Articles | Volume 17, issue 4
https://doi.org/10.5194/acp-17-2981-2017
https://doi.org/10.5194/acp-17-2981-2017
Research article
 | 
28 Feb 2017
Research article |  | 28 Feb 2017

Fast retrievals of tropospheric carbonyl sulfide with IASI

R. Anthony Vincent and Anu Dudhia

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Cited articles

Amato, U., De Canditiis, D., and Serio, C.: Effect of apodization on the retrieval of geophysical parameters from Fourier-transform spectrometers, Appl. Opt., 37, 6537–6543, 1998.
Aydin, M., De Bruyn, W. J., and Saltzman, E. S.: Preindustrial atmospheric carbonyl sulfide (OCS) from an Antarctic ice core, Geophys. Res. Lett., 29, 73-1–73-4, https://doi.org/10.1029/2002GL014796, 2002.
Barkley, M. P., Palmer, P. I., Boone, C. D., Bernath, P. F., and Suntharalingam, P.: Global distributions of carbonyl sulfide in the upper troposphere and stratosphere, Geophys. Res. Lett., 35, l14810, https://doi.org/10.1029/2008GL034270, 2008.
Barnes, I., Becker, K. H., and Patroescu, I.: The tropospheric oxidation of dimethyl sulfide: A new source of carbonyl sulfide, Geophys. Res. Lett., 21, 2389–2392, https://doi.org/10.1029/94GL02499, 1994.
Berry, J., Wolf, A., Campbell, J. E., Baker, I., Blake, N., Blake, D., Denning, A. S., Kawa, S. R., Montzka, S. A., Seibt, U., Stimler, K., Yakir, D., and Zhu, Z.: A coupled model of the global cycles of carbonyl sulfide and CO2: A possible new window on the carbon cycle, J. Geophys. Res.-Biogeosci., 118, 842–852, https://doi.org/10.1002/jgrg.20068, 2013.
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Short summary
A fast method to estimate trace gases using the IASI sensor is presented and applied to tropospheric carbonyl sulphide (OCS). This rapid approach neglects non-linear effects, which introduces an 11 % error on average when estimating OCS but is significantly faster than iterative methods. All of the IASI data from 2014 were analysed to show seasonal and spatial trends in OCS. These results were compared to ground samples taken from seven different NOAA sites across the globe.
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