2 1Climate Research, Finnish Meteorological Institute, Helsinki, 00560, Finland
2Department of Physics, University of Helsinki, Helsinki, 00560, Finland
Abstract. Cloud misclassification is a serious problem in the retrieval of aerosol optical depth (AOD), which might considerably bias the AOD results. On the one hand, residual cloud contamination leads to AOD overestimation, whereas the removal of high-AOD pixels (due to their misclassification as clouds) leads to underestimation. To remove cloud-contaminated areas in AOD retrieved from reflectances measured with the (Advanced) Along Track Scanning Radiometers (ATSR-2 and AATSR), using the ATSR dual-view algorithm (ADV) over land or the ATSR single-view algorithm (ASV) over ocean, a cloud post-processing (CPP) scheme has been developed at the Finnish Meteorological Institute (FMI) as described in Kolmonen et al. (2016). The application of this scheme results in the removal of cloud-contaminated areas, providing spatially smoother AOD maps and favourable comparison with AOD obtained from the ground-based reference measurements from the AERONET sun photometer network. However, closer inspection shows that the CPP also removes areas with elevated AOD not due to cloud contamination, as shown in this paper. We present an improved CPP scheme which better discriminates between cloud-free and cloud-contaminated areas. The CPP thresholds have been further evaluated and adjusted according to the findings. The thresholds for the detection of high-AOD regions (> 60 % of the retrieved pixels should be high-AOD (> 0.6) pixels), and cloud contamination criteria for low-AOD regions have been accepted as the default for AOD global post-processing in the improved CPP. Retaining elevated AOD while effectively removing cloud-contaminated pixels affects the resulting global and regional mean AOD values as well as coverage. Effects of the CPP scheme on both spatial and temporal variation for the period 2002–2012 are discussed. With the improved CPP, the AOD coverage increases by 10–15 % with respect to the existing scheme. The validation versus AERONET shows an improvement of the correlation coefficient from 0.84 to 0.86 for the global data set for the period 2002–2012. The global aggregated AOD over land for the period 2003–2011 is 0.163 with the improved CPP compared to 0.144 with the existing scheme. The aggregated AOD over ocean and globally (land and ocean together) is 0.164 with the improved CPP scheme (compared to 0.152 and 0.150 with the existing scheme, for ocean and globally respectively). Effects of the improved CPP scheme on the 10-year time series are illustrated and seasonal and temporal changes are discussed. The improved CPP method introduced here is applicable to other aerosol retrieval algorithms. However, the thresholds for detecting the high-AOD regions, which were developed for AATSR, might have to be adjusted to the actual features of the instruments.

Citation: Sogacheva, L., Kolmonen, P., Virtanen, T. H., Rodriguez, E., Saponaro, G., and de Leeuw, G.: Post-processing to remove residual clouds from aerosol optical depth retrieved using the Advanced Along Track Scanning Radiometer, Atmos. Meas. Tech., 10, 491-505, doi:10.5194/amt-10-491-2017, 2017.
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Short summary
Clouds reflect solar light much more strongly than aerosol particles. Therefore, the retrieval of aerosol optical depth is usually only attempted over cloud-free areas. A very strict cloud detection scheme has to be applied to remove all cloudy pixels. However, often not all clouds are detected. To remove possibly cloud-contaminated pixels, a cloud post-processing algorithm has been designed, which effectively solves the problem and results in smoother AOD maps and improved validation results.
Clouds reflect solar light much more strongly than aerosol particles. Therefore, the retrieval...
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