Articles | Volume 7, issue 11
https://doi.org/10.5194/amt-7-3989-2014
https://doi.org/10.5194/amt-7-3989-2014
Research article
 | 
27 Nov 2014
Research article |  | 27 Nov 2014

MISR research-aerosol-algorithm refinements for dark water retrievals

J. A. Limbacher and R. A. Kahn

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Cited articles

Abdou, W. A., Martonchik, J. V., Kahn, R. A., West, R. A., and Diner, D. J.: A modified linear-mixing method for calculating atmospheric path radiances of aerosol mixtures, J. Geophys. Res., 102, 16883–16888, https://doi.org/10.1029/96JD03434, 1997.
Antoine, D., Morel, A., Leymarie, E., Houyou, A., Gentili, B., Victori, S., Buis, J.-P., Buis, N., Meunier, S., Canini, M., Crozel, D., Fougnie, B., and Henry, P.: Underwater Radiance Distributions Measured with Miniaturized Multispectral Radiance Cameras, J. Atmos. Ocean. Tech., 30, 74–95, 2013.
Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., and Gombos, D.: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications, B. Am. Meteorol. Soc., 92, 157–174, https://doi.org/10.1175/2010BAMS2946.1, 2011.
Barrot, G., Mangin, A., and Pinnock, S.: GlobColour Product User Guide, http://www.globcolour.info (last access: 31 January 2014), 2010.
Baum, B., Yang, P., Heymsfield, A., Platnick, S., King, M., Hu, Y., and Bedka, S.: Bulk scattering properties for the remote sensing of ice clouds: Part II. Narrowband models, J. Appl. Meteorol., 44, 1896–1911, https://doi.org/10.1175/JAM2309.1, 2005.
Short summary
We systematically explore the cumulative effect of MISR research aerosol retrieval algorithm assumptions, quantifying and correcting the main sources of uncertainty over ocean. High median spectral aerosol optical depth biases of ~0.024 at low AOD are reduced to ~0.01 with an improved, physically based ocean surface model, particle properties and mixtures, adaptive reflectance uncertainty estimates and pixel selection, minor radiometric calibration adjustments and more stringent cloud screening.