A flexible and robust neural network IASI-NH3 retrieval algorithm

Abstract : In 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.
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Submitted on : Wednesday, July 13, 2016 - 11:26:07 AM
Last modification on : Friday, July 26, 2019 - 1:28:04 PM

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Simon Whitburn, M. van Damme, L. Clarisse, S. Bauduin, C. L. Heald, et al.. A flexible and robust neural network IASI-NH3 retrieval algorithm. Journal of Geophysical Research: Atmospheres, American Geophysical Union, 2016, 121 (11), pp.6581-6599. ⟨10.1002/2016JD024828⟩. ⟨insu-01345167⟩

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