An evaluation of IASI-NH<sub>3</sub> with ground-based Fourier transform infrared spectroscopy measurements

Abstract

Abstract. Global distributions of atmospheric ammonia (NH 3 ) measured with satellite instruments such as the Infrared Atmospheric Sounding Interferometer (IASI) contain valuable information on NH 3 concentrations and variability in regions not yet covered by ground-based instruments. Due to their large spatial coverage and (bi-)daily overpasses, the satellite observations have the potential to increase our knowledge of the distribution of NH 3 emissions and associated seasonal cycles. However the observations remain poorly validated, with only a handful of available studies often using only surface measurements without any vertical information. In this study, we present the first validation of the IASI-NH 3 product using ground-based Fourier transform infrared spectroscopy (FTIR) observations. Using a recently developed consistent retrieval strategy, NH 3 concentration profiles have been retrieved using observations from nine Network for the Detection of Atmospheric Composition Change (NDACC) stations around the world between 2008 and 2015. We demonstrate the importance of strict spatiotemporal collocation criteria for the comparison. Large differences in the regression results are observed for changing intervals of spatial criteria, mostly due to terrain characteristics and the short lifetime of NH 3 in the atmosphere. The seasonal variations of both datasets are consistent for most sites. Correlations are found to be high at sites in areas with considerable NH 3 levels, whereas correlations are lower at sites with low atmospheric NH 3 levels close to the detection limit of the IASI instrument. A combination of the observations Published by Copernicus Publications on behalf of the European Geosciences Union. These results give an improved estimate of the IASI-NH3 product performance compared to the previous upper-bound estimates (−50 to +100 %).

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

“…This restriction does mean that a number of sites of the FTIR-NH 3 data set will not be used. For comparability of this study to the results of the IASI-LUT evaluation in an earlier study by Dammers et al (2016a) we include a short paragraph on the performance of the IASI-LUT and the more recent IASI-NN product when applying similar constraints.…”

“…For Wollongong the wide spectral windows are used. For a more detailed description of each of the stations see the publications listed in Table 1 or Dammers et al (2016a).…”

“…The CrIS retrieval will also be compared with corresponding IASI/FTIR retrievals using results from a previous study by Dammers et al (2016a). 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.…”

“…The FTIR-NH 3 product used in this study is similar to the set described in Dammers et al (2016a) and is based on the retrieval methodology described by Dammers et al (2015). The retrieval methodology uses two spectral microwindows with spectral width that depends on the NH 3 background concentration determined for the observation stations and location (wider window for stations with background concentrations less than one ppb (Rodgers, 2000) is used, implemented in the SFIT4 algorithm (Pougatchev et al, 1995;Hase et al, 2004Hase et al, , 2006.…”

“…The few validation studies showed a limited vertical, spatial and or temporal coverage of surface observations for a proper uncertainty analysis (Van Damme et al, 2015b;Sun et al, 2015). 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.…”