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Aerostat - Read Me First!

A guide to essential documentation about the aerosol datasets offered in Aerostat.

 

 A caution before using Giovanni-AeroStat

For many purposes, especially scientific research,  it is critical to read key background articles describing each satellite data set before drawing conclusions based on results obtained from Giovanni-AeroStat.

MODIS Level 2 Aerosols:

MISR Level 2 Aerosols

Note that particular observing conditions may degrade the accuracy of remotely-sensed data products, which may cause processing algorithms to fail and result in missing data.  Other types of conditions may make a data product less accurate, even though the data values may appear valid.  Thus, all remotely-sensed data products should be evaluated with caution, and with respect to conditions that may cause them to be incomplete or inaccurate.  Below we list several papers that address some of the challenges in working with and comparing remotely sensed aerosol data products.

MISR Aerosol Optical Depth (AOD) compared with AERONET:

  • Kahn, R. A., B. J. Gaitley, M. J. Garay, D. J. Diner, T. F. Eck, A. Smirnov, and B. N. Holben (2010) Multiangle Imaging SpectroRadiometer global aerosol product assessment by comparison with the Aerosol Robotic Network. J. Geophys. Res.115, D23209, doi:10.1029/2010JD014601.

MISR Aerosol Product Description and Comparison with MODIS

  • Kahn, R.A., D.L. Nelson, M.Garay, R.C. Levy, M.A. Bull, D.J. Diner, J.V. Martonchik, S.R. Paradise, and E.G. Hansen, and L.A. Remer (2009) MISR Aerosol product attributes, and statistical comparisons with MODIS. IEEE Trans. Geosci. Rem. Sens., 47, 4095-4114, doi:10.1109/TGRS.2009.2023115

MISR aerosol retrieval over dark water, and some comparisons with MODIS and AERONET over dark water

  • Kahn, Ralph, and Coauthors (2005) MISR Calibration and Implications for Low-Light-Level Aerosol Retrieval over Dark Water. J. Atmos. Sci.62, 1032–1052, doi: http://dx.doi.org/10.1175/JAS3390.1.
  • Kahn, R. A., M. J. Garay, D. L. Nelson, K. K. Yau, M. A. Bull, B. J. Gaitley, J. V. Martonchik, and R. C. Levy (2007) Satellite-derived aerosol optical depth over dark water from MISR and MODIS: Comparisons with AERONET and implications for climatological studies. J. Geophys. Res.112, D18205, doi:10.1029/2006JD008175.

Possible aerosol variability due to cloud contamination in multi-sensors

  • Tian, B., D. E. Waliser, R. A. Kahn, Q. Li, Y. L. Yung, T. Tyranowski, I. V. Geogdzhayev, M. I. Mishchenko, O. Torres, and A. Smirnov (2008) Does the Madden-Julian Oscillation influence aerosol variability? J. Geophys. Res.113, D12215, doi:10.1029/2007JD009372.  

Sampling biases comparing multi-sensors

  • Levy, R., G.G. Leptoukh, R.A. Kahn, V. Zubko, A. Gopalan, and L.Remer, 2009. A critical look at deriving monthly aerosol optical depth from satellite data. IEEE Trans. Geosci. Remt. Sens., 47, 2942-2956, doi:10.1109/TGRS.2009.2013842.

The datasets used in Giovanni-AeroStat as input are satellite Level 2 swath or scene products, but are output to gridded (Level 3), a process which averages the Level 2 pixels in each grid cell.   This computational process may also affect the quality and accuracy of the data.  We provide documentation of the computational steps performed by Giovanni, but a researcher may also need to know the methods used to create the datasets used by Giovanni.  

The responsible use of remote-sensing data for research requires a researcher to understand potential sources of uncertainty and bias in the data.  The production of remotely-sensed geophysical parameters from satellite-borne instruments is the result of a multi-step computational process, incorporating a number of assumptions.  Data are also often accompanied by quality control indicators representing the production algorithm’s confidence in the retrieval. These indicators can be used within Aerostat to filter suspect data (Aerostat will default to the science team’s recommendation for quality filtering.)

For more details on the production process, algorithms and quality assurance, we suggest users read:  

MODIS aerosol products, collection 51

  • Algorithm Theoretical Basis Document: Levy, R., L. Remer, D. Tanre, S. Mattoo, and Y. Kaufman, 2009. Algorithm for remote sensing of tropospheric aerosol over dark targets from MODIS, Collections 005 and 051.  Download PDF
  • Quality Assurance: Hubanks, P. A., 2012. MODIS Atmosphere QA Plan for Collection 005 and 051. Download PDF

MISR aerosol products, version F12_0022

  • Algorithm Theoretical Basis Document:  Diner, D., W. Abdou, T. Ackerman, K. Crean, H. Gordon, R. Kahn, J. Martonchik, S. McMuldroch, S. Paradise, B. Pinty, M. Verstraete, M. Wang, and R. West, 2008. Multi-angle Imaging Spectro-Radiometer Level 2 Aerosol Retrieval Algorithm Theoretical Basis. Download PDF

AERONET aerosol products

 


In summary, we encourage the use of Giovanni for research, and we are continually striving to make it an excellent tool for that purpose.   Successful research investigations, however, require that researchers fully understand the characteristics and limitations of the data they are using, as well as the characteristics and limitations of the tools they utilize to analyze such data. 

Should you have any questions, please contact gsfc-help-disc@lists.nasa.gov

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Last updated: May 02, 2013 10:39 AM ET
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