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Volume 6, issue 8 | Copyright

Special issue: Observations and modeling of aerosol and cloud properties...

Atmos. Meas. Tech., 6, 1919-1957, 2013
https://doi.org/10.5194/amt-6-1919-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Aug 2013

Research article | 08 Aug 2013

Aerosol retrieval experiments in the ESA Aerosol_cci project

T. Holzer-Popp1, G. de Leeuw2,3,4, J. Griesfeller5, D. Martynenko1, L. Klüser1, S. Bevan8, W. Davies8, F. Ducos7, J. L. Deuzé7, R. G. Graigner9, A. Heckel8, W. von Hoyningen-Hüne10, P. Kolmonen2, P. Litvinov7, P. North8, C. A. Poulsen11, D. Ramon14, R. Siddans11, L. Sogacheva2, D. Tanre7, G. E. Thomas9, M. Vountas10, J. Descloitres12, J. Griesfeller3, S. Kinne6, M. Schulz5, and S. Pinnock13 T. Holzer-Popp et al.
  • 1Deutsches Zentrum für Luft- und Raumfahrt (DLR), Deutsches Fernerkundungsdatenzentrum, Oberpfaffenhofen, Germany
  • 2Finnish Meteorological Institute (FMI), Helsinki, Finland
  • 3Department of Physics, University of Helsinki, Helsinki, Finland
  • 4TNO Environment and Geosciences, Dept. of Air Quality and Climate, Utrecht, the Netherlands
  • 5Norwegian Meteorological Institute, Oslo, Norway
  • 6Max-Planck-Institut für Meteorologie (MPI), Hamburg, Germany
  • 7Laboratoire d'Optique Atmosphérique, Lille, France
  • 8Department of Geography, Swansea University, Swansea, UK
  • 9Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK
  • 10Universität Bremen, Institute of Environmental Physics, Bremen, Germany
  • 11Rutherford Appleton Laboratory (RAL), Space Science and Technology Department, Chilton, UK
  • 12ICARE Data and Services Center, University of Lille, Lille, France
  • 13ESA, ESRIN, Frascati, Itlay
  • 14HYGEOS, Euratechnologies, Lille, France

Abstract. Within the ESA Climate Change Initiative (CCI) project Aerosol_cci (2010–2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL.

This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data: qualitatively by inspection of monthly mean AOD maps and quantitatively by comparing daily gridded satellite data against daily averaged AERONET sun photometer observations for the different versions of each algorithm globally (land and coastal) and for three regions with different aerosol regimes.

The analysis allowed for an assessment of sensitivities of all algorithms, which helped define the best algorithm versions for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol-type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR. It is noted that all these observations are mostly consistent for all five analyses (global land, global coastal, three regional), which can be understood well, since the set of aerosol components defined in Sect. 3.1 was explicitly designed to cover different global aerosol regimes (with low and high absorption fine mode, sea salt and dust).

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