Abstract. We use in situ observations from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network, the Midwest Ammonia Monitoring Project, 11 surface site campaigns as well as Infrared Atmospheric Sounding Interferometer (IASI) satellite measurements with the GEOS-Chem model to investigate inorganic aerosol loading and atmospheric ammonia concentrations over the United States. IASI observations suggest that current ammonia emissions are underestimated in California and in the springtime in the Midwest. In California this underestimate likely drives the underestimate in nitrate formation in the GEOSChem model. However in the remaining continental United States we find that the nitrate simulation is biased high (normalized mean bias > = 1.0) year-round, except in Spring (due to the underestimate in ammonia in this season). None of the uncertainties in precursor emissions, the uptake efficiency of N 2 O 5 on aerosols, OH concentrations, the reaction rate for the formation of nitric acid, or the dry deposition velocity of nitric acid are able to explain this bias. We find that reducing nitric acid concentrations to 75 % of their simulated values corrects the bias in nitrate (as well as ammonium) in the US. However the mechanism for this potential reduction is unclear and may be a combination of errors in chemistry, deposition and sub-grid near-surface gradients. This "updated" simulation reproduces PM and ammonia loading and captures the strong seasonal and spatial gradients in gas-particle partitioning across the United States. We estimate that nitrogen makes up 15-35 % of inorganic fine PM mass over the US, and that this fraction is likely to increase in the coming decade, both with decreases in sulfur emissions and increases in ammonia emissions.
Abstract. Ammonia (NH3) emissions in the atmosphere have increased substantially over the past decades, largely because of intensive livestock production and use of fertilizers. As a short-lived species, NH3 is highly variable in the atmosphere and its concentration is generally small, except near local sources. While ground-based measurements are possible, they are challenging and sparse. Advanced infrared sounders in orbit have recently demonstrated their capability to measure NH3, offering a new tool to refine global and regional budgets. In this paper we describe an improved retrieval scheme of NH3 total columns from the measurements of the Infrared Atmospheric Sounding Interferometer (IASI). It exploits the hyperspectral character of this instrument by using an extended spectral range (800–1200 cm−1) where NH3 is optically active. This scheme consists of the calculation of a dimensionless spectral index from the IASI level1C radiances, which is subsequently converted to a total NH3 column using look-up tables built from forward radiative transfer model simulations. We show how to retrieve the NH3 total columns from IASI quasi-globally and twice daily above both land and sea without large computational resources and with an improved detection limit. The retrieval also includes error characterization of the retrieved columns. Five years of IASI measurements (1 November 2007 to 31 October 2012) have been processed to acquire the first global and multiple-year data set of NH3 total columns, which are evaluated and compared to similar products from other retrieval methods. Spatial distributions from the five years data set are provided and analyzed at global and regional scales. In particular, we show the ability of this method to identify smaller emission sources than those previously reported, as well as transport patterns over the ocean. The five-year time series is further examined in terms of seasonality and interannual variability (in particular as a function of fire activity) separately for the Northern and Southern Hemispheres.
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.
Abstract. Limited availability of ammonia (NH 3 ) observations is currently a barrier for effective monitoring of the nitrogen cycle. It prevents a full understanding of the atmospheric processes in which this trace gas is involved and therefore impedes determining its related budgets. Since the end of 2007, the Infrared Atmospheric Sounding Interferometer (IASI) satellite has been observing NH 3 from space at a high spatio-temporal resolution. This valuable data set, already used by models, still needs validation. We present here a first attempt to validate IASI-NH 3 measurements using existing independent ground-based and airborne data sets. The yearly distributions reveal similar patterns between ground-based and space-borne observations and highlight the scarcity of local NH 3 measurements as well as their spatial heterogeneity and lack of representativity. By comparison with monthly resolved data sets in Europe, China and Africa, we show that IASI-NH 3 observations are in fair agreement, but they are characterized by a smaller variation in concentrations. The use of hourly and airborne data sets to compare with IASI individual observations allows investigations of the impact of averaging as well as the representativity of independent observations for the satellite footprint. The importance of considering the latter and the added value of densely located airborne measurements at various altitudes to validate IASI-NH 3 columns are discussed. Perspectives and guidelines for future validation work on NH 3 satellite observations are presented.
Retrieving concentrations of minor atmospheric trace gases from satellite observations is challenging due to their weak spectral signature. Here we present a new version of the ANNI (Artificial Neural Network for Infrared Atmospheric Sounding Interferometer, IASI) retrieval framework, which relies on a hyperspectral range index (HRI) for the quantification of the gas spectral signature and on an artificial feedforward neural network to convert the HRI into a gas total column. We detail the different steps of the retrieval method, especially where they differ from previous work, and apply the retrieval to three important volatile organic compounds: methanol (CH3OH), formic acid (HCOOH), and peroxyacetyl nitrate (PAN). The comparison of the retrieved columns with those from an optimal estimation inversion retrieval shows an overall excellent agreement: differences occur mainly when the sensitivity to the target gas is low and are consistent with the conceptual differences between the two approaches. We present retrieval examples over selected regions, comparison with previously developed products, and the global seasonal distributions including the first global distributions of PAN on a daily basis. The ANNI retrieval has been carried out on the whole time series of IASI observations (2007–2018), so that currently over 10 years of twice‐daily global CH3OH, HCOOH, and PAN total column distributions have been produced. This unique data set opens avenues for tackling important questions related to sources, transport, and transformation of volatile organic compounds in the global atmosphere.
The spatial comparison reveals a good overall agreement of the NH 3 distributions not only in these source regions but also over remote areas and over sea when transport is observed. On average, the measured columns exceed the modeled ones, except for a few cases. Large discrepancies over several industrial areas in Eastern Europe and Russia point to underestimated emissions in the underlying inventories. The temporal analysis over the three hot spot areas reveals that the seasonality is well captured by the model when the lower sensitivity of the satellite measurements in the colder months is taken into account. Comparison of the daily time series indicates possible misrepresentations of the timing and magnitude of the emissions. Finally, specific attention to biomass burning events shows that modeled plumes are less spread out than the observed ones. This is confirmed for the 2010 Russian fires with a comparison using in situ observations.
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.
scite is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
334 Leonard St
Brooklyn, NY 11211
Copyright © 2023 scite Inc. All rights reserved.
Made with 💙 for researchers