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.

Validation of the CrIS fast physical NH<sub>3</sub> retrieval with ground-based FTIR

“…The retrieval includes a retrieval error based on the uncertainties in the initial HRI and TC parameters. The more recent IASI-NN retrieval (Whitburn et al, 2016) follows similar steps but it makes use of a neural network. The neural network combines the complete temperature, humidity and pressure profiles for a better representation of the state of the atmosphere.…”

“…A recent study by Dammers et al (2016a) explored the use of Fourier transform infrared (FTIR-NH 3 , Dammers et al, 2015) observations to evaluate the uncertainty of the IASI-NH 3 total column product. The study showed the good performance of the IASI-LUT (look-up table; Van Damme et al, 2014a) retrieval with a high correlation (r ∼ 0.8), but indicated an underestimation of around 30 % due to potential assumptions of the shape of the vertical profile (Whitburn et al, 2016;IASI-NN, neural network), uncertainty in spectral line parameters and assumptions on the distributions of interfering species. The study showed the potential of using FTIR observations to validate satellite observations of NH 3 , but also stressed the challenges of validating retrievals that do not provide the vertical measurement sensitivity, such as the IASI-LUT retrieval.…”

“…For a comparison with previous reported satellite results, we included both the IASI-LUT (Van Damme et al, 2014a) and the IASI-NN (Whitburn et al, 2016) comparisons with the FTIR observations. To put the results of this study into perspective of the IASI-LUT and IASI-NN products we added Fig.…”

“…Both the IASI-LUT (Van Damme et al, 2014a) and the IASI-NN (Whitburn et al, 2016) retrievals from observations by the IASI instrument aboard MetOp-A will be used. A short description of both IASI retrievals is provided here; for a more in-depth description see the respective publications by Van Damme et al (2014a) and Whitburn et al (2016). The IASI instrument on board the MetOp-A platform is in a sun-synchronous orbit and has a daytime overpass at around 09:30 LST (local solar time) and a night-time overpass at around 21:30 LST.…”

Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using aerosol CCI satellite data

“…It is based on a statistical regression technique and the use of a neural network trained on synthetic IASI data. A similar scheme has already been applied for the retrieval of NH3 (ammonia) (Whitburn et al, 2016 andVan Damme et al, 2017). As input variables it uses the IASI L2 pressure, humidity and temperature information, spectral information and a CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) derived dust altitude climatology.…”