Ammonia, along with ammonium nitrate and ammonium sulfate aerosols, is important for the nitrogen cycle that directly or indirectly impacts air quality, water quality and the climate. In the atmosphere ammonia is a toxin, and it combines with sulfates and nitric acid to form ammonium nitrate and ammonium sulfate, which constitute ∼50 % of the mass of fine particulate matter (PM2.5) over land (e.g. Seinfeld and Pandis, 1988). These particles form smog and, in addition to being statistically associated with health impacts, such as bronchitis, asthma, cardiovascular disease and lung disease, cause premature deaths (Schwartz et al., 2002; Reiss et al., 2007; Pope et al., 2002, 2009; Crouse et al., 2012). For example, there is a 6 and 8 % increase in the risk of cardiopulmonary and lung cancer mortality associated with exposure to 10 µgm-3 increases in PM2.5 concentrations (Pope et al., 2002). In terms of climate change, ammonia's contribution to atmospheric aerosols (ammonium) has both a direct (reflection of solar radiation) radiative forcing effect of ∼-0.35 Wm-2 since pre-industrial times, and a potentially larger indirect (clouds) radiative forcing effect (e.g. Charlson et al., 1991; Myhre et al., 2013). Furthermore, reactive nitrogen (Nr) (e.g. ammonia (NH3), ammonium (NH4), nitrogen oxide (NO)) has increased by a factor of 2 to 5 over the last century (Reay et al., 2008; Lamarque et al., 2010), and anthropogenic ammonia gas emissions (i.e. concentrated animal feeding operations (CAFO), fertilizers, biofuel) are one of the IPCC AR5 Representative Pathway Concentration (RPC) species predicted to increase in the future (Lamarque et al., 2011; Ciais et al., 2013). Increasing atmospheric concentrations of ammonia have the potential to increase the global deposition of reactive nitrogen to nitrogen-poor ecosystems, which in turn increases the efficiency of the land and ocean in removing human-induced carbon dioxide from the atmosphere, thus acting as a carbon sink (“carbon dioxide fertilization effect” (Reay et al., 2008)). Excess deposition in terrestrial ecosystems leads to soil acidification and loss of biodiversity (e.g. Carfrae et al., 2004); and in coastal ecosystems causes eutrophication, algal blooms, and loss of fish and shellfish (e.g. Paerl et al., 2002). In spite of the significant role ammonia plays in our environment and health, there is still limited knowledge of the magnitude and seasonal/spatial distribution of NH3 emission sources, especially on a global scale. Therefore, satellite observations of ammonia provide an unprecedented opportunity to gain a greater understanding of atmospheric ammonia concentrations and to constrain model emission, which are still poorly known, especially outside of North America and Europe.

Observations from the NASA Aura Tropospheric Emission Spectrometer (TES) (Beer et al., 2001) Fourier Transform Spectrometer (FTS) launched on 15 July 2004, and the Infrared Atmospheric Sounder Interferometer (IASI) (Clerbaux et al., 2009) FTS launched on MetOp-A (19 October 2006) and MetOp-B (17 September 2012), have demonstrated the value of lower tropospheric ammonia satellite measurements. For example, IASI and TES observations have shown spatial and seasonal distributions of ambient tropospheric ammonia concentrations globally (Clarisse et al., 2009; Shephard et al., 2011; Van Damme et al., 2014a) and regionally (Beer et al., 2008; Clarisse et al., 2010). Also, combining these satellite ammonia emissions with coincident satellite observations of carbon monoxide has shown the potential of using the satellite-derived NH3:CO enhancement ratios to identify the ammonia emission sources and constrain NH3 emission inventories (e.g. Luo et al., 2015). These satellite observations have been initially evaluated with in situ ammonia surface observations. Comparisons of instantaneous twice daily satellite boundary layer averaged observations with footprints on the order of 5–15 km with commonly measured in situ bi-weekly averaged surface network observations can be challenging given the obvious sampling differences (horizontal, temporal, and vertical). Nevertheless, Pinder et al. (2011) was able to show that the TES ammonia observations reflect spatial gradients and seasonal trends when compared with overlapping bi-weekly CAMNet in situ surface observations. Similar evaluations of the IASI NH3 column retrievals have been performed using indirect surface and aircraft comparisons (Van Damme et al., 2014a).

Satellite observations of tropospheric ammonia are also contributing to better understanding of the ammonia emission inventories used in chemical transport models. Both IASI and TES satellite observations have been used to evaluate and improve ammonia emissions and transport in global (GEOS-Chem) and regional chemistry transport (Community Multiscale Air Quality (CMAQ)) models, which have been broadly under-predicting ammonia concentrations compared to the satellite observations, especially in large source regions like the central valley in California, USA. Some examples include using TES ammonia observations to provide top-down constraints on NH3 emissions in GEOS-Chem (Zhu et al., 2013). Heald et al. (2012) used IASI observations along with the GEOS-Chem chemical transport model to show that NH3 is likely underestimated in California, leading to a local underestimate of ammonium nitrate aerosol. At the same time, Walker et al. (2012) using TES observations showed a similar under-prediction of ammonia emissions by GEOS-Chem over California, which has among the largest concentrations of ammonia in the USA. TES satellite and in situ observations were also used to evaluate the new treatment of ammonia bidirectional fluxes in the CMAQ and GEOS-Chem models (Zhu et al., 2015). In addition, insights into the diurnal variability of animal NH3 emissions have been obtained by combining TES satellite with in situ ground-based and aircraft observations in order to develop and evaluated a new improved NH3 temporal emissions profile for CMAQ (Bash et al., 2013, 2015).

While these initial studies have greatly improved our knowledge of the magnitude, seasonal cycle and spatial distribution of NH3 emissions, there still remain large uncertainties in ammonia emissions and in the nitrogen cycle in general. Therefore, advancements in our understanding of ammonia emission around the globe will benefit from recent and new satellite ammonia observations. The Cross-track Infrared Sounder (CrIS) instrument is a FTS operated by the USA NOAA/NASA/DoD Joint Polar Satellite System (JPSS) program on Suomi National Polar-orbiting Partnership (NPP) satellite, which was launched on 28 October 2011. With its good radiometric calibration and instrument signal-to-noise ratio (SNR), CrIS also has the potential to globally monitor ammonia and to contribute to a better understanding of tropospheric ammonia variability over the globe. The overall objective of this analysis is to demonstrate the capability of the CrIS instrument to retrieve atmospheric ammonia. We present (i) the CrIS ammonia retrieval strategy including spectral microwindows and error analysis, (ii) simulations showing the retrieval vertical sensitivity, level-of-detectability, and performance, (iii) the example of the first CrIS observations of elevated ammonia over the Central Valley of California, USA, and (iv) initial comparison of these CrIS NH3 retrievals with coincident TES satellite and Quantum Cascade-Laser (QCL) surface observations (Miller et al., 2014).

The main governing satellite sensor characteristics for detecting ammonia in the infrared are the measurement noise, spectral resolution, and local overpass sampling time (as the thermal contrast is tightly correlated with the diurnal cycle). This section details the CrIS instrument specifications pertinent to ammonia observations, plus a summary of comparable FTS IASI and TES sensor specifications and corresponding ammonia measurement characteristics.

One thing that needs to be considered when comparing infrared satellite inferred retrieved profiles for species with limited information, such as ammonia, is that the true vertical resolution of the retrieved parameter is often more coarse than the reported retrieval vertical levels. One of the main reasons for performing retrievals at more levels than there are independent pieces of information is to capture the vertical sensitivity as it varies from profile-to-profile depending on the atmospheric state. However, due to this “oversampling”, the minor trace gas species (i.e. NH3) profiles often have several levels that are substantially influenced by the a priori profile (i.e. containing little information from the measurement). Depending on the purpose of the comparison, or the quantity the satellite retrieved observations are being compared against, there are a number of possible satellite comparison methods that can be implemented that take into account the true satellite retrieval sensitivity.

One approach often utilized when comparing the retrieved satellite profile, xc, against other profiles is to “map” the comparison data to the satellite levels using a linear weighted average and to apply the satellite averaging kernel and the a priori to the mapped in situ profile: xcest=xa+Axcmapped-xa. This comparison approach accounts for the satellite retrieval a priori bias and the sensitivity and vertical resolution by applying the satellites averaging kernel, A, and a priori, xa, to the comparison profile xcmapped (i.e. model or in situ). This method obtains an estimated profile, xcext, which represents what the satellite would measure for the same air mass sampled by the in situ measurements or model. Differences between xcext and x^ are presumed to be associated with the satellite measurement error on the retrieval and systematic errors resulting from parameters that were not well represented in the forward model (e.g. temperature, interfering gases, and instrument calibration), which are the latter two terms in Eq. (1). This procedure is used to compare the simulated modelled NH3 profiles with the satellite-retrieved profiles.

One advantage of the optimal estimation retrieval approach is that a retrieval error estimate can be computed in a straightforward manner based on retrieval input parameters. The total error covariance matrix Sx for a given parameter x on the retrieved levels i is given by Sx=(A-I)Sa(A-I)T︸smoothing+GSnGT︸measurement+∑iGKbiSb(GKbi)T︸systematic+crossstate, where Sb is the expected covariance of the non-retrieved parameter errors (Worden et al., 2004). This total error on the retrieved parameters is expressed as the sum of the smoothing (sometimes referred to as the “representation”) error (first term), the measurement error (middle term), and the systematic error (last term). The smoothing error is the uncertainty due to unresolved fine structure in the profile. The measurement error is the random instrument noise in the radiance spectrum propagated to the retrieval parameter, x. The systematic errors are any errors from uncertainties in the non-retrieved forward model parameters, some of which are systematic (i.e. errors in spectroscopic line parameters), and some of which change from cross-state errors propagated from retrieval-to-retrieval (i.e. interfering species). The observation error is defined as a sum of the measurement and systematic plus cross-state terms (last two terms in Eq. 5), which is useful to report when the smoothing error is accounted for in a comparison (e.g. assimilations, profile comparisons when the observational operator has been applied, see Sect. 4). Note that for this initial study we did not include any systematic errors (last term) in the total error estimates. Thus, the reported total random error covariance matrix, which is just the sum of the first two terms in Eq. (5), can be rearranged and simply written as the inverse of the Hessian H, Sx=H-1=KTSn-1K+Sa-1-1.

CrIS simulations are utilized to test the algorithm development and to determine the retrieval performance capabilities and characteristics. The CrIS NH3 retrieval algorithm is then applied to example CrIS observations over the San Joaquin Valley in California, USA, and compared with nearby TES satellite and quantum cascade laser (QCL) based surface observations (Miller et al., 2014).

This study presents a robust CrIS NH3 retrieval that demonstrates the capabilities of utilizing CrIS to measure tropospheric ammonia. Based on both CrIS simulations and real observations there are a number of insights gained in terms of the ability of CrIS to measure tropospheric ammonia. The peak CrIS sensitivity to NH3 is typically in the range of 900–750 hPa (∼1.0–2.5 km) depending on the atmospheric conditions. It has a minimum NH3 detection limit of a profile with a peak value of ∼1 ppbv (typically at the surface). The retrievals have limited information content with at most one piece of information (DOFS), which provides more of an average boundary layer mixing ratio value (or a partial column type) measurement as opposed to a true atmospheric NH3 profile, which would have a number of independent pieces of information in the vertical. The information content and sensitivity varies from profile-to-profile depending on the atmospheric conditions, with increased thermal contrast and ammonia concentrations providing improved measurement sensitivity. The retrieval performance based on simulations where the truth is known shows a small positive bias of ∼6 % with a standard deviation of ∼±20 % (ranging from ±12 to ±30 % over the vertical profile). Considering that these are ideal conditions where everything except the ammonia is known perfectly, these should be considered as lower bounds on the actual errors.

Retrievals from CrIS observations on 28 January 2013 during the DISCOVER-AQ field study over the Central Valley in California, USA, correlate well with nearby QCL and TES observations. CrIS values at ∼1 km compare quantitatively very well with the TES observations, and the differences are generally within the error estimates. The CrIS ammonia distribution map over a large domain including the Central Valley (USA), demonstrates its ability to capture the expected spatial distribution in the ammonia values from elevated values in the valley from anthropogenic agriculture emissions to lower ammonia values in the unpolluted (“clean”) surrounding mountainous regions.

There are a number of refinements to the retrieval strategy that will be addressed in the future to facilitate more routine global operational CrIS retrievals. Some of these potential improvements include the following: (i) accounting for impact of clouds on the NH3 retrieval (Shephard et al., 2011) either through screening, or more desirably retrieving the clouds (Kulawik et al., 2006; Eldering et al., 2008), (ii) further exploring the impact of interfering species (i.e. water vapour) on systematic errors on the ammonia retrieval as CrIS has a 0.625 cm-1 spectral resolution, and (iii) refinement of the CrIS surface property retrievals (i.e. surface temperature and emissivity) in the ammonia spectral region to further reduce their impact on the ammonia retrievals.