Retrieving radius, concentration, optical depth, and mass of different types of aerosols from high-resolution infrared nadir spectra

Retrieving radius, concentration, optical depth,and mass of different types of aerosols from

high-resolution infrared nadir spectra

Lieven Clarisse,1,* Daniel Hurtmans,1 Alfred J. Prata,2

Federico Karagulian,1 Cathy Clerbaux,3,1

Martine De Mazière,4 and Pierre-François Coheur1

1Spectroscopie de l’Atmosphère, Service de Chimie Quantique et Photophysique,Université Libre de Bruxelles (ULB), Brussels, Belgium

2Climate and Atmosphere Department, Norwegian Institute for Air Research, P.O. Box 100, 2027 Kjeller, Norway3UPMC University Paris 06, Université Versailles Saint Quentin; CNRS/INSU, LATMOS-IPSL, Paris, France

4Belgian Institute for Space Aeronomy, 3 Avenue Circulaire, B-1180 Brussels, Belgium

*Corresponding author: [email protected]

Received 15 March 2010; revised 12 May 2010; accepted 18 May 2010;posted 2 June 2010 (Doc. ID 125477); published 23 June 2010

We present a sophisticated radiative transfer code for modeling outgoing IR radiation from planetaryatmospheres and, conversely, for retrieving atmospheric properties from high-resolution nadir-observedspectra. The forward model is built around a doubling-adding routine and calculates, in a spherical re-fractive geometry, the outgoing radiation emitted by the Earth and the atmosphere containing one layerof aerosol. The inverse model uses an optimal estimation approach and can simultaneously retrieve at-mospheric trace gases, aerosol effective radius, and concentration. It is different from existing codes, asmost forward codes dealing with multiple scattering assume a plane-parallel atmosphere, and as for theretrieval, it does not rely on precalculated spectra, the use of microwindows, or two-step retrievals. Thesimultaneous retrieval on a broad spectral range exploits the full potential of current state-of-the-arthyperspectral IR sounders, such as AIRS and IASI, and should be particularly useful in studying majorpollution events. We present five example retrievals of IASI spectra observed in the range from 800 to1200 cm−1 above dust, volcanic ash, sulfuric acid, ice particles, and biomass burning aerosols. © 2010Optical Society of AmericaOCIS codes: 010.1100, 010.0280, 010.5620.

1. Introduction

Aerosols impact our climate through a number ofdirect and indirect effects. The direct effects are ab-sorption and scattering of solar and terrestrial radia-tion, while indirect effects mainly occur through theinteraction of aerosols with cloud formation andchanges in precipitation efficiencies. Large uncer-tainties are associated with the magnitude of these

effects on the radiation budget, although major pro-gress has been made in recent years both from anobservational and a modeling point of view [1]. Glo-bal aerosol emissions are dominated in mass by seasalt (sea spray), sulfate (from volcanic and anthropo-genic SO2), mineral dust (dust, sand, and volcanicash), particulate organic matter (from biomass andfossil fuel burning but also from secondary organicaerosols), and black carbon (mostly from incompletecombustion of fossil fuels, biofuel, and biomass) [2].

Given the large variability of aerosols in timeand space, satellite observations have become

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indispensable in monitoring their distribution andcomposition [3,4]. The longest record of satellite mon-itoring of aerosols is provided by nadir-looking broad-band sensors, such as AVHRR [5] (from the visible tothe IR), TOMS [6] (ultraviolet), and the SAGE instru-ments [7]. These have been used mainly to detect air-borne aerosol and to determine the aerosol opticaldepth (see [1] and references therein). While theSAGE series of instruments were aimed at retrievalsof stratospheric water vapor and aerosol, AVHRRand TOMS were not designed to retrieve aerosolproperties, and so the error bars and biases on theretrievals were quite large. New dedicated aerosol/cloud sounders, such as MODIS [8], MISR [9], andPOLDER [10,11], offer, respectively, a larger spectralrange with more channels, multiple angle viewingcapability, and polarization measurements, whichsignificantly enhance the accuracy of the retrievedparameters. These also allow retrospective calibra-tion of older products (see [12] and references there-in) and retrieval of physical properties, such as theaerosol effective radius and aerosol type. The newestgeneration of aerosol sounders, such as the APS [13],are able to retrieve, simultaneously, a range of opti-cal parameters (optical depth and single-scatteringalbedo) and physical parameters (effective radiusand variance, refractive index, and particle shape)via accurate intensity and polarization measure-ments along an increased number of viewing direc-tions. Apart from passive nadir measurements, lidarmeasurements, such as those performed by CALIOP[14], are extremely useful in determining accurate al-titude information.

Aerosol remote sensing in the IR has received com-paratively less attention than in the visible/near IR,mainly because of its limited sensitivity to smallerparticles and particles located in the boundary layer.Yet sounding of aerosols in the IR has a number ofadvantages, such as a good sensitivity to larger par-ticles, ability to observe aerosols during the night,the possibility of retrieving altitude information, andlow dependency on particle shape and surface reflec-tance (i.e., observations over deserts are possible).The radiative transfer in the IR is strongly influ-enced by the refractive indices of the aerosols, whichcan vary rapidly over a small spectral range. Thismakes IR sounding especially attractive to deter-mine aerosol type, and with the advent of high-resolution IR measurements, it may be possible tostudy composition as well.

Early work on dedicated retrievals from IR soun-ders includes the IR difference dust index method,which uses only one broadband channel for the re-trieval of optical thickness (see [15] and referencestherein). Later, several methods were developedusing two or three channels and split-window tech-niques built around lookup tables to detect soil, vol-canic, and ice aerosols and to retrieve their opticalthickness, masses, and size of their particles [16–20].Such methods have recently been extended for IRsounders with higher resolution, such as the AIRS

and IASI sounders [21–31]. The most common meth-od is to simulate a large number of atmospheres withdifferent aerosol loadings (or heights, effective radii)and match those using a least-squares procedurewith the observed spectra. To minimize interferenceswith molecules, a limited number of microwindows isoften chosen, working around the largest molecularabsorption features. Remaining interferences are in-corporated by using a priori information such as thatfrom the corresponding European Center for Med-ium Range Weather Forecasting data or retrieved ina preliminary step to the aerosol retrievals.

IASI is a high-resolution (0:5 cm−1 apodized) IRsounder with broad spectral coverage (645−2760 cm−1) and low instrumental noise (∼0:2K at300K) in the window 800–1200 cm−1 [32]. The instru-ment was launched in 2006 onboard the MetOp plat-form and offers almost twice daily global nadircoverage. In this paperwe present a sophisticated for-ward model and retrieval tool for IASI, capable of thesimultaneous retrieval of atmospheric trace gasesand aerosol radius and concentration using a broadspectral range of the thermal spectrum at high spec-tral resolution. It is designed to study intense events(volcanic eruptions, dust storms, and large fires),where it can be desirable to study both the gas andthe aerosol chemistry orwhere the presence of aerosolcan affect the retrieval of trace gases and vice versa.Themethoddoesnot rely on lookup tables or theuse ofmicrowindows but adjusts the atmospheric para-meters iteratively using an optimal estimation ap-proach, thereby exploiting the full potential of IRsounders. The forward model is built around the ex-isting Atmosphit code [33] with the addition of a dou-bling-adding routine, which is described in Section 2.The inverse model and five example retrievalsrepresentative of five different aerosol classes are pre-sented in Section 3. We discuss some of the less-frequent types of aerosol observed in the IR, suchas biomass burning aerosols and sulfate aerosols.

2. Forward Model

The Université Libre de Bruxelles maintains a so-phisticated line-by-line radiative transfer forwardand inverse code called Atmosphit [33]. It supportscommon geometries with full ray tracing on a sphe-rical refractive geometry. The forward model dividesthe atmosphere into a user-defined number of dis-crete layers for which the average pressure, tem-perature, and molecular densities along the path ofradiation are calculated using an adaptive curvi-linear integration method. The refractive indices ofthe atmosphere are computed from the Edlén formu-lation using the representation of Birch and Downs[34]. Using these averaged layer properties, absorp-tion intensities are calculated. Atmosphit features ahighly optimized and careful computation of absorp-tion lines with both Voigt and Galatry line shapesfully based on impact approximation (with extra cor-rections for CO2 saturated bands) and support for thewater vapor and CO2 absorption continua (MT_CKD

3714 APPLIED OPTICS / Vol. 49, No. 19 / 1 July 2010

1.03), O2 and N2 collision-induced absorptions.Radiation is calculated using the discrete radiativetransfer equation. For the forward calculation ofthermal IR outgoing radiation, first a downward rayalong a fixed angle of 53:5° is calculated, represent-ing a best guess (see also [35]) of a representativedownward ray for the Lambertian reflectance of theradiation at the surface of the Earth. Then the up-ward radiation is calculated using the reflected ra-diation and Earth radiation as a source alongthe specified viewing angle. Until now, the radiativetransfer of aerosols was not implemented inAtmosphit.

A large number of methods have been developed tosolve the radiative transfer equation with multiplescattering; see, e.g., [36] for a review. In thermalIR, the radiative transfer is heavily simplified, asthermal radiation is azimuthally independent aslong as we are dealing with spherical or randomlyorientated particles. Many public multiple scatteringcodes also make the plane parallel assumption (suchas DISORT [37] and derivatives). However, this isonly a good approximation when the viewing angleis small; as for larger viewing angles, the layeredaveraged temperature, pressure, and densities canbe significantly different from those in a sphericalgeometry. On the other hand, a full 3D radiativetransfer (as implemented in, for instance, ARTS[38]) is computationally expensive in modeling out-going thermal IR radiation in nadir geometry andimpractical for inverse retrievals. We have adopteda variant of the doubling-adding method [39], whichdeals rigorously and efficiently with the effects ofmultiple scattering while preserving the 2D featuresof Atmosphit. The method described below was de-signed so that only minimal changes were requiredin the original code of Atmosphit, with convergenceto the original model for aerosol optical depths tend-ing to zero. We have made the assumption that theaerosol load is confined to a single layer.

The two most popular methods for solving the ra-diative transfer equation are the discrete ordinatesmethod and the doubling-adding method [40]. Bothmethods assume a layered atmosphere and discre-tize the zenith angle in a fixed number of 2n streams,n upward and n downward. This amounts to re-placing the integral over the zenith angle in the ra-diative transfer equation by a quadrature over adiscrete number of fixed angles. By increasing thenumber of streams, accuracy can be improved at thecost of computation time (8–16 streams are usuallysufficient). While the discrete ordinates method thensolves the discretized radiative transfer equation ex-plicitly, the doubling–adding method uses a morephysical approach via the calculation of reflectanceand transmittance matrices. This is done for eachdiscrete layer, after which iterative adding rulescan be applied to govern the total outgoing radiation.For the calculation of the matrices for the individuallayers, doubling rules can be used, starting from alayer with an infinitesimal optical depth for which

single scattering can be assumed. Apart from thefixed zenith angles, it is possible to add extra angleswith zero weight (so that the rays along these anglesdo not contribute to the radiative transfer). We referthe reader to [41] for the detailed mathematics ofadding–doubling.

For the adding principle to work (at least withoutresorting to interpolation techniques), a transmittedray needs to have a fixed zenith angle throughout theatmosphere. In a spherical refractive geometry, thisis not the case. Our algorithm circumvents this prob-lem by including only one aerosol layer, and thislayer is used to determine the specific rays for whichthe radiances will be calculated. These are 2n rayscorresponding to the discrete (Gaussian quadrature)angles in the aerosol layer, two extra rays cor-responding to the viewing angle (at the sounder),and the downward ray along 53:5° (at the surface).The calculation of the propagation of these rays isthe first step in the algorithm. The next step isthe calculation of the reflectance and transmittancematrix in the aerosol layer, for which we use the stan-dard doubling rules (with thermal emission) to modelthe effects of multiple scattering. In terms of compu-tation time, this is the most expensive step, as itrequires matrix multiplications and calculation of in-verse matrices. The extra two rays are included withzero weight. In the other layers there is no reflec-tance, and, therefore, only the transmittance coeffi-cients along the 2nþ 2 angles are calculated. Oncethese are calculated the discrete radiative transferequations can be applied. Just as in the regular At-mosphit, radiative transfer starts from the top of theatmosphere, with the calculation of nþ 1 downwardradiances. The goal here is the calculation of the(upward) reflectance at the aerosol layer (in the di-rection of the viewing angle) and the (upward) reflec-tance at the surface (in all nþ 1 upward directions).Next, the upward radiance along the nþ 1 upwardrays is calculated. Note that we do not consider ex-pensive adding rules here (which require matrixmultiplications). Because we only have one aerosollayer and because we do not include second-order re-flectance at the surface (this is a reasonable approx-imation; see, e.g., [29]), the radiative transfer can becarried out with simple vector multiplications.

The calculation of the single-scattering coefficientsmentioned above is driven by a Mie scattering code,which furnishes the absorption and scattering coeffi-cients, the phase function, and the asymmetry pa-rameter, given a refractive index and particle radius[40,42]. The computation of these quantities requiresthe iterative calculation of spherical Bessel func-tions, which can be numerically unstable, and, inconsequence, a lot of algorithmic improvements andcodes have been suggested over the years (see, e.g.,[43] and references therein). We have implementedthe algorithm recently proposed in [44], which is fastand accurate for both large and small particles. Thecalculation of the Mie coefficients over particle sizedistributions is achieved by numerical integration


over the single particle size coefficients. Note thatthe Mie solution is only applicable for scattering onspherical particles, an assumption which is very fre-quently made. However, the method outlined aboveis not restricted to spherical particles, and the Miecode we have used could be replaced by a more gen-eral code accounting for different particle shapes(e.g., using T-matrix calculations [45]). For the effi-cient calculation of the Mie phase function alongthe 2nþ 2 different angles, we have implemented thedelta-M method [46], which approximates the phasefunction as the sum of a forward peak and 2n termseries of Legendre polynomials. Doing so has a num-ber of clear advantages, such as automatic conserva-tion of energy and the removal of forward peaks,which can be considered as being transmitted ratherthan scattered. Another advantage is that the Le-gendre moments can be stored in lookup tables,which grow only linearly with the number of streams.The Legendre moments are calculated with theLobatto quadrature [46] and stored together withextinction and absorption coefficients, forwardpeaks, and asymmetry parameters (as a functionof mean radius and wavenumber) in a lookup table.

3. Inverse Model and Examples

Optimal estimation [47] is a Bayesian method for re-trieving a most probable state (here atmosphericproperties) based on a measurement (here an IASIspectrum), the expected measurement error, bestguesses of the target atmospheric properties prior tomeasurement, and their expected variability and cor-relation (covariance matrix). This method has beenwidely applied in retrieving atmospheric trace gases,but to our knowledge it has not been applied for thesimultaneous retrieval of aerosol properties andtrace gases from high-resolution thermal IR spectra.We have implemented optimal estimation for bothaerosol effective radius and concentration (altitude,aerosol type, layer thickness, and size distributionare kept fixed). The programmatic implementationof the optimal estimation method follows that oftrace gases. The two retrieved parameters are trea-ted independently, and derivatives for the aerosolproperties are calculated numerically.

Below we give five example retrievals on IASIspectra (described in the subsections below), whichare representative for different classes of aerosolsthat affect the thermal outgoing IR radiation. Theseare minerals (sand aerosols and volcanic ash), bio-mass burning aerosols, sulfuric acid aerosols, andice particles (e.g., in ice clouds or volcanic plumes).We have omitted liquid water clouds here, as a studyof cloud retrieval is beyond the scope of this paper(see, e.g., [48] and references therein for water cloudretrieval).

The number of streams was set to eight and theaerosol layer was, in each case, taken to be 500mthick. The standard deviation of both aerosol radiusand concentration in the optimal estimation was setto 100%. The fitting range was set between 800 and

1200 cm−1 (for sulfuric acid, the range was reduced to825–1200 cm−1, to give a better fit). The moleculesabsorbing in this spectral range, i.e., O3, H2O (tenpartial columns each), HNO3, CFC-11, and CFC-12(total columns) were retrieved simultaneously. Whennecessary, also SO2 (volcanic plumes) and NH3,HCOOH, and CH3OH (biomass burning plumes)were included. The surface temperature was keptfixed and taken from the EUMETSAT level 2 datafrom the closest clear IASI spectrum. For the firstfour examples, we have used the lognormal particlesize distribution

NðrÞ ¼ N0ffiffiffiffiffiffi2π

pln σgr

exp�−

ln2ðr=rgÞ2ln2σg

�; ð1Þ

with N0 being the total number of particles, rg thegeometric mean radius, and σg the geometric stan-dard deviation. Because absorption and scatteringare proportional to r2, the parameter rg does not re-present the distribution very well. Following [36], wewill determine the effective radius

re ¼Rr3NðrÞdrRr2NðrÞdr ¼ rg expð2:5ln2σgÞ: ð2Þ

Similarly (see, e.g., [49]), we can define an effectivenumber concentration Ne such that Ner2e ¼RNðrÞr2dr. For the lognormal distribution this

reduces to

Ne ¼ N0 expð−3ln2σgÞ: ð3Þ

The geometric standard deviation was set at 2, whichis within the 1.75–2.25 range typically measured forsand aerosols [50], sulfate [51,52], and volcanic ash[53]. This value of 2 μm is also close to what is usedin the OPAC database for most aerosol components[54]. For biomass burning aerosols, we set thegeometric standard deviation to 1:5 μm [55,56].

For the last example, that of ice particles, it is com-mon to use the gamma size distribution. We haveapplied the distribution

NðrÞ ¼ N0ðrebÞð2b−1Þ=bΓðð1 − 2bÞ=bÞ r

ð1−3bÞ=be−r=ðrebÞ; ð4Þ

where re is the effective particle radius and b ¼ 1=9is the effective variance [36,57,58]. The effectivenumber concentration can be calculated as Ne ¼N0ð1 − bÞð1 − 2bÞ.

With the retrieved radius and concentration, twoderivedproperties canbe calculated: the optical depthand themass of the aerosol layer. Themass can be cal-culated by assuming a fixed bulk density ρ andequates to

M ¼ 4π3ρZ

r3NðrÞdr ¼ 4π3ρr3eNe ¼ Me: ð5Þ


This last equation gives another physical meaning tothe effective radius and effective number density.Below we will report both the effective number con-centration and mass per square meter. There are anumber of ways to calculate the optical depth. Oneway is to simply integrate over the extinction coeffi-cient, but for aerosols, this is not a good physical mea-sure, as it does not correspond to any observablequantity and does not take into account the thermalemission or effects of multiple scattering. We will re-port here the optical depth at 10 μm calculated as

τ ¼ − lnðLout=LinÞ; ð6ÞwhereLin is the radiance (at nadir) before it enters theaerosol layer and Lout is the radiance leavingthe aerosol layer (here without taking into accountmolecular absorption).

4. Examples

The retrievals of the IASI spectra are summarized inFig. 1 and Table 1. The dark blue spectra are the ob-served IASI spectra, and the dark red spectra are thefitted spectra. The residue or the difference betweenthese two is plotted in dark green. A forward simula-tion of the spectrum using all the retrieved param-eters, but omitting the aerosol layer, is shown in lightred. This is the spectrum that would have been ob-served if the aerosol layer were not present. The dif-ference between this spectrum and the observedspectrum is shown in light green. This differenceshows the overall extinction due to the aerosol layer.

A. Sand

MODIS and CALIPSO data on 21 June 2009 show alarge dust plume off the West Coast of Mauritaniaand Senegal at an altitude of around 5km. An IASIspectrum observed over the plume is shown inFig. 1(a). This spectrum was selected because of itslarge V-shaped absorption feature between 800and 1200 cm−1. This V shape is very characteristic forsand aerosols [25,59]. Very few measurements havebeen made of refractive indices of suspended sandaerosols [60,61]. For the retrieval, we used the refrac-tive indices from [62], which were measured fromtransported Saharan sand collected at Barbados.The shape of the extinction coefficient is only veryweakly dependent on the effective radius in the spec-tral range from 800 to 1200 cm−1, and we were able toconfirm this when trying to fit the effective radius.The retrieved radius heavily depends upon the initialvalue of the radius, with good fits obtained within therange 1:5–2:5 μm. For the sample fit in Fig. 1(a), wehave, therefore, fixed the radius at 2 μm, which iscommonly observed for such dust plumes [21,23].The fit is able to capture the overall absorption fea-ture due to aerosols. With an RMS residue of 0:5K,which is around twice the IASI spectral noise, somefiner features of the observed spectrum remain unac-counted for. We have fitted several other spectra fromdust storms over the North Atlantic, with varying de-grees of success. Generally, either the left or the right

slope of the V shape fits well, but not both. This is notsurprising, given the fact that the quality of the fit islargely determined by which refractive indices wereused. Different measurements of such refractive in-dices show very large scatter, pointing to a large de-pendency on mineral content, but also to largeexperimental errors. Note that the Mie approxima-tion is usually very good for mineral aerosols [63].

B. Volcanic Ash

We illustrate the retrieval of volcanic ash character-istics on a spectrum from the plume of the eruptionof Chaitén on 3 May 2009 [64]. The spectrum [seeFig. 1(b)] was observed north of the volcano over aplume that was estimated to be at an altitude around12km, based on HYSPLIT trajectories [30]. The ashin the plume was found, via laboratory measure-ments, to be ryolitic (obsidian) [64], and we were ableto confirm this with a good fit using the refractive in-dices of obsidian reported in [65]. For the retrieval,both radius and concentration were included in thefit. An effective radius re of 1:49 μmwas found in veryclose agreement with the re of 1:5 μm found by [30]using a lookup approach. Especially the 1070–1200 cm−1 region is very sensitive to the effectiveradius, and the retrieval always converged to re ¼1:49 μm, no matter which starting point was used.A lower bound on the altitude layer was found tobe 8km by doing the retrieval several times with aplume altitude varying from 5 to 15km. Below8km the fit yields a much larger residue; above thatthe residues are comparable. Also, in line with theconclusions of [30], we found that fits using other(volcanic) minerals, such as basalt and andesite,yielded much larger residues or simply diverged.

C. Sulfuric Acid Droplets

Following the large eruption of Kasatochi on 7 and 8August 2008, where between 1–2Tg SO2 was emitted[31,66], observable concentrations of liquid H2SO4droplets were expected and confirmed by [31]. Theirmethod consisted in making the average of all spec-tra of the Northern Hemisphere one day before theeruption, and comparing that to the average of allspectra with a detectable SO2 amount one monthafter the eruption. The difference shows a clearH2SO4 signature, characterized by a bumpy and in-creasing absorption feature between 800 and1200 cm−1. Here we present a retrieval of one spec-trum, observed one month after the eruption. Thespectrum was chosen within the aged plume of theKasatochi eruption (confirmed by the SO2 contentof about 6DU). For the retrieval, an H2SO4 aerosollayer was put at an altitude of 14km (correspondingto a temperature of 215K). Both concentration andradius (especially the 1100–1200 cm−1 region is sen-sitive to the radius [67]) were retrieved and found tobe independent of the starting point. Refractiveindices were taken from [68]. Because the refractiveindices have a large dependence on the H2SO4 con-tent of the droplets, retrievals were carried out with


refractive indices ranging from 45% to 80% H2SO4 inweight. The best fit, as shown in Fig. 1(c), was ob-tained for the 80% H2SO4 composition.

D. Biomass Burning

To illustrate the retrieval of organic biomass burningaerosols, we have selected a spectrum measured

Fig. 1. (Color online) Example fits for sand, volcanic ash, sulfuric acid, biomass, and ice aerosols. See text for details.


above a smoke plume over the Mediterranean Basinon 25 May 2007. The smoke plume was emitted dur-ing the intense Greek fires [69–71] of August 2007.CALIPSO data from that day show maximal back-scatter coefficients around 2km over that plume [71].The spectrum and fit are shown in Fig. 1(d). The re-fractive indices used for the fit were taken from [72],who reported measured refractive indices of burningaerosols fromamixedweed sample.The extinction re-sembles that of the smooth step function, with weakattenuation in the 800–1000 cm−1 range and thestrongest (and almost uniform) absorption in the1000–1200 cm−1 range. The location of the transitionstep isweakly dependent on the radius.Good fitswereobtained for effective radii varying from 0.25 to0:5 μm, with the precise value dependent on the apriori value of the radius. In Fig. 1(d), the fit is shownfor re ¼ 0:45 μm, which corresponds to the fit with thesmallest residue.

E. Ice

A spectrum on 23 July 2009 observed above an icecloud over the South Atlantic is shown in Fig. 1(e).Apart from an overall drop in the baseline, a differ-ential absorption can be observed between 800 and1000 cm−1, which is characteristic for ice particles.The effect can especially be large for particles smal-ler than 5 μm [73]. For the fit, we used the refractiveindices from [74] at 210K, corresponding to an alti-tude of 11km, which is the altitude estimated by theoperational IASI L2 cloud data [75]. The retrieval issensitive to particle size and converged to 9:33 μm, nomatter which starting point was used. The density isquite low, but at 9:33 μm the particle sizes are of thesame order as the considered wavelengths, makingthem very efficient scatterers. Note that in contrastto the previous examples, assuming spherical parti-cles is not a very good approximation for modeling iceclouds in thermal IR [76].

5. Caveats and Errors

We have presented a forward and inverse code formodeling outgoing radiation with an atmospherecontaining one aerosol layer. The two main retrievedparameters are aerosol radius and concentration, butthese two cannot always be retrieved simultaneously(i.e., the retrieved parameters might depend stronglyon the a priori information). In general, whether thisis possible will depend strongly on the consideredaerosol species. For the examples we have consideredhere, we found volcanic ash and ice particles to be themost sensitive to radius. It is not surprising that the

radius is hard to retrieve for small particles, such asthose found in biomass burning plumes. For verysmall particles, the optical properties in the IR canbe reasonably modeled with the Rayleigh approxima-tion [36,77]. In this case, the absorption dominatesover scattering, and the absorption coefficient is pro-portional to Ner3eν (ν is the wavenumber), so that dif-ferent pairs of Ne and re can account for a givenabsorption optical depth. The product of numberdensity and particle volume is a useful property toretrieve, but for small particles, it is sensible to fixthe radius. For large sand aerosols, one way to re-trieve both properties independently would be to con-sider additional channels toward the shortwave(2000 cm−1).

Another issue that will affect the sensitivity of theretrieved parameters is the aerosol load. There aretwo extreme cases: for low aerosol loadings and smalloptical depth, the instrumental sensitivity will likelybe too small to see the subtle spectral features of theaerosol absorption. Conversely, when the opticaldepth is very large, the spectra tend to flatten outand look like a gray body radiator, again limiting thepossibility of simultaneous retrieval of radius andconcentration. The examples we have presented hererepresent moderate aerosol loadings and are, there-fore, well suited for carrying out retrievals. The ex-amples (except for the ice cloud spectrum) werealso cloud free, according to the EUMETSAT level2 cloud data [75]. It is important to note that cloudsand heavy aerosol layers in the lower troposphere(below the main aerosol layer) can lead to additionalerrors, which are difficult to quantify.

As presented here, the retrieval algorithm does notexplicitly account for retrieval of aerosol type and al-titude, but this can be achieved by running the retrie-val several times with varying input composition andaltitude and subsequent comparison of the residues.It should be stressed that, in general, the retrieval ofaltitude will be competing with that of concentrationand radius. When the absorption is uniform over aspectral range, putting the aerosol layer at a colderaltitude will likely give a good fit, but will result inthe concentration or radius to be underestimated.Such an approach will, indeed, only make sense if thethree parameters have different sensitivities to dif-ferent parts of the spectral range. Again, taking abroader spectral range is likely to help the retrieval.

A fourth competing property is that of the surfacetemperature. As most aerosols will affect the base-line over a broad spectral range, it is much betterto take the temperature from a nearby clear pixel

Table 1. Summary of Assumed and Retrieved (Boldface) Parameters (Densities ρ Taken from [78,79])

Altitude (km) reðμmÞ Neðm−2Þ Mðgm−2Þ τ ρðgcm−3Þ Ref. Ind.

Sand 5 2.00 2.62e + 10 2.18 0.17 2.5 [62]Volcanic 12 1.49 2.27e + 10 0.89 0.14 2.8 [65]Biomass 2 0.45 3.93e + 12 1.98 0.03 1.3 [72]H2SO4 14 0.56 1.14e + 11 0.14 0.02 1.7 [68]Ice 11 9.33 2.64e + 09 8.26 0.33 0.9 [74]


than trying to fit the temperature. However, for aero-sols that do not affect the baseline in some spectralrange, there is no reason why temperature cannot befitted along with the other properties. For the exam-ples we have presented, only the biomass burningaerosols are transparent in some range of the spec-trum (800–850 cm−1), and, in this case, we have ver-ified that it was possible to retrieve both surfacetemperature and aerosol concentration.

In order to assess the order of magnitude of errorsassociated with the spectral noise and other fixedparameters in the retrieval, we have repeated thevolcanic ash retrieval several times, each time per-turbing a different parameter. The resulting errors inradius, concentration, mass, and optical depth aresummarized in Table 2. The perturbed param-eters are (i) the spectrum (1σ of extra instrumentalnoise was added), (ii) the refractive indices (in-creased with 10% over the whole spectral range),(iii) surface temperature (increased by 2K), (iv) alti-tude (decreased corresponding to a temperature in-crease of 10K), and (v) size distribution (standarddeviation was increased by 10%). As noted before,particle shape has a negligible impact on the spec-trum in the IR. We found that, for the volcanic ashretrieval, the instrumental noise has little or no in-fluence in the retrieval of aerosols, as the randomnoise is small compared to the aerosol absorptionand does not change the overall absorption shapein the spectrum. The optical depth, as we definedit here, is least affected by the perturbations and onlysensitive to the surface temperature. The largest er-rors, in general, are due to uncertainties in particlesize distribution (larger wings of the distributionlead rapidly to a larger effective radius).

Although errors in the refractive indices do not leadto disproportional errors in the retrieved parameters,it is clear that sucherrors canbeespecially large in thecase of sand aerosols, for which, in general, it is noteasy to achieve a good fit due to differences in the re-fractive indices used for the forward calculation andthe refractive indices corresponding to the observedaerosols. Measured refractive indices of sand containlarge scatter and are extremely sensitive to composi-tion [59,60]. For future studies on sand aerosol, itwould be extremely valuable to have independentnew measurements of the refractive indices of sandwith different compositions and publicly available re-fractive indices of pure mineralogical components.

IASI has been developed and built under theresponsibility of the Centre National d’Etudes

Spatiales (CNES, France). It is flown onboard theMetOp satellites as part of the EUMETSAT PolarSystem. The IASI L1 data are received through theEUMETCastnear real-timedatadistribution service.C. Clerbaux is grateful to the CNES for scientific col-laboration and financial support. The research inBelgium was funded by the National Fund for Scien-tific Research (F.R.S.-FNRS) (M.I.S. nF.4511.08), theBelgian State Federal Office for Scientific, TechnicalandCultural Affairs, and theEuropeanSpaceAgency(ESA-Prodex arrangements C90-327). Financialsupport by the “Actions de Recherche Concertées”(Communauté Française de Belgique) is also ac-knowledged. L. Clarisse is very grateful to Q. Liufor discussions on the advanced doubling–addingmethod.

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Table 2. Simulated Errors in Retrieved Parameters for Volcanic Ash Spectrum

reðμmÞ Neðm−2Þ Mðgm−2Þ τ

Particle shape (nonspherical) Very small [63]IASI noise (þ1σ) <1% <1% <1% <1%Surface temperature (þ2K) 10% 8% 23% 20%Altitude (þ10K) <1% 13% 9% 5%Size distribution (σ ¼ 2:2 μm) 26% 45% 42% <1%Refractive index þ10%) 3% 2% 7% <1%


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Retrieving radius, concentration, optical depth, and mass of different types of aerosols from high-resolution infrared nadir spectra