Articles | Volume 12, issue 4
https://doi.org/10.5194/amt-12-2331-2019
https://doi.org/10.5194/amt-12-2331-2019
Research article
 | 
15 Apr 2019
Research article |  | 15 Apr 2019

Characterization and evaluation of AIRS-based estimates of the deuterium content of water vapor

John R. Worden, Susan S. Kulawik, Dejian Fu, Vivienne H. Payne, Alan E. Lipton, Igor Polonsky, Yuguang He, Karen Cady-Pereira, Jean-Luc Moncet, Robert L. Herman, Fredrick W. Irion, and Kevin W. Bowman

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Cited articles

Alvarado, M. J., Payne, V. H., Mlawer, E. J., Uymin, G., Shephard, M. W., Cady-Pereira, K. E., Delamere, J. S., and Moncet, J.-L.: Supplement to “Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: recent updates evaluated with IASI case studies”, Atmos. Chem. Phys., 13, https://doi.org/10.5194/acp-13-6687-2013-supplement, 2013. 
Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E., McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., Staelin, D. H., Strow, L. L., and Susskind, J.: AIRS/AMSU/HSB on the aqua mission: design, science objectives, data products, and processing systems, IEEE T. Geosci. Remote, 41, 253–264, https://doi.org/10.1109/TGRS.2002.808356, 2003. 
Bailey, A., Blossey, P. N., Noone, D., Nusbaumer, J., and Wood, R.: Detecting shifts in tropical moisture imbalances with satellite-derived isotope ratios in water vapor, J. Geophys. Res.-Atmos., 122, 5763–5779, https://doi.org/10.1029/2010JD015197, 2017. 
Beer, R., Glavich, T. A., and Rider, D. M.: Tropospheric emission spectrometer for the Earth Observing System's Aura satellite, Appl. Optics, 40, 2356–2367, 2001. 
Beer, R., Shephard, M. W., Kulawik, S. S., Clough, S. A., Eldering, A., Bowman, K. W., Sander, S. P., Fisher, B. M., Payne, V. H., Luo, M., Osterman, G. B., and Worden, J. R.: First satellite observations of lower tropospheric ammonia and methanol, Geophys. Res. Lett., 35, L09801, https://doi.org/10.1029/2008GL033642, 2008. 
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Short summary
In this paper we take the first steps towards generating a multi-decadal record of the deuterium content of water vapor, useful for evaluating the moisture sources and processes affecting water vapor, by estimating the deuterium content from thermal IR radiances from the AIRS instrument. We find the AIRS-based measurements are sensitive to the deuterium content of water vapor in the middle and lower troposphere with a single measurement uncertainty of ~ 3 % and an accuracy of ~ 0.7 %.
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