Retrieval of ammonia from ground-based FTIR solar spectra

Abstract

Abstract. We present a retrieval method for ammonia (NH 3 ) total columns from ground-based Fourier transform infrared (FTIR) observations. Observations from Bremen (53.10 15 molecules cm −2 ). In conditions with high surface concentrations of ammonia, as in Bremen, it is possible to retrieve information on the vertical gradient, as two layers can be distinguished. The retrieval there is most sensitive to ammonia in the planetary boundary layer, where the trace gas concentration is highest. For conditions with low concentrations, only the total column can be retrieved. Combining the systematic and random errors we have a mean total error of 26 % for all spectra measured at Bremen (number of spectra (N) = 554), 30 % for all spectra from Lauder (N = 2412), 25 % for spectra from Réunion (N = 1262) and 34 % for spectra measured at Jungfraujoch (N = 2702). The error is dominated by the systematic uncertainties in the spectroscopy parameters. Station-specific seasonal cycles were found to be consistent with known seasonal cycles of the dominant ammonia sources in the station surroundings. The developed retrieval methodology from FTIR instruments provides a new way of obtaining highly timeresolved measurements of ammonia burdens. FTIR-NH 3 observations will be useful for understanding the dynamics of ammonia concentrations in the atmosphere and for satellite and model validation. It will also provide additional information to constrain the global ammonia budget.

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

“…The standard deviation over these bins is also more or less constant, and the weak dependence on the number of observations in each bin indicates that most of the effect is coming from the random error on the observations. The relative difference becomes systematically smaller with increasing column total amounts, and tends towards zero with a standard deviation ∼ 25-50 %, which is on the order of the reported estimated errors of the FTIR retrieval (Dammers et al, 2015).…”

“…For the interfering species a priori profiles we use the Whole Atmosphere Community Climate Model (WACCM, Chang et al, 2008, v3548). The estimated errors in the FTIR-NH 3 retrievals are of the order of ∼ 30 % (Dammers et al, 2015) with the uncertainties in the NH 3 line spectroscopy being the most important contributor. Based on the data requirements in Sect.…”

“…The total column of our example profile is ∼ 20 × 10 15 molecules cm −2 which is a slightly larger value than average. The total random error is < 10 % for each of the layers, mostly dominated by the measurement error, which is somewhat smaller than average (Dammers et al, 2015) following the larger NH 3 VMR. A similar value is found for the CrIS measurement error with most layers showing an error < 10 %.…”

“…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.…”