Articles | Volume 8, issue 11
Atmos. Meas. Tech., 8, 4699–4718, 2015
https://doi.org/10.5194/amt-8-4699-2015
Atmos. Meas. Tech., 8, 4699–4718, 2015
https://doi.org/10.5194/amt-8-4699-2015
Research article
06 Nov 2015
Research article | 06 Nov 2015

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

A. C. Povey and R. G. Grainger

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Cited articles

Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C., and Gumley, L. E.: Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103, 32141–32157, https://doi.org/10.1029/1998JD200032, 1998.
Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and Holmén, K.: Mesoscale Variations of Tropospheric Aerosols, J. Atmos. Sci., 60, 119–136, https://doi.org/10.1175/1520-0469(2003)060<0119:MVOTA>2.0.CO;2, 2003.
Barnes, W., Pagano, T., and Salomonson, V.: Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1, IEEE T. Geosci. Remote, 36, 1088–1100, https://doi.org/10.1109/36.700993, 1998.
Bates, J. J. and Barkstrom, B. R.: A maturity model for satellite-derived climate data records, in: 14th Conference on Satellite Meteorology and Oceanography, p. 2.11, Atlanta, GA, available at: http://ams.confex.com/ams/Annual2006/techprogram/paper_100658.htm (last access: 28 October 2015), 2006.
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Short summary
Clear communication of the uncertainty on data is necessary for users to make appropriate use of it. This paper discusses the representation of uncertainty in satellite observations of the environment, arguing that the dominant sources of error are assumptions made during data analysis. The resulting uncertainty may be more usefully represented using ensemble techniques (a set of analyses using different assumptions to illustrate their impact) than with traditional statistical metrics.