A flexible and robust neural network IASI-NH₃

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.