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Discussion papers
https://doi.org/10.5194/acp-2019-1193
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-1193
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 03 Feb 2020

Submitted as: research article | 03 Feb 2020

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This preprint is currently under review for the journal ACP.

An AeroCom/AeroSat study: Intercomparison of Satellite AOD Datasets for Aerosol Model Evaluation

Nick Schutgens1, Andrew M. Sayer2,8, Andreas Heckel3, Christina Hsu4, Hiren Jethva5, Gerrit de Leeuw6, Peter J. T. Leonard7, Robert C. Levy4, Antti Lipponen9, Alexei Lyapustin10, Peter North3, Thomas Popp11, Caroline Poulson12, Virginia Sawyer13,4, Larisa Sogacheva6, Gareth Thomas14, Omar Torres15, Yujie Wang16, Stefan Kinne17, Michael Schulz18, and Philip Stier19 Nick Schutgens et al.
  • 1Department of Earth Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
  • 2Universities Space Research Association, Columbia, USA
  • 3Department of Geography, Swansea University, Swansea, UK
  • 4Climate and Radiation Laboratory, Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, USA
  • 5Universities Space Research Association-GESTAR, NASA Goddard Space Flight Center, Greenbelt, USA
  • 6Finnish Meteorological Institute (FMI), Climate Research Programme, Helsinki, Finland
  • 7ADNET Systems, Inc., Suite A100, 7515 Mission Drive, Lanham, MD 20706, USA
  • 8Ocean Ecology Laboratory, NASA-Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
  • 9Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute, Kuopio, Finland
  • 10Laboratory for Atmospheres, NASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
  • 11German Aerospace Center (DLR), German Remote Sensing Data Center Atmosphere, Oberpfaffenhofen, Germany
  • 12School of Earth Atmosphere and Environment, Monash University, Australia
  • 13Science Systems and Applications (SSAI), Lanham, Maryland, USA
  • 14Remote Sensing group, Rutherford Appleton Laboratory, Harwell Campus, Didcot, Oxfordshire, UK
  • 15Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center (GSFC), Greenbelt, USA
  • 16University of Maryland Baltimore County, Baltimore, Maryland, USA
  • 17Max-Planck-Institut für Meteorologie, Hamburg, Germany
  • 18Norwegian Meteorological Institute, Blindern, Oslo, Norway
  • 19Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, UK

Abstract. To better understand current uncertainties in the important observational constraint to climate models of AOD (Aerosol Optical Depth), we evaluate and intercompare fourteen satellite products, representing 9 different retrieval algorithm families using observations from 5 different sensors on 6 different platforms. The satellite products, super-observations consisting of 1° × 1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010, are evaluated with AERONET (AErosol RObotic NETwork) and MAN (Maritime Aerosol Network) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15–25 % (depending on product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1° × 1° grid cells. Up to 50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations, and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

Nick Schutgens et al.

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Latest update: 18 Feb 2020
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Short summary
We intercompare 14 different datasets of satellite observations of aerosol. Such meaaurements are challenging but also provide the best opportunity to globally observe an atmospheric component strongly related to air pollution and climate change. Our study shows that most datasets perform similarly well on a global scale but that locally errors can be quite different. We develop a technique to estimate satellite errors everywhere, even in the absence of surface reference data.
We intercompare 14 different datasets of satellite observations of aerosol. Such meaaurements...
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