Articles | Volume 10, issue 2
https://doi.org/10.5194/amt-10-491-2017
https://doi.org/10.5194/amt-10-491-2017
Research article
 | 
13 Feb 2017
Research article |  | 13 Feb 2017

Post-processing to remove residual clouds from aerosol optical depth retrieved using the Advanced Along Track Scanning Radiometer

Larisa Sogacheva, Pekka Kolmonen, Timo H. Virtanen, Edith Rodriguez, Giulia Saponaro, and Gerrit de Leeuw

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Cited articles

Baker, N.: VCM ATBD: VIIRS cloud mask algorithm theoretical basis document: 474-00033, available at: https://jointmission.gsfc.nasa.gov/sciencedocs/2015-06/474-00044_Rev-Baseline.pdf (last access: 6 February 2017), 2013.
Birks, A.: ESA, VEGA Group PLC, and University of Leicester, AATSR Product Handbook, ESA, 2.2 Edn., available at: http://envisat.esa.int/handbooks/aatsr/CNTR.html (last access: 6 February 2017), 2007a.
Birks, A. R.: AATSR Technical Note: Improvements to the AATSR IPF relating to Land Surface Temperature Retrieval and Cloud Clearing over Land, Science and Technology Facilities Council, Rutherford Appleton Laboratory, 2007b.
Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'Neill, N. T., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols, J. Geophys. Res., 104, 31333–31349, 1999.
Flowerdew R. J. and Haigh J. D.: An approximation to improve accuracy in the derivation of surface reflectance from multi-look satellite radiometers, Geophys. Res. Lett., 23, 1693–1696, 1995.
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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.