Atmos. Chem. Phys., 14, 2905–2922, 2014www.atmos-chem-phys.net/14/2905/2014/doi:10.5194/acp-14-2905-2014© Author(s) 2014. CC Attribution 3.0 License.

Atmospheric Chemistry

and PhysicsO

pen Access

Global distributions, time series and error characterization ofatmospheric ammonia (NH3) from IASI satellite observations

M. Van Damme1,2, L. Clarisse1, C. L. Heald3, D. Hurtmans1, Y. Ngadi1, C. Clerbaux1,4, A. J. Dolman2,J. W. Erisman2,5, and P. F. Coheur1

1Spectroscopie de l’atmosphère, Chimie Quantique et Photophysique, Université Libre de Bruxelles, Brussels, Belgium2Cluster Earth and Climate, Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands3Department of Civil and Environmental Engineering and Department of Earth, Atmospheric and Planetary Sciences, MIT,Cambridge, MA, USA4UPMC Univ. Paris 06; Université Versailles St.-Quentin; CNRS/INSU, LATMOS-IPSL, Paris, France5Louis Bolk Institute, Driebergen, the Netherlands

Correspondence to:M. Van Damme (martin.van.damme@ulb.ac.be)

Received: 22 July 2013 – Published in Atmos. Chem. Phys. Discuss.: 16 September 2013Revised: 20 January 2014 – Accepted: 5 February 2014 – Published: 21 March 2014

Abstract. Ammonia (NH3) emissions in the atmospherehave increased substantially over the past decades, largelybecause of intensive livestock production and use of fertil-izers. As a short-lived species, NH3 is highly variable inthe atmosphere and its concentration is generally small, ex-cept near local sources. While ground-based measurementsare possible, they are challenging and sparse. Advanced in-frared sounders in orbit have recently demonstrated their ca-pability to measure NH3, offering a new tool to refine globaland regional budgets. In this paper we describe an improvedretrieval scheme of NH3 total columns from the measure-ments of the Infrared Atmospheric Sounding Interferometer(IASI). It exploits the hyperspectral character of this instru-ment by using an extended spectral range (800–1200 cm−1)where NH3 is optically active. This scheme consists of thecalculation of a dimensionless spectral index from the IASIlevel1C radiances, which is subsequently converted to a to-tal NH3 column using look-up tables built from forward ra-diative transfer model simulations. We show how to retrievethe NH3 total columns from IASI quasi-globally and twicedaily above both land and sea without large computationalresources and with an improved detection limit. The retrievalalso includes error characterization of the retrieved columns.Five years of IASI measurements (1 November 2007 to 31October 2012) have been processed to acquire the first global

and multiple-year data set of NH3 total columns, which areevaluated and compared to similar products from other re-trieval methods. Spatial distributions from the five years dataset are provided and analyzed at global and regional scales.In particular, we show the ability of this method to identifysmaller emission sources than those previously reported, aswell as transport patterns over the ocean. The five-year timeseries is further examined in terms of seasonality and inter-annual variability (in particular as a function of fire activity)separately for the Northern and Southern Hemispheres.

1 Introduction

Human activities over the last decades have substantially per-turbed the natural nitrogen cycle up to a level that is believedto be beyond the safe operating space for humanity (Gal-loway et al., 2008; Rockström et al., 2009, and referencestherein). Similar to the carbon cycle, anthropogenic pertur-bations to the nitrogen cycle originate from the production ofenergy and food to sustain human populations, which causesthe release of reactive nitrogen compounds (collectively ab-breviated as Nr), principally in the form of nitrogen oxides(NOx = NO + NO2), nitrous oxide (N2O), nitrate (NO3) andammonia (NH3).

Published by Copernicus Publications on behalf of the European Geosciences Union.

2906 M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations

The Nr released into the environment is dispersed by hy-drologic and atmospheric transport processes and can accu-mulate locally in soils, vegetation, and groundwater (Gal-loway et al., 2008). Excess Nr has important impacts on theenvironment, climate, and human health (e.g.,Sutton et al.,2011; Erisman et al., 2013) such as loss of biodiversity, veg-etation damage, and an increasing number of coastal deadzones (Bobbink et al., 2010; Krupa, 2003; Diaz and Rosen-berg, 2011). In fact, one single nitrogen atom, moving alongits biogeochemical pathway in ecosystems, can have a multi-tude of negative impacts in sequence (Galloway et al., 2003).This sequential process, known as the “nitrogen cascade”,has been described in theory but quantitatively large uncer-tainties exist on atmospheric emissions as well as chemistry,transport, and deposition of Nr. These are such that our un-derstanding of the environmental impacts of Nr is largely in-complete (Galloway et al., 2003; Erisman et al., 2013). It iscommonly acknowledged that the major uncertainties are re-lated to reduced nitrogen compounds, and in particular NH3(e.g.,Sutton et al., 2011; Erisman et al., 2007; Fowler et al.,2013).

Globally, the EDGAR v4.2 emission inventory estimatesthat about 49.3 Tg of NH3 was emitted in the atmosphere in2008, with 81 % of this amount related to agriculture: 58 %to agricultural soils, 21 % to manure management, and 2 %to agricultural burning. The second most important source ofNH3 is vegetation fires (16 % of global emissions in 2008),with substantial year-to-year variability (EDGAR-EmissionDatabase for Global Atmospheric Research, 2011). In addi-tion, the relative importance of emission sources show largevariations at local and regional scales: for instance, combus-tion associated with catalytic converters could contribute upto 10 % of the yearly emissions in the United States (Reiset al., 2009); in Europe the agricultural sector accounts foraround 94 % of the NH3 emissions (EEA-European Environ-ment Agency, 2012).

In the atmosphere, NH3 is a highly reactive and solu-ble alkaline gas. Reactions with acidic gases formed fromNOx, sulphur dioxide (SO2) emissions, as well as hydrochlo-ric acid (HCl), can form secondary ammonium (NH4+)particles, which are important components of atmosphericaerosols of anthropogenic origin (Seinfeld, 1986; Pinderet al., 2008). In this respect, model studies have shown thata reduction of secondary particulate matter in Europe couldonly be effectively achieved by reducing NH3 emissions inconcert with the reduction of other primary gases (Erismanand Schaap, 2004). The large uncertainties in emissions com-bined with the complexity associated with modeling aerosolformation are such that current models do not satisfactorilyreproduce atmospheric measurements of NH3. Current mod-els exhibit a general tendency to underestimate the concen-trations, at least in the industrialized Northern Hemisphere(Heald et al., 2012).

Observing the spatial and temporal distribution of atmo-spheric NH3 is therefore essential to better quantify emis-

sions, atmospheric concentrations, and deposition rates andto develop and evaluate relevant management strategies forthe future. Until recently, all available measurements werelimited to surface sites (Erisman et al., 2007), providing onlya local snapshot. Furthermore, these surface automated mea-suring devices are expensive and not always reliable, puttinga limit on what can be expected from these in situ measure-ments (Erisman et al., 2003; Laj et al., 2009). It is worthnoting that airborne (e.g.,Nowak et al., 2007, 2010) as wellas ship (e.g.,Norman and Leck, 2005; Sharma et al., 2012)NH3 observations have recently been reported but these arerestrained to campaigns with limited temporal and spatialcoverage. The recently discovered capability of advanced IR-sounders to probe atmospheric NH3 (Beer et al., 2008; Co-heur et al., 2009; Clarisse et al., 2009, 2010; Shephard et al.,2011) is an important step towards addressing this observa-tional gap. In particular, the Infrared Atmospheric SoundingInterferometer (IASI), which is aboard the European MetOppolar orbiting satellites, offers the potential for monitoringNH3 distributions globally and on a daily basis. This capa-bility stems, on the one hand, from its scanning mode and,on the other hand, from its unprecedented hyperspectral andradiometric specifications. The first global distributions wereacquired by IASI in 2009 following a simplified retrievalmethod (Clarisse et al., 2009) and by averaging the retrievedcolumns over a full year. In a subsequent case study of theSan Joaquin Valley in California (Clarisse et al., 2010), abetter understanding was achieved of what can be done withspace-based measurements, and of the different parametersthat affect measurement sensitivity (especially skin and at-mospheric temperatures). It is shown that, when the detectionis possible, the peak sensitivity for NH3 is in the atmosphericboundary layer (Clarisse et al., 2010). Walker et al.(2011)introduced an innovative detection method, which substan-tially improves the NH3 detection sensitivity of IASI. Theavailability of the satellite NH3 measurements from IASIor the Tropospheric Emission Spectrometer (TES) has trig-gered work on particulate inorganic nitrogen in the UnitedStates:Heald et al.(2012) andWalker et al.(2012) used forthis purpose one complete year of IASI measurements anda series of TES measurements, respectively, in combinationwith the GEOS-Chem model. Following these studies, TESmeasurements have been used to constrain emissions overover the United States (US) and have highlighted the under-estimation of emission inventories, particularly in the west(Zhu et al., 2013). IASI measurements have also been usedto confirm the importance of agricultural sources of anthro-pogenic dust and the non-negligible role of NH3 in deter-mining their properties (e.g., lifetime) (Ginoux et al., 2012).Finally, IASI-NH3 observations over Asia have been com-pared qualitatively to GEOS-Chem NH3 columns inKharolet al.(2013), suggesting that the emissions in the model arenot overestimated.

The present paper describes an improved retrieval schemefor near real-time global NH3 retrievals from IASI, building

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M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations 2907

on the work ofWalker et al.(2011). Here we retrieve NH3total columns from IASI with better sensitivity on a singlemeasurement and on a twice-daily basis (i.e., using both thenighttime and daytime measurements); importantly, we alsoprovide an appropriate error characterization. In the next sec-tion, we first review some of the main characteristics of IASI.In Sect. 3, the retrieval method is thoroughly described andits advantages are discussed against other existing retrievalschemes, especially the Fast Optimal Retrieval on Layers forIASI (FORLI) (Hurtmans et al., 2012). Section 4 providesan analysis of the global distributions of NH3 acquired withthis new retrieval method, underlining the improved sensi-tivity by the identification of new hotspots. In this last sec-tion the trends in NH3 concentrations over five years of IASIoperation are presented and discussed.

2 The Infrared Atmospheric Sounding Interferometer(IASI)

IASI is an infrared Fourier transform spectrometer, the firstof which was launched aboard MetOp-A in October 2006and has been operating since with remarkable stability(Hilton et al., 2012). A second instrument has been in op-eration on MetOp-B since September 2012, but here onlythe IASI-A measurements are analyzed. IASI combines theheritage of weather forecasting instruments with that of tro-pospheric sounders dedicated to atmospheric chemistry andclimate (Clerbaux et al., 2009; Hilton et al., 2012). It circlesin a polar Sun-synchronous orbit and operates in a nadir-viewing mode with overpass times at 9:30 local solar time(hereafter referred to as daytime measurements) and 21:30local solar time (nighttime measurements) when it crossesover the Equator. The nadir views are complemented by mea-surements off-nadir along a 2100 km wide swath perpen-dicular to the flight line. With a total of 120 views alongthe swath, IASI provides near global coverage two times aday. It has a square field of view composed of four circularfootprints of 12 km each at nadir, distorted to ellipse-shapedpixels off-nadir. IASI measures the infrared radiation emit-ted by the Earth’s surface and the atmosphere in the 645–2760 cm−1 spectral range at a medium spectral resolution of0.5 cm−1 apodized and low noise (∼ 0.2 K at 950 cm−1 and280 K) (Clerbaux et al., 2009). The spectral performance andhigh spatial and temporal sampling makes IASI a powerfulsounder to monitor atmospheric composition, with routinemeasurements of greenhouse gases and some reactive species(in particular CO, O3, HNO3) and other short-lived species,including NH3, above source regions or in very concentratedpollution plumes. In total, 24 atmospheric species have beenidentified in the IASI spectra (Clarisse et al., 2011).

Fig. 1. Top: Example of an IASI spectra between 645 and1300 cm−1 measured on 30 August 2011 in the California SanJoaquin Valley (USA). The orange range was used for the firstglobal NH3 distribution obtained by satellite (Clarisse et al., 2009),with the vertical lines representing the channels used to compute thebrightness temperature difference. The green range shows the spec-tral interval used by FORLI on IASI spectra and the pink ranges themicrowindows used for TES NH3 retrievals (Shephard et al., 2011).The large red and dark blue ranges are the continuous spectral inter-vals used for the NH3 detection in Walker et al. (2011) and in thiswork, respectively. Bottom: Transmittance ofν2 vibrational band ofNH3.

3 Retrievals of NH3 from IASI

3.1 Overview of retrieval schemes and spectral ranges

NH3 is detected in the thermal infrared spectral range in itsν2 vibrational band centered at around 950 cm−1 (Beer et al.,2008; Coheur et al., 2009). While many spectral featuresare potentially usable in the spectral range between 750 and1250 cm−1 (Fig. 1), the early retrievals from TES and IASIhave only used part of the available spectral information. Forinstance, TES retrievals (Shephard et al., 2011), which arebased on an optimal estimation strategy and provide weakly-resolved profiles, exploit only a set of microwindows withinthe strong Q-branch between 960 and 970 cm−1 (pink rangesin Fig. 1), while the first global distributions from IASI wereacquired using a brightness temperature difference based ona single NH3 feature at 867.75 cm−1 (vertical orange lines inFig. 1) (Clarisse et al., 2009). Near real-time distributions ofNH3 were later obtained from the Fast Optimal Retrieval onLayers for IASI (FORLI) processing chain described else-where in detail for CO, O3, and HNO3 profiles (Hurtmanset al., 2012). FORLI relies on a full radiative transfer modelusing the optimal estimation method for the inverse scheme.For the NH3 profile retrievals it uses specifically a priori con-straints from the TM5 model and a spectral range from 950to 979 cm−1 (green range in Fig.1). FORLI retrievals are,however, only performed on the IASI spectra from the morn-ing orbit and for which the NH3 signal is clearly detected

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−150 −100 −50 0 50 100 150

−80

−60

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0

20

40

60

80

0

0.5

1

1.5

2

2.5

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3.5

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4.5

5x 1016

Fig. 2. IASI morning NH3 observations (molec cm−2) on 15 Au-gust 2010 obtained using the FORLI processing chain. The highconcentrations measured in eastern Europe are due to the excep-tional emissions from the large Russian fires in the summer of 2010(R’Honi et al., 2013).

in a first step. This produces a limited number of retrievalsper day, which favor high concentrations (an example is pro-vided in Fig.2). Preliminary usage of the FORLI-NH3 re-trieved profiles for model studies also revealed difficulties inusing the averaging kernels (Heald et al., 2012), which aretoo low and not representative of the available information inthe measurements as a consequence of the retrieval settingschosen to ensure stability and convergence with the optimalestimation framework.

The retrieval scheme we developed here avoids theseweaknesses. One element of our improved retrieval algo-rithm relies on the detection algorithm ofWalker et al.(2011), which allows using a large spectral range. In fact, weextend the 800–1000 cm−1 range (red range in Fig.1) usedin Walker et al.(2011) to 800–1200 cm−1 (dark blue rangein Fig. 1). The second element of our improved algorithm isakin to the brightness temperature differences to column con-version fromClarisse et al.(2009), but also accounting forthe NH3 spectral signature dependence on thermal contrast.

3.2 A retrieval scheme based on the calculations ofhyperspectral range index

The retrieval scheme presented here is built on the detectionmethod described byWalker et al.(2011), which can detecttrace gases better than any other known method. It worksespecially well for those species which are only sometimesseen in IASI spectra. The first step is an extension of thisdetection method and consists of calculating a so-called Hy-perspectral Range Index (HRI hereafter) from each IASI ob-servation; in the second step the HRI is converted into a NH3total column using large look-up tables built from forward ra-diative transfer calculations under various atmospheric con-ditions. The two steps are detailed below.

3.2.1 Hyperspectral Range Index (HRI)

As opposed to brightness temperature differences (1BT)which usually rely on a single specific spectral channel inwhich the target species is optically active, the HRI takesinto account a broad spectral range to increase sensitivity. ForNH3 from IASI we consider almost the entireν2 vibrationalband, from 800 to 1200 cm−1 (dark blue range in Fig.1). Themethod developed inWalker et al.(2011) relies on the opti-mal estimation formalism (Rodgers, 2000), but with a gen-eralized noise covariance matrix that contains the entire ex-pected spectral variability due to all atmospheric parametersexcept NH3, in addition to the usual instrumental noise. Inthe spectral range selected here, the variability is associatedmainly with temperature, ozone, water, clouds, and surfaceemissivity. In addition to lowering the detection threshold,the method has the advantage of providing in a single re-trieval step (assuming linearity of the NH3 signature arounda vanishingly small abundance) a quantity that is representa-tive of the NH3 abundance, without having to retrieve otherparameters. It is this quantity that we refer to as the HRI. It issimilar, other than units, to the apparent column retrieved inWalker et al.(2011). However, unlike the optimal estimationmethod, no information about the vertical sensitivity can beextracted. The use of a fixed Jacobian to calculate HRI doesnot allow generating meaningful averaging kernels.

More specifically, we first construct a mean backgroundspectrumy and associated variance-covariance matrixSobsyfrom spectra that are assumed to have no detectable NH3signature. With these the HRI of a measured spectrumy isdefined as

HRI = G(y − y) (1)

with G the measurement contribution function

G = (KT Sobsy−1

K)−1

KT Sobsy−1

. (2)

HereK is the difference between a spectrum simulated witha given (small) amount and a spectrum simulated withoutNH3. For the forward model the Atmosphit software wasused (Coheur et al., 2005). With these definitions,K andGhave respectively radiance and inverse radiance units. TheHRI is, as a matter of consequence, dimensionless and willbe either positive or negative, depending on the sign ofK(positive for an absorption spectrum and negative for anemission spectrum).

For the calculation ofy and Sobsy we used a subset ofthe IASI spectra measured on the 15 August 2010 (around640 000 spectra in total), in which no NH3 was detected. Toselect spectra with no detectable ammonia signature, we usedan iterative approach (Clarisse et al., 2013). First, all spec-tra showing a significant brightness temperature difference at867.75 cm−1 (1BT > 0.25 K) were excluded (Clarisse et al.,2009). Second, from the remaining set, an initial variance–covariance matrixSobsy was built using the spectral interval

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Fig. 3. NH3 GEOS-Chem model profiles above a source area (red)and transported above sea (blue), used as reference for the radiativetransfer simulations.

between 900 and 970 cm−1 and the HRI initially calculated.Finally, the spectra with a measurable HRI were removed tobuild a newSobsy between 800 and 1200 cm

−1, which ulti-mately includes around 500 000 NH3-free measurements.

The conversion of the HRI to total columns of NH3 is notstraightforward and requires full radiative transfer simula-tions. In our method, the conversion is done using theoreticallook-up tables (LUTs) to achieve fast processing of the IASIdata set from 2007 to the present.

3.2.2 Look-up tables

The LUTs were built by simulating a large amount of IASIspectra using a climatology of 4940 thermodynamic atmo-spheric profiles above land and 8904 profiles above sea(Chevallier, 2001) with the Atmosphit line-by-line radia-tive transfer model. For these two categories a referenceNH3 vertical profile was used (Fig.3), which was con-structed from a series of GEOS-Chem (www.geos-chem.org)v8.03.01 model profiles representative of polluted (for theland) and transported (for the sea) conditions in 2009 at2◦ × 2.5◦ horizontal resolution. Model simulations were usedas a substitute for representative NH3 measured profiles thatwere not available. Figure3 shows that the land standard pro-file peaks at the surface and decreases rapidly with altitude,whereas the ocean profile has its maximum at around 1.4 km.To include in the LUTs a representative set of NH3 concen-trations, the profiles were scaled by 13 different values from0 to 10 (0; 0.1; 0.3; 0.5; 1; 1.5; 2.0; 3.0; 4.0; 5.0; 6.5; 8.0;10.0) for the ocean and by 30 different values from 0 to 200(0; 0.1; 0.3; 0.5; 1.0; 1.5; 2.0; 2.5; 3.0; 4.0; 5.0; 6.5; 8.0; 10;12.5; 15; 20; 25; 30; 35; 42.5; 50; 62.5; 75; 87.5; 100; 125;150; 175; 200) for the land. The largest concentrations for thesimulations thus correspond to a concentration at the surfaceof close to 185 ppb.

In addition to the varying NH3 concentrations, a criticaldimension in the LUTs is the thermal contrast near the sur-

Fig. 4. Look-up tables used to convert HRI into NH3 total columnsthrough the thermal contrast (left panels) with the associated re-trieval error (right panels), above land (top panels) and sea (bot-tom panels). The tables are built from about 450 000 simulated IASIspectra above land and about 116 000 above sea; see text for details.

face, which drives the sensitivity of the infrared measure-ments to boundary layer concentrations (e.g.,Clarisse et al.,2010; Bauduin et al., 2014; Deeter et al., 2007). It is de-fined here as the difference between the skin (surface) tem-perature and that of the air at an altitude of 1.5 km. Whilethe climatology ofChevallier(2001) encompasses a rangeof thermal contrasts, it does not include enough variabilityfor the large values that are the most favorable for probingNH3 (Clarisse et al., 2010). To ensure that the LUT charac-terizes these larger thermal contrasts, the surface temperaturewas artificially changed for each of the atmospheric profilesto provide reference cases characterized by thermal contrastsup to−20 to +20 K for the sea and−20 to +40 K for the land.

With these inputs, around 450 000 and 116 000 IASI spec-tra were simulated for land and sea, respectively, from whichtheoretical HRI values were calculated following Eq. (1).The LUTs, calculated independently for land and sea, linkthe HRI to the NH3 column concentration (in molec cm−2)and thermal contrast (K). This is achieved by averaging allthe NH3 columns in a box determined by a given thermalcontrast and a given HRI plus/minus as an estimated erroron each. The error on the thermal contrast (TC) is taken as√

2× 1 K (considering that both the skin and air temperaturehave an uncertainty of about 1 K;Pougatchev et al., 2009)while the error on the HRI was taken as 0.0306, which corre-sponds to the standard deviation of HRI calculated for spec-tra above an area without NH3 (20–30◦ N 30–40◦ W) for aperiod of 6 days (1–3 May 2010 and 1–3 October 2010). Theresulting LUTs are depicted in Fig.4. It shows that a givenHRI can be associated with different values of the NH3 col-umn, depending on thermal contrast: for values of thermal

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contrasts close to zero the HRI is almost vanishing (indicat-ing a signal below the measurement noise) and basically allvalues of NH3 total columns are possible for a narrow HRIrange. In contrast, for the largest values of TC, there is a one-to-one correspondence between HRI and the NH3 column.This dependence also appears very clearly in the estimatederrors (right panels in Fig.4) which are calculated as thestandard deviation of the NH3 columns inside the box definedpreviously. Errors are the largest (above 100 %) for low val-ues of TC and/or HRI, but above land they are generally wellbelow 25 % for TC above a few K and HRI larger than 0.5.Above sea, the HRI does not include large values (becausethe concentration range included in the forward simulationsis smaller) but the relative errors follow the same behavior.

As the LUTs were built for a zero viewing angle, a co-sine factor was applied to the calculated HRI values beforethe conversion in the LUTs to account for the increased pathlength at larger angles. The cosine factor was verified toproperly correct the angle dependence for larger values ofHRI. However, for lower values and low thermal contrasts,this factor appears to overcorrect and introduces a low biasfor the larger angles. This is possibly caused by the fact thatthe noise error covariance matrix partially accounts for theviewing angle when the spectral noise dominates over theNH3 spectral signature. In future versions of this product,the angle dependence could be removed completely by theuse of angle dependent error covariance matrices.

A last source of error arises from the retrieval sensitiv-ity to the vertical distribution of NH3. This sensitivity is notincluded in the error assessment presented here as the mea-surements provide no information regarding the NH3 verti-cal profile. As previously explained, two fixed profiles werechosen to retrieve NH3 columns: a source profile for landand a transported profile for sea. Such an approach allows usto provide a coherent five-year data set produced with rea-sonable computational power. To test the dependence of theretrieval on the vertical profile used to build the LUTs, wehave taken the observations over land on 15 August 2010 andused the LUT made for sea observations – built with a trans-ported profile – to retrieve the concentrations. We comparethe two sets of retrieved values and find that a regression lineweighted by the error on the retrievals suggests a factor oftwo between the NH3 columns obtained from a profile hav-ing its maximum at the surface and the columns obtain fromone having its maximum at 1.4 km. Given that the two pro-files differ substantially, it is reasonable to conclude that inthe large majority of cases the factor of two is likely an upperbound for the error introduced by considering these constantNH3 land/sea profiles.

Considering a detection threshold defined as 2σ on theHRI, an indicative total column detection threshold of NH3as a function of thermal contrast can be calculated using theLUTs. The result is shown in Fig.5 separately for land (redcurve) and for sea (blue curve). It indicates that when thecontrast in temperature is low (a value of−2.9 K for TC

Fig. 5. Lowest possible detectable NH3 total column, inmolec cm−2, above land (red) and sea (blue). The values are thosefor which the retrieved column would be significant below 2σ inHRI.

at 1.5 km corresponds to an almost vanishing contrast be-tween the surface and the air just above it), the measure-ment is insensitive to even very high concentrations of NH3.For the more favorable values of TC the IASI measurementsshould be able to measure NH3 down to the 1016 molec cm−2

level. As an illustration, this detection limit would allowmeasuring NH3 columns year round in the San JoaquinValley, where we measure columns varying above 1016 to4.3× 1017 molec cm−2 for 81 % of the observations (medianat 3.2× 1016 molec cm−2).

3.2.3 Global processing of IASI data

The HRI are calculated following Eq. (1) from the IASILevel 1C radiance spectra, using the meteorological Level2 information from the operational IASI processor (Augustet al., 2012) to calculate the thermal contrast. Note that occa-sionally the meteorological Level 2 data contain a completetemperature profile but no surface temperature. To not losevaluable data, for these scenes we retrieve the surface tem-perature directly from the spectra using window channels at957 and 2143 cm−1 (keeping the highest value) and the spec-tral emissivity database provided byZhou et al.(2011). Anexample of processing is shown in Fig.6 for the same day(15 August 2010, morning overpass time), as in Fig.2. HRIdistribution (top-left panel, Fig.6) depicts a global coveragewith highest values over eastern Europe and Russia associ-ated with the large fires outside of Moscow in 2010. Ther-mal contrast (top-right panel, Fig.6) is positive and highfor low- and mid-latitudes and lower at higher latitudes; itis negative above Antarctica and part of Greenland. Datafiltering is needed to remove unreliable columns from theNH3 distribution. The bottom panel of Fig.6 shows dataonly for the scenes that have a cloud fraction below 25 %and a surface temperature above 265.15 K. A more strict datafiltering could be carried out by using the error (bottom-left

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Fig. 6.From top to bottom and from left to right: HRI (no unit), thermal contrast (K), relative error (%) and NH3 total columns (molec cm−2)with posterior filtering, for 15 August 2010 in the morning, as in Fig. 2. The filtering applied here (apart from the HRI) removes all pointswith a cloud fraction above 25 % and a surface temperature below 263.15 K.

panel, Fig.6) to exclude unreliable columns, including thehigh measurements observed above Greenland or Antarctica.The comparison between Figs.2 (FORLI optimal estimationscheme) and6 (our new HRI-based retrieval scheme) reveal asignificant additional number of daily retrieved NH3 columnvalues. As an indication, even considering only values withan error below 50 %, we estimate a net gain of 16 082 mea-surements (or an additional 63 % number of measurements)with our improved scheme.

4 Results and discussion

4.1 Product evaluation

A first characterization of the product is provided in Fig.7,with a histogram of the relative error on the retrieved NH3column for four different situations corresponding to landand sea observations, separately for the IASI morning andevening overpasses. The histogram, which is based on fiveyears of observations, shows that the majority of measure-ments have an error above 75 %. These situations correspondto small values of the HRI (small NH3 column and/or lowvalue of the thermal contrast), and are, as expected, mainlyabove sea. Retrievals with an error smaller than 75 % arefound above land and sea primarily during daytime (espe-

cially for the lowest errors), when the thermal contrast isgenerally positive. As will be shown in Sect. 4.2 (see forexample Fig.10), the NH3 measurements above sea are allin coastal regions and can be attributed unambiguously totransport from nearby continental sources. Another impor-tant conclusion from Fig.7 comes from the significant num-ber of retrieved columns with errors below 50–75 % dur-ing the evening overpass of IASI, and which are very likelyassociated with temperature inversions at a given altitudewithin the boundary layer, i.e., negative thermal contrasts,which strongly increase the retrieval sensitivity at that alti-tude (Clarisse et al., 2010; Bauduin et al., 2014). Finally, weconclude that the retrievals with the lowest errors (smallerthan 25 % on the column) are obtained above land for thedaytime overpass and are associated with a large positivethermal contrast and a significant amount of NH3.

To test the performance of the different detection methodsand retrieval ranges we compare them in terms of sensitiv-ity. As the different quantities (brightness temperature differ-ences and effective retrieved columns) have different units,it is useful to look at the noise-to-signal ratio, which is di-mensionless. As a measure of the signal we take the mean re-trieved value of a collection of 100 spectra with a strong NH3signature. This value can then be used as a normalizationfactor for the noise. To measure the noise we take a large

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2912 M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations

Fig. 7. Number of IASI NH3 measurements with relative errors onthe total column in the range 0–25, 25–50, 50–75, 75–100 and above100 % over five years (from 1 November 2007 to 31 October 2012).

collection of spectra with no detectable NH3 signature andfor which we expect the retrieved (normalized) value (whichwe called2) to be close to zero. The distribution of2 valuesfor the different detection methods is depicted in Fig.8. Agood measure of the noise-to-signal ratio is the standard de-viation of these distributions. Our newly developed scheme,based on the calculation of the HRI, provides the small-est standard deviation (0.04), indicating a gain of sensitiv-ity as compared to the other detection methods. The com-parison with an HRI calculation based on a smaller spec-tral range (800–1000 cm−1, STD = 0.07) indicates the im-provement given by the consideration of an extended range(800–1200 cm−1). The improvement is largest in compari-son to the initial brightness temperature approach presentedin (Clarisse et al., 2009), characterized here by a standarddeviation of 0.30.

A quantitative comparison between the NH3 total columnsretrieved from FORLI and the HRI scheme presented hereis provided in Fig.9. The comparison is shown separatelyfor one day (15 August 2010 – similar to Figs.2 and6) andone year (2011, right panel) of IASI measurements, and isshown as a function of the HRI retrieval error (color bar).The agreement is excellent (closely matching the 1 : 1 slope)for the HRI retrievals with the smallest errors, as clearly seenfor 15 August, a day with very large NH3 total columns dueto the fires in Russia. For the HRI derived columns with anerror above 50 %, the FORLI retrievals are close to the a pri-ori, indicative of a small NH3 signature due to either lowNH3 or small values of the thermal contrast. When takenglobally, the correlation between the columns retrieved bythe two methods is high (Pearson’s r coefficient of 0.81 forthe year 2011) but with the HRI columns on average 35 %lower. Overall the agreement is very good, considering thevery different approaches and the dependence of the FORLIretrievals on the a priori. The HRI-based retrieval scheme re-moves this dependence, allows retrieval for the daytime and

Fig. 8. Histogram of the2 values of the new retrieval scheme (or-ange), for a smaller hyperspectral range (green) and for a bright-ness temperature difference detection method (Clarisse et al., 2009)(blue) for 15 August 2010 (morning overpass). The standard de-viation (STD) of 2 values associated with each approach showsthe gain of sensitivity of this work using the HRI-based retrievalscheme.

nighttime overpasses, above land and sea, and has the advan-tage of providing an associated error with each observation.

4.2 Global and regional distributions

With the HRI retrieval scheme, global distributions of NH3have been retrieved from IASI level 1C twice a day overfive years of IASI measurements from 1 November 2007to 31 October 2012. As the amount of daily data is not al-ways sufficient to obtain meaningful global distributions (dueto cloud cover and the availability of the temperature pro-files from the EUMETSAT operational processing chain),it is convenient to consider monthly or yearly averages forsome applications. However, averaging is not straightforwardas the error is highly variable. To tackle this issue two ap-proaches can be followed: applying a pre-filtering of the mea-surements by the relative error followed by an arithmetic av-eraging or using a weighted averaging method on the entiredata set. The pre-filtering approach has the advantage of us-ing only those measurements with the lowest error, but willlead naturally to a bias of the average towards the highestvalues (for which the error is lower). The weighted averag-ing method has the advantage of using all of the IASI ob-servations and therefore introduces a smaller bias into theaveraging. This is the approach chosen in what is presentednext, where the measurements over the period of interest arefurther gridded in 0.25◦ × 0.5◦ cells. The column in each cellis then a weighted average:

x =

∑wixi∑wi

, (3)

wherewi = 1/σ 2 andσ is the error – relative or absolute –on the retrieved column estimated on a pixel basis. The mean

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M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations 2913

Fig. 9. NH3 total columns in molec cm−2 retrieved in this work from the HRI retrieval scheme (y axis) versus those retrieved by FORLI (xaxis) for 15 August 2010 (left panel) and over the entire year 2011 (right panel). The colors refer to the error calculated with the HRI retrievalscheme. The black line represents the 1 : 1 slope and the red line the linear regression weighted by the HRI retrieval errors. The Pearson’srcoefficient, the values of slope and intercept from the linear regression are also given.

column itself can be assigned an errorσ , which is calculatedin a similar way:

σ =

∑ 1σi∑ 1σ2i

. (4)

Figure 10 shows the NH3 total column distribution inmolec cm−2 averaged using relative error weighting over thefive years, separately for the morning (top panel) and evening(bottom panel) overpass time. Note that a post-filtering of themean columns in the cells has also been carried out to obtainmore reliable distributions: all cells with less than 50 (150)measurements per cell and a mean errorσ larger than 75 %(58 %) for the morning orbit above land (sea) have been re-jected. The same was done for the evening data, with thresh-old values of 100 (300) individual observations per cell and100 % (58 %) on the mean error in the cell. Also the relativeerror weighted averaging approach using Eq. (3) preferen-tially accounts for measurements with low relative error andthis explains the large impact of fires plumes, as the IASI sen-sitivity and the NH3 retrieval scheme efficiency is higher inthese cases. Conversely, weighting with the absolute valueof the error may introduce a lower bias as large columnswith low relative error can still have large errors in abso-lute terms. Distributions averaged using absolute errors forthe weighting are shown in Fig.11, similarly to Fig.10. Wehave also carried out a post-filtering on the global distribu-tions of Fig. 11: for the morning mean, we have excluded allpixels with less than 100 measurements and a mean absoluteerror larger than 90 % of the averaged column above land andsea; for the evening mean, we have excluded data with lessthan 100 measurements per cell for land and sea and with anabsolute error larger than the averaged columns for land andthan 90 % of the averaged columns for sea. The differences

between Figs.10 and11 highlight the challenges associatedwith averaging data with large variability in concentrationsand errors. In the following discussion, we consider only thedistributions weighted with relative errors as it is representa-tive of the more reliable observations and highlights all theevents where high columns are observed over the course ofthe five years.

The daytime distribution (top panel in Fig.10) shows ex-treme average column values (up to 1.5× 1017 molec cm−2

with error around 60 %) in Russia, which are due to the2010 large fires and associated NH3 emissions that persistedfor several weeks in August 2010 (seeR’Honi et al., 2013,and references therein). Other fire-related hotspot regionsare seen over Alaska (from the 2009 fires), eastern Rus-sia (2011), South America (mainly from 2010), and centralAfrica (throughout 2008–2012).

Most other hotspot regions are related to agriculture.Asia is responsible for the largest NH3 emissions (EDGAR-Emission Database for Global Atmospheric Research, 2010),especially then over the Indo-Gangetic plain where we esti-mate total columns up to 6.4× 1017 molec cm−2 (19 % er-ror). This area is well known for its intensive agriculturalpractices and its industrial emissions (EDGAR-EmissionDatabase for Global Atmospheric Research, 2011). Addi-tional hotspot regions include the Fergana Valley and thecentral Asia area irrigated by the Syr Darya and the AmuDarya in Uzbekistan and Kazakhstan highlighted in previousstudies (Scheer et al., 2008; Clarisse et al., 2009), as wellas the North China Plain in China (see Fig.13 andClarisseet al., 2009). While many of these larger hotspots have al-ready been identified in our previous study (Clarisse et al.,2009) our new retrieval scheme allows detection of smallerand weaker NH3 sources. A striking example is the NH3detected above regions associated with the development of

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2914 M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations

Fig. 10. NH3 total columns (molec cm−2) and relative error (bottom-left inset, %) distributions for five years of IASI measurements (1November 2007 to 31 October 2012), in 0.25◦ × 0.5◦ cells for the morning (top panel) and evening (bottom panel) overpasses. The NH3distributions are a mean of all measurements within a cell, weighted by the relative retrieval error following Eq. (3). The error distributionsare a weighted mean of the relative error of all observations within a cell, following Eq. (4).

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M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations 2915

Fig. 11. NH3 total columns (molec cm−2) and relative error (bottom-left inset, %) distributions for five years of IASI measurements(1 November 2007 to 31 October 2012), in 0.25◦ × 0.5◦ cells for the morning (top panel) and evening (bottom panel) overpasses. TheNH3 distributions are a mean of all measurements within a cell, weighted by the absolute retrieval error following Eq. (3). The error distri-butions are a weighted mean of the absolute error of all observations within a cell following Eq. (4) and divided by the NH3 total columns toprovide an error distribution in %.

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2916 M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations

Fig. 12.NH3 total columns (molec cm−2) distribution averaged for February 2011. A post-filtering has been carried out excluding columnswith a mean error above 100 %. Transported NH3 is observed mainly from fire plumes on the west and south coast of western Africa (1), andmainly from agricultural sources on the west coast of India (2), the Bay of Bengal (3) and the Gulf of Guinea (4).

intensive center-pivot irrigation agriculture in Saudi Arabiaalready seen by the LANDSAT instrument (NASA, 2012).Other newly identified source areas in Asia include the mouthof the Shatt al-Arab river (Iraq), Thailand and Indonesia.These are likely caused by biomass burning events on theBorneo and Sumatra Islands (Justice et al., 2011) and inten-sive fertilizer application on Java Island (Potter et al., 2010).

The distribution of atmospheric NH3 in South Amer-ica is driven by the fire events in 2010 (e.g., center ofBrazil), but we can also identify new agricultural hotspotsaround Santiago (Chile) and in the Llanos area (Columbia,Venezuela) (LADA , 2008; Potter et al., 2010). Two mainagricultural source areas show up in North America: theUS Midwest region and the San Joaquin Valley (California,US), where high NH3 columns are observed throughout theyear (Clarisse et al., 2010) and up to 6.0× 1016 molec cm−2

(17 % error). NH3 columns are also now retrieved aboveeastern states of the US. Other new source areas are identi-fied in Canada, for example southeast of Calgary and south-east of Winnipeg. Both areas are known for their high an-thropogenic NH3 emission (Environment Canada, 2013). InAfrica, the largest columns are found over major agricul-tural regions, especially western Africa (Adon et al., 2010)as well as Sudan and Ethiopia (LADA , 2008). The largestcolumns were found in Nigeria, where averaged columnsup to 2.6× 1016 molec cm−2 (39 % error) were measured.Previously unreported hotspots include the Zambezi basin(Mozambique) and particularly the Bethal/Secunda area(South Africa). The location of the latter suggests industrialemissions as the main source. The availability of retrievalsover Oceania highlights hotspots in southern Australia andNew Zealand, which correspond to the main agricultural landuse systems for this continent (LADA , 2008; Potter et al.,2010). In Europe, the highest values are measured above theItalian Po Valley (up to 3.3× 1016 molec cm−2, 19 % error)and the Netherlands. The improved algorithm is also sensi-

tive above the UK (Sutton et al., 2013) and suggests markedemissions in eastern Europe.

The nighttime measurements show similar hotspots (bot-tom panel, Fig.10), but with larger relative errors caused bythe general lower thermal contrast for the nighttime over-pass. On a daily basis, the morning and evening distribu-tions can be quite different as the measurements will bestrongly dependent on the local thermal contrast (in sev-eral places, morning and evening measurements bring com-plementary information). While nighttime measurements ofNH3 have been reported before (Clarisse et al., 2010), thisis the first time that a global nighttime distribution is ob-tained, which constitutes in itself an important improvementover previous work.

Another major improvement compared to previous stud-ies (both from IASI or TES) is the clear observation oflarge transported plumes NH3, on the south coast of west-ern Africa, around India and Mexico, and to lesser extentoff the east coast of the United States. Atmospheric transportof NH3 above the Mediterranean and Adriatic seas, emittedfrom agricultural activities in the Spanish Ebro and ItalianPo valleys, respectively, is also observed by IASI as well asmore sporadic transport events on the Plata River (Argentinecoast). These results are spatially consistent with modeleddistributions of Nr deposition (Duce et al., 2008) and withNHx wet deposition simulations (Dentener et al., 2006). Amonthly distribution of NH3 columns (February 2011) is de-picted in Fig.12, showing (1) transported fires plumes onthe west and south coasts of western Africa, (2) NH3 trans-ported from agricultural sources on the west coast of India,(3) the Bay of Bengal, and (4) the Gulf of Guinea. The sourcetypes have been attributed by comparing the NH3 distribu-tion with fire radiative power (FRP) measurements from theMODerate resolution Imaging Spectroradiometer (MODIS)instrument (Justice et al., 2011). Similar export of NH3 frombiomass combustion and other continental sources to theocean has already been reported for the Atlantic and Indian

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M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations 2917

Fig. 13.NH3 distribution over eastern Asia (molec cm−2), following a griding method explicitly accounting for the IASI footprint on eachindividual measurement. The distribution is a five-year error-weighted average of the IASI daytime total columns in the region (a post-filteringexcluding cells with less than 10 observations has been carried out over land).

oceans (Norman and Leck, 2005) and for the Bay of Bengal(Sharma et al., 2012) during ship campaigns.

The griding method used for the global distribution inFig. 10 is not the most suitable for analyzing the NH3 spa-tial distributions at smaller scales, as it smooths out some ofthe finer features. Figure13 shows the regional NH3 distri-bution over eastern Asia as an example, where each columnis distributed in the corresponding IASI footprint (circular-to ellipse-shaped, depending on the angle off nadir) beforebeing averaged in a smaller 0.05◦ × 0.05◦ grid, following thesame method as described above (i.e., error weighted aver-ages, Eqs. 3 and 4). Fine-scale details of the emission re-gions are clearly revealed, with larger column values in areaswhere there is intensive agriculture. This is the case espe-cially in the Hebei, Henan, Shandong and Jiangsu provinces(North China Plain, 1 in Fig.13), which altogether accountfor approximately 30 % of the N fertilizer consumption and33 % of the crop production in China in 2006 (Zhang et al.,2010; Huang et al., 2012). The North China Plain as a wholeis responsible for 43 % of the NH3 emitted from fertiliza-tion in China, while it represents only 3.3 % of the nationalarea (Zhang et al., 2010; Huang et al., 2012). Several smaller

hotspots are detected in Fig.13, including (2) the Sichuanprovince where the emissions are mainly from livestock, (3)the Xinjiang province, near Ürümqi and in Dzungaria and(4) around the Tarim basin, where there are sheep manuremanagement and intensive fertilizer use (Huang et al., 2012;Li et al., 2012). Overall, the patterns observed above Chinaare in excellent qualitative agreement with the distribution ofsources from the recent emission inventory ofHuang et al.(2012).

4.3 Temporal evolution

In Fig. 14 we show the first time series of daily retrievedNH3 columns above land over the course of the five yearsaveraged separately for the entire Northern (NH) and South-ern Hemispheres (SH). The average values were calculatedfollowing Eq. (3), using only the columns measured dur-ing the morning orbits of IASI. Higher columns are mea-sured in the Northern Hemisphere (on average for the fiveyears 1.5× 1016 molec cm−2) as compared to the SouthernHemisphere (1.1× 1016 molec cm−2), which is mainly due

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2918 M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations

01/11/2007

01/02/2008

01/05/2008

01/08/2008

01/11/2008

01/02/2009

01/05/2009

01/08/2009

01/11/2009

01/02/2010

01/05/2010

01/08/2010

01/11/2010

01/02/2011

01/05/2011

01/08/2011

01/11/2011

01/02/2012

01/05/2012

01/08/2012

01/11/2012

0.00E+000

1.00E+016

2.00E+016

3.00E+016

4.00E+016

5.00E+016

NH

3 (m

olec

/cm

²)

Date

Fig. 14.Time series of daytime NH3 total columns above land, averaged each day separately over the Northern (NH, red) and the Southern(SH, blue) Hemispheres. The error bars correspond to the error calculated as Eq. (4) around the daily mean value. The red and blue lines are11 day running means.

to the dominance of agricultural sources in the northern partof the globe.

A seasonal cycle is observed in both hemispheres, withpeak columns during local spring and summer. The season-ality in the Northern Hemisphere is especially pronounced,with columns varying from about 1× 1016 molec cm−2 inwinter (from October to February) up to double in springand summer. The large columns measured throughout Au-gust 2010, which increase the average hemispheric columnup to 4× 1016 molec cm−2 on 5 August, are due to the largefires in central Russia (see, e.g., Figs.6 andR’Honi et al.,2013). In the Southern Hemisphere the seasonal cycle isless marked, the maximum columns being typically observedthere for a three-month period extending from August toOctober, which corresponds to the biomass burning periodin South America and southern Africa. In 2010 the emis-sion processes were particularly strong in South Americaand the average hemispheric NH3 value reaches 4.1× 1016

on 27 August. Note that the variations in the NH3 columnsas a function of the fire activity (from year to year) is qual-itatively similar to that observed for carbon monoxide, forwhich large enhancements in 2010 in particular have beenreported (Worden et al., 2013).

5 Conclusions and perspectives

We have described an improved method for the retrieval ofNH3 total column concentrations from IASI spectra, withimproved sensitivity and near real-time applicability. Themethod follows a two-step process. The first step consists ofmeasuring a so-called Hyperspectral Range Index from each

IASI spectrum using an optimal-estimation like approach, inwhich the measurement variance–covariance matrix is set torepresent the total spectral variability that is not attributableto NH3. A spectral range extending from 800 to 1200 cm−1,larger than that used in previous studies, was selected andthe matrix was built using one day of IASI measurements.In a second step the HRI is converted into a total columnof NH3, using look-up tables (separately for sea and land)built from forward radiative transfer calculations and consid-ering a large number of different atmospheric conditions. Theerror on the retrieved total columns was estimated from thelook-up tables and shown to be strongly related to the thermalcontrast, varying from more than 100 % to lower than 25 %error for the most favorable situations. The latter are usuallyabove land and during daytime, when there is a combinationof high thermal contrast and enhanced NH3. We show thatthis improved method retrieves NH3 with improved sensitiv-ity over previous retrieval schemes. A detailed comparisonwith the retrieval results from the FORLI software, whichuses a full radiative transfer model, showed good agreementoverall (correlation coefficient of 0.81, with FORLI biasedhigh by 35 %).

Retrievals of the NH3 columns for five years of IASI mea-surements have been performed, from which spatial distribu-tions (separately for morning and evening orbits of IASI) andfirst time series have been derived and analyzed. A number ofnew hotspots have been identified from the five-year averageglobal distributions, further highlighting the gain in sensitiv-ity over earlier retrieval schemes. Export of NH3, principallyon the west coast of Africa and around India and Mexico,have been observed for the first time. We have shown with the

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M. Van Damme et al.: Atmospheric ammonia (NH3) satellite observations 2919

example of eastern Asia that the improved retrieval methodalso detects fine patterns of emissions on the regional scale.Seasonal cycles have been studied from the time series, sep-arately for the Northern and Southern Hemispheres. The sea-sonality was shown to be more pronounced in the NorthernHemisphere, with peak columns in spring and summer. Inthe Southern Hemisphere, the seasonality is principally re-lated to the biomass burning activity, which leads to columnenhancements mainly from August to October. The summer2010 stands out in the time series in both hemispheres as aresult of the exceptional fires in Russia and in South Americaand Africa that year.

Currently both MetOp-A and MetOp-B are in operationand so in the near future twice as much data will be available.The IASI program is foreseen to last at least 15 years and willbe followed up by the IASI-NG mission aboard the MetOp-SG satellite series (Clerbaux and Crevoisier, 2013). IASI willtherefore provide the means to study global emissions ofNH3 and the long-term trends. Observation of NH3 exportmay also provide new information to assess the ocean fertil-ization from atmospheric NH3 deposition and the residencetime of this trace gas in export plumes. The high spatial andtemporal sampling of IASI observations also offers a suit-able tool for evaluation of regional and global models. Forthese purposes, validation (which is ongoing) is required,which is challenging as both the infrared satellite measure-ments and other monitoring methods each have their own setof limitations. Nevertheless, the complementarities betweenground-based, airborne, and ship measurements with satelliteinstruments and associated modeling efforts will increasinglyenable better assessments of the local to global atmosphericNH3 budget, spatial distributions, and long-term trends.

Acknowledgements.IASI has been developed and built under theresponsibility of the “Centre National d’Etudes Spatiales” (CNES,France). It is flown on board the Metop satellites as part of theEUMETSAT Polar System. The IASI L1 data are received throughthe EUMETCast near real-time data distribution service. Theresearch in Belgium was funded by the F.R.S.-FNRS, the BelgianState Federal Office for Scientific, Technical and Cultural Affairsand the European Space Agency (ESA-Prodex arrangements).Financial support by the “Actions de Recherche Concertées”(Communauté Française de Belgique) is also acknowledged.M. Van Damme is grateful to the “Fonds pour la Formation àla Recherche dans l’Industrie et dans l’Agriculture” of Belgiumfor a PhD grant (Boursier FRIA). L. Clarisse and P.-F. Coheurare respectively Postdoctoral Researcher and Research Associate(Chercheur Qualifié) with F.R.S.-FNRS. C. Clerbaux is grateful toCNES for scientific collaboration and financial support. C. L. Healdacknowledges funding support from NOAA (NA12OAR4310064).We gratefully acknowledge support from the project “Effects ofClimate Change on Air Pollution Impacts and Response Strategiesfor European Ecosystems” (ÉCLAIRE), funded under the EC 7thFramework Programme (Grant Agreement No. 282910). We wouldlike to thank J. Hadji-Lazaro for her assistance.

Edited by: C. H. Song

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