A flexible and robust neural network IASI-NH₃
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Author(s)
Whitburn, S.
Van Damme, M.
Clarisse, L.
Bauduin, S.
Hadji-Lazaro, J.
Hurtmans, D.
Zondlo, M. A.
Clerbaux, C.
Coheur, P.-F
Heald, Colette L.
Date Issued
June 2016
Journal
Journal of Geophysical Research: Atmospheres
Publisher
American Geophysical Union (AGU)
Citation
Whitburn, S.; Van Damme, M.; Clarisse, L.; Bauduin, S.; Heald, C. L.; Hadji-Lazaro, J.; Hurtmans, D.; Zondlo, M. A.; Clerbaux, C. and Coheur, P.-F. “A Flexible and Robust Neural Network IASI-NH₃ retrieval Algorithm.” Journal of Geophysical Research: Atmospheres 121, no. 11 (June 2016): 6581–6599 ©2016 American Geophysical Union
Version
Final published version
Abstract
n this paper, we describe a new flexible and robust NH₃ retrieval algorithm from measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The method is based on the calculation of a spectral hyperspectral range index (HRI) and subsequent conversion to NH₃ columns via a neural network. It is an extension of the method presented in Van Damme et al. (2014a) who used lookup tables (LUT) for the radiance-concentration conversion. The new method inherits the advantages of the LUT-based method while providing several significant improvements. These include the following: (1) Complete temperature and humidity vertical profiles can be accounted for. (2) Third-party NH₃ vertical profile information can be used. (3) Reported positive biases of LUT retrieval are reduced, and finally (4) a full measurement uncertainty characterization is provided. A running theme in this study, related to item (2), is the importance of the assumed vertical NH₃ profile. We demonstrate the advantages of allowing variable profile shapes in the retrieval. As an example, we analyze how the retrievals change when all NH₃ is assumed to be confined to the boundary layer. We analyze different averaging procedures in use for NH₃ in the literature, introduced to cope with the variable measurement sensitivity and derive global averaged distributions for the year 2013. A comparison with a GEOS-Chem modeled global distribution is also presented, showing a general good correspondence (within ±3 × 10¹⁵ molecules.cm⁻²) over most of the Northern Hemisphere. However, IASI finds mean columns about 1–1.5 × 10¹⁶ molecules.cm⁻² (∼50–60%) lower than GEOS-Chem for India and the North China plain.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1002/2016JD024828
