Introduction
The discussion about the possibility to detect and characterize atmospheric
aerosols in the thermal infrared spectral range is relatively recent
and very active, since
infrared satellite observations enable an extensive spatial–temporal
characterization of aerosol optical and microphysical properties, and
quantitative description of their transport processes (e.g.
). However, proficient exploitation of
satellite infrared data requires a good knowledge of the spectral properties
of the most common atmospheric aerosols in this spectral range.
In this context, there can be several sources of uncertainty, such as poor characterization of complex refractive indices, the particle
size distribution, the aerosol's vertical profile on the spatial scale of
interest , and the degree of robustness of radiative
transfer, which should be able to consistently reproduce the radiative
impact of aerosols.
All these issues have been already investigated, leading
to a significant improvement of the physics of radiative transfer and
retrievals in the presence of aerosols e.g., as far as both
accuracy and computational performances are concerned. The two aspects have
the same importance, since both the quality and the amount of infrared data
produced by satellite-based sensors have steeply increased in the last
decade. In this respect, before going through the exploration of aerosol
properties, we describe the radiative transfer model we use in this work,
called σ-IASI-as, focusing on the scheme implemented to
simulate the radiative effect of clouds and aerosols, and on its
computational performance. The model, which is conceived mainly for research
purposes, has been already successfully used in the simultaneous retrieval of
several polluting gaseous species together with the thermodynamic state of
surface and atmosphere with the Infrared Atmospheric Sounder
Interferometer (IASI; ). In this work, instead, we perform a
simulation exercise in which we use the model to reproduce the observed
radiance in the presence of aerosols and clouds. The ability of the model to
deal with a range of atmospheric aerosols, with arbitrary vertical profiles
and dimensional distributions, is one of the main advances with respect
to the former σ-IASI model , which was conceived to
work only with simplified single-layer cloud models, in which the cloud was
characterized by its own temperature and emissivity. The new version of the
model, instead, works with the spectrally variable complex refractive index
of aerosols and clouds (water/ice), performing ab initio Mie calculations,
and adopting robust and well-validated schemes for the fast parameterization
of multiple scattering effects.
In this respect, it is fair to point out that
σ-IASI-as computes the extinction due to aerosol/cloud particles
adopting a strategy which is similar to that of other fast radiative transfer
models, such as the Radiative Transfer for TOVs model (RTTOV; ),
massively employed in data assimilation in the context of numerical weather
prediction (NWP). The approach we will describe is significantly faster than the
exact multiple scattering effects calculation implemented in line-by-line
models such as LBLRTM , and the Discrete Ordinates Radiative
Transfer Program for a Multi-Layered Plane-Parallel Medium (DisORT;
). The scheme used in σ-IASI-as is a compromise between
a fully parameterized one and an exact multiple scattering algorithm.
Furthermore, like its predecessor σ-IASI, σ-IASI-as is not
customized for any particular instrument, and works in a pseudo-monochromatic
fashion: in this way the model is of more general application than the
majority of predictor-based radiative transfer models. Also, since the model
ingests the optical properties of several aerosol and cloud types, it can be
applied to simulate infrared observations including all the variability of
such properties.
The present study completes the picture of the model
capabilities, which have been already investigated in depth in
as far as gases are concerned. As stated before, an
incomplete or inaccurate characterization of the atmospheric aerosol could be
the greatest source of uncertainty in radiative transfer calculations. Thus,
for this validation exercise, we have fruitfully employed the aerosol
microphysical properties (i.e. dimensional distribution and concentration)
derived during the Chemistry-Aerosol Mediterranean Experiment (ChArMEx;
), special observation period (SOP) 1a. One of the components of
SOP1 was the ADRIMED field campaign (Aerosol Direct Radiative Impact on the
regional climate in the MEDiterranean region). Here, we exploit the
measurements acquired on board the ATR-42 aircraft, operated by SAFIRE
(Service des Avions Francais Instruments pour la Recherche en Environnement,
http://www.safire.fr/) over Lampedusa, on four different days between
June and July 2013. Indeed, natural aerosols largely affect the radiative
transfer in the Mediterranean Basin atmosphere in a direct or indirect way
e.g.. At the
location and the time of the observations we have chosen, the dominant
contribution has been found to be that of the desert dust emissions from
various Northwest African soils. For our purposes, we have used the
dimensional properties and profiles derived from ATR-42 flights (details in
), and produced radiative transfer calculations with
different sets of complex refractive indices, according to the soil from
which aerosol has been observed to come: such an exercise is devoted to
quantify the accuracy of our knowledge of desert dust optical properties in
the thermal infrared. This will be made on the full IASI spectral coverage
(3.62 to 15.5 µm) in order to see whether or not input refractive indices
fail to represent the observed radiance in spectral regions dominated either
from aerosol absorption or scattering.
The paper is organized as follows: in
Sect. we will describe the σ-IASI-as model, with the
aerosol extinction calculation scheme implemented therein; Sect.
is dedicated to show the IASI data and related ancillary information used in
this case study. In Sect. we will conduct an in-depth
analysis of results, with a particular focus on refractive indices
consistency. Conclusions are drawn in the final section.
The σ-IASI-as radiative transfer model
The radiative transfer model we work with is called σ-IASI-as. As
already stated, it is an advanced version of the σ-IASI model
as far as cloud and aerosol treatment is concerned. The
model is tailored to compute the Earth/atmosphere-emitted radiance in the
wave number interval 100–3000 cm-1, and works within the assumption of a
plane-parallel geometry. Moreover, although most of the forthcoming discussion
is based on the hypothesis of an observer located at the top of the
atmosphere, the model is capable of computing both ground-based and
satellite-based radiance.
The radiative transfer equation solved in
σ-IASI-as takes the following form, in which the total observed
radiance R(σ) at wave number σ is expressed as the sum of four
terms:
R(σ)=Rsurf(σ)+R↑(σ)+R↓(σ)+Rsun(σ)=ϵgB(Tg)τ0+∫0+∞B(T)∂τ∂zdz+(ϵg-1)τ0∫0+∞B(T)∂τ*f∂zdz+1-ϵgπτ′μsIs,
where the first term represents the radiance emitted directly
by the surface, with a skin temperature Tg, an emissivity
ϵg(σ), and attenuated by the atmosphere along the observation
path, whose total transmittance is τ0(σ). The second term is the
up-welling radiance, namely the radiance emitted directly from the atmosphere
along the slant path in the viewing direction, integrated along the path
itself. The third term expresses the down-welling radiance, which is the
radiance contribution emitted from the atmosphere towards surface,
back-reflected from it and that reaches the observer; according to the type
of surface, the term τ*f is computed in different ways; if the surface
is a Lambertian diffuser, τ*f is the diffuse transmittance, that can
be calculated as the transmittance function at a suitable angle,
corresponding to an effective zenith angle θr=52.96 degrees
. In the case of a specular reflector (e.g. sea surface),
instead, τ*f will be the nadir transmittance, exponentially rescaled
by the cosine of the viewing zenith angle (VZA). Finally, the last term
represents the solar radiance Is/π reflected back from surface and then
reaching the observer, with τ′ the two-way transmittance along the
Sun–surface–observer path, and μs the cosine of the solar zenith angle. The
solar irradiance Is implemented in the model is borrowed by the Kurucz
model . Formally speaking, τ′ is the only term that
undergoes a modification, according to the geometry (nadir-looking or
zenith-looking): in this second case, the two-way transmittance simplifies to
one-way, along the observation path. The other terms remain unaltered.
In a
non-scattering atmosphere, Eq. () can be used to estimate the
observed radiance both in clear sky and in the presence of aerosol/cloud
particles. Under the further limitation that the instrumental field of view
(FOV) is not affected by cloud shadows or other weird inhomogeneities, the
total observed radiance will be the sum of two terms, one of clear sky and
the other that is related to the overcast fraction α of the FOV
:
R(σ)=(1-α)RC(σ)+αRcld(σ).
The term RC(σ) in Eq. () is
expressed by
Eq. (), and is the clear-sky radiance. The cloudy radiance
Rcld(σ), at first order, is formally calculated in the same
way;
the transmittance is calculated
taking into account not only gas optical depth, but also cloud
opacity. At second order, an additional term ought to be considered to
account for possible sunglint effects caused by reflection of solar
radiation by cloud top or sea surface. Such a contribution becomes important
only at wave numbers greater than 2000 cm-1, while it is negligible in
the thermal infrared. However, since in the analysis we conduct we
preliminarily discard spectra affected by such effects, the calculation of
this term is not implemented in σ-IASI-as.
The radiative transfer
Eq. () is solved in the model in its discrete form. The
atmosphere is divided into a number of discrete layers, each one assumed to be
characterized by a homogeneous temperature and composition. Hence, one can
write two expressions for RC and Rcld:
RC=ϵgB(Tg)τ0,C+∑j=1NLB(Tj)(τC,j-τC,j-1)+(ϵg-1)τ0,C∑j=1NLB(Tj)(τC,j*f-τC,j-1*f)+1-ϵgπτC′μsIs,
Rcld=ϵgB(Tg)τ0,cld+∑j=1NLB(Tj)(τcld,j-τcld,j-1)+(ϵg-1)τ0,N∑j=1NLB(Tj)(τcld,j*f-τcld,j-1*f)+1-ϵgπτcld′μsIs.
The dependence on the wave number σ is omitted for brevity. In both
equations, τj is the total transmittance from the top of the jth
layer to ∞, and τj-1 is the total transmittance from the
bottom of the jth layer to ∞, leading τcld,L=1.
For clarification see Fig. . In addition, Tj is
the equivalent average temperature of the bulk of the jth layer. In
σ-IASI-as, the atmospheric grid is made up of 60 layers, whose pressure
boundaries are fixed, and span the interval 1013–0.005 mbar: hence, the
number of layers for which transmittance is computed depends strictly on
surface pressure, taking into account the air mass above the observed target.
In , it is demonstrated that NL=60 is enough to confine the
radiative transfer error well below 0.1 K throughout the whole IASI spectral
range.
Layering scheme of the atmosphere, with layer numbering and the definition of
transmittances.
Transmittances in the clear-sky and cloudy radiances are defined
according to the considered absorbers. In σ-IASI-as it is assumed that
the clear-sky transmittance is affected both by gases and aerosol particles,
namely assuming that aerosols are uniformly distributed on the entire FOV.
The extinction due to water and ice clouds, together with that due to gases
and other aerosols, yield the transmittance in the cloudy portion α of
the FOV. Looking at Fig. , it is possible to write explicitly
the expressions for the transmittance τj in the cases of clear and
cloudy sky:
τC,j=∏i=1jexp(-χC,i)=∏i=1jexp-χgas,i+χaer,i
τcld,j=τC,j⋅∏i=1jexp-χcld,i,
where χC,i is single-layer total optical depth in the absence of
clouds, and χaer,i and χcld,i are the optical depth of
aerosol and clouds in the ith layer.
Calling ηj the single-layer
transmittance of the jth layer, and using the definitions provided so far,
we can better express the relation between ηj and the sum of the
optical depths of all the atmospheric absorbers in the layer itself as
ηj(σ)=τj-1(σ)τj(σ)=exp-∑i=1Sχi,j(σ)
with S being the number of atmospheric absorbers, and χi,j(σ) the monochromatic optical
depth of the ith species from the top of the jth layer to its bottom at wave number σ.
The simplest way to express χi,j(σ) is to recall the well-known Lambert–Beer law
χi,j(σ)=qi,jki,j(σ)Δhj,
where qi,j is the concentration of the ith species in the jth
layer, ki,j(σ) is the absorption coefficient for that species at
wave number σ, and Δhj=Hj/cosθ denotes the path
length, along the θ-direction of observation, from the bottom of the
jth layer to its top, Hj being the thickness of the jth layer.
The
way in which the term ki,j(σ) is treated in the model depends
strictly on the type of absorber: since a different physics regulates the
interaction of radiation with gases and particles, the estimation of
ki,j(σ) relies on two distinct approaches, one for gases and one
for aerosols and clouds, which are described in what follows.
Gas optical depths
The σ-IASI-as code calculation of gas optical depths is based on a
scheme which avoids the direct manipulation of spectroscopy. In
contrast, the approach followed is that of working in a pseudo-monochromatic
context, in which transmittances are calculated on an equally spaced wave
number grid. To do this, the σ-IASI-as architecture embodies a wide
look-up table where – for each layer, atmospheric species, and wave numbers –
optical depths are pre-computed and stored. Then, optical depths are rescaled
with air pressure and temperature: on the one hand, pressure is fixed by the
different atmospheric layers; on the other hand, the dependence on
temperature is parameterized: purely monochromatic optical depths are
generated using the version 12.2 of LBLRTM , equipped with
the spectral library AER v_3.2 with the continuum model MT-CKD v_2.5.2
using as input parameters the reference temperature profile
US Standard Atmosphere 1962 , with the associated
reference gases concentrations. Without varying them, optical depths are
re-calculated using the same temperature profile translated by eight evenly
spaced temperature values ranging from -40 to +40 K. This choice is adequate
to take into account the usual temperature variations that occur in the
Earth's atmosphere.
Once optical depths have been calculated, for each individual
species (except for water vapour), layer, and wave number, the behaviour of
the optical depth with temperature is parameterized by a second-order
polynomial:
χi,jσ=qi,jc0,i,jσ+c1,i,jσΔTj+c2,i,jσ(ΔTj)2,
where ΔTj is the difference between the reference temperature profile and the actual
equivalent temperature of the jth layer, and qi,j is the concentration of the ith gas in the
jth layer. Water vapour optical depth, instead, is modelled using a slightly different
parameterization: in order to take into account the effects depending on gas concentration, such as
self-broadening of spectral lines, the polynomial expression includes a further coefficient
c3,1,jσ , which multiplies the water vapour concentration:
χ1,jσ=q1,jc0,1,jσ+c1,1,jσΔTj+c2,1,jσ(ΔTj)2+c3,1,jσq1,j,
where the water vapour species is denoted by i=1, holding the species
ordering of the HITRAN database . In order to reduce the
dimensionality of calculations with respect to line-by-line models, the
coefficients of the polynomial parameterizations are binned on a pseudo-
monochromatic spectral grid with Δσ=10-2 cm-1.
The
present version of σ-IASI-as can compute the observed radiance with a
user-specified temperature and emissivity, atmospheric profiles of
temperature, and specific profiles for H2O, HDO, O3, CO2, CO,
CH4, N2O, HNO3, SO2, NH3, OCS, and CF4. Besides these gases,
the code computes the optical depth of an ensemble of other gases whose
concentration is kept fixed (mixed gases). This set includes the major
species N2 and O2, which are considered through their continuum.
Moreover, the model considers other trace gases, such as NO, NO2, HCl,
HCN, CH3Cl, OH, H2CO, and C2H2. Their reference vertical profiles
are fixed according to the US Standard atmosphere . Mixed
gases include also the heavy CFC molecules CCl4, CFC-11, CFC-12, and
HCFC-22. Their column abundance is scaled consistently with the most recent
World Data Centre for Greenhouse Gases report . It is fair to
point out that, among the available fast radiative transfer models,
σ-IASI-as is the more flexible in terms of number of gaseous species
whose concentration can be tuned. Furthermore, the analytical
parameterization expressed by Eqs. ()–()
enables the possibility to compute, within the model, the derivatives of the
radiance (namely, Jacobians) with respect to all the atmospheric and surface
parameters mentioned above (for details see ).
Aerosol and cloud optical depths
The σ-IASI-as model works with a physically based method also to compute the extinction due to
aerosol particles and clouds. To treat them, the model exploits an ab initio approach: the code
embodies Mie routines which are called iteratively within the calculation
of single-layer transmittances. The results of Mie calculations are manipulated according to the
scheme described in for calculating effective aerosol and cloud optical depths.
According to this, the aerosol optical depth at a given wave number σ and a given layer j is
computed as follows:
χaer,j(σ)=qaer,jkaer,j(σ)Δhj,
where qaer,j is the aerosol concentration [particles cm-3], and Δhj has the same meaning as in Eq. (), while kaer,j(σ) is
the equivalent aerosol extinction per particle [km-1].
In the scheme of
, kaer,j(σ) includes the effects of three
processes: emission, absorption, and scattering. These three
processes are in fact quite coupled, and there is no way to exactly take them into
account at the same time. Despite this, there are several possible
approximations that can be used to do this: the scheme implemented in
σ-IASI-as estimates multiple scattering effects in the longwave,
yielding an equivalent aerosol optical depth. In this sense, this is
comparable to an “absorption approach”.
Hence, at a given wave number
σ and for the layer jth (subsequently omitted for conciseness), the
term kaer,j(σ) can be expressed as
kaer(σ)=βext(σ)[(1-ω(σ))+b(σ)ω(σ)],
where βext(σ) is the extinction efficiency per particle [km-1], ω(σ)
is the single-scattering albedo, and b(σ) is the mean fraction of radiation scattered in the
upward direction. In the hypothesis that the incoming radiation is isotropic, b(σ) is the
integral average in the upward direction (first integral) of the cumulative function (second integral)
of the scattering phase function
b(σ)=12∫01dμ∫-10dμ′P(μ,μ′,σ),
where μ=cos(θ) and P(μ,μ′,σ) is the scattering phase function. In this way,
the code bypasses the exact calculation of multiple scattering effects, by parameterizing them in a
semi-analytical way. The Henyey–Greenstein phase function is implemented, since it is
the most common one for atmospheric applications. It is not directly computed in its original integral
form, in order not to introduce an unsustainable computational load. Instead, b(σ) is expressed
as a third-order polynomial of the asymmetry parameter g(σ):
b(σ)=1-∑l=14algl-1(σ),
where the four coefficients of the linear combination are fixed: a1=0.5,
a2=0.3738, a3=0.0076, and a4=0.1186.
All the quantities involved in the aerosol/cloud extinction calculations
depend on the microphysical properties of particles, namely their dimensional
distribution and their complex refractive indices. The σ-IASI-as can
compute aerosol and cloud extinction due to an arbitrary number of aerosols
and a superposition of a user-defined number of log-normal modes, which are
computed within the code on the basis of user-defined average radius,
standard deviation, cut-offs, and concentration on 100 points. Calculations
are performed on a wave number grid with Δσ=15 cm-1, which
is fine enough to correctly reproduce the absorption features of all the
atmospheric aerosols. Results are subsequently interpolated on the wave
number grid used to compute gas optical depths.
The σ-IASI-as code has a
built-in routine that includes the complex refractive indices of the most
common atmospheric aerosols, borrowed from existing databases
, and of water ice , and water
vapour for cloud extinction calculations. The aerosols handled by the code
are listed in Table . For these aerosols, the code can compute
also radiance Jacobians with respect to their concentration.
Code performance and potentialities
The computational performance of the code varies according to the complexity
of cloud and aerosol vertical profiles, the number of log-normal modes
involved, and due to the fact that Jacobians are computed or not. A clear-sky
single IASI spectrum (8461 channels) needs 1.0 s to be computed on a modern
CPU. This time increases by ∼ 0.1 s for each log-normal aerosol/cloud
mode; this is comparable to other fast models such as RTTOV
. The calculation of a full IASI spectrum with all the
Jacobians (with respect to surface temperature, emissivity, temperature
profile, gas and aerosol/cloud concentrations) requires some ∼ 6.0 s,
which means 0.04 s per 50 spectral channels. The time includes
that required to read the optical depths look-up table.
As already stated,
the way in which the code computes the observed radiance is not dependent on
the instrumental technical characteristics, because of the
pseudo-monochromatic approach, differently from other, common parametric
methods based on predictors. This feature makes the code adaptable to any
kind of instrument which observes in the infrared, simply by changing the
spectral response function in the code, and not its architecture, which is a
unique in the context of fast radiative transfer models.
Another relevant
features of σ-IASI-as is its capability to deal with different surface
types, hence different reflection geometries from surface, both Lambertian
(e.g. on most land surfaces) and specular (e.g. on the sea surface),
without any degradation in code performance.
The radiative transfer
scheme at the basis of σ-IASI-as has been subjected to several
validation steps and applied in many contexts (e.g.
) and is based on robustly assessed
procedures as far as aerosol and cloud extinction estimation is concerned
.
List of the aerosols included in the σ-IASI-as model.
Aerosol types
Water droplets
Ice crystals
NaCl
Sea salta
Hydrophilic aerosol
NH3 droplets
Carbonaceous aerosol
Volcanic dust
H2SO4 dropletsb
Meteoric dust
Quartzc
Hematitec
Desert sandc
Saharan dust
Volcanic ash
Flame soot
Ammonium sulphate
Burning vegetation ash
a Sea salt refractive
indices are available for eight different values of relative humidity (0 to 99 %). b
H2SO4 droplets are available for two temperature values: 215 and 300 K.c Birefringent
materials: each of them has two sets of refractive indices – one for the ordinary light ray and the
other for the extraordinary one. This is convenient if σ-IASI-as is used to simulate
polarized radiances.
Data and methods
IASI data
The analysis that follows consists of a direct comparison between
the radiance simulated using σ-IASI-as, which requires atmospheric
profiles of temperature, gases, and aerosols, together with their spectral
refractive indices, and their dimensional characterization, and the
collocated observations in the thermal infrared from the IASI interferometer.
As already stated, besides the explanation of σ-IASI-as architecture,
the goal of this study is to quantify the sensitivity of IASI data with
respect to the geographical origin of desert dust aerosol. Its optical
properties have been already explored in many studies, such as
.
Location of the IASI fields of view analysed in this work. From left to right: 22
June, 28 June, and 3 July 2013. Red circles correspond to those acquired from IASI on board of MetOp-A,
blue from IASI on board of MetOp-B. Filled footprints are those that have been effectively
used, while those unfilled have not been exploited because of strong sunglint contamination in the
shortwave.
have estimated the information content of IASI spectra
with respect to desert dust aerosol properties and mineralogical composition.
Here we further investigate this topic, assessing new, independently measured
optical parameters of desert dust.
IASI was developed
in France by CNES and is flying on board the MetOp platforms. The two MetOp
satellites (A and B) are part of the EUMETSAT European Polar System (EPS).
IASI was conceived for meteorological studies; hence, its main aim is to
provide suitable information on the thermodynamic status of atmosphere
(temperature and water vapour profiles) and surface. The instrument works in
the whole spectral interval from 645 to 2760 cm-1, with a sampling
interval Δσ equal to 0.25 cm-1 and an effective apodized
resolution of 0.5 cm-1, which results in 8461 spectral channels for each
single spectrum. Since MetOp platforms are on a polar orbit, IASI works as a
cross-track scanner, with 30 effective fields of regard (FOR) per scan,
spanning an angle range of ±48.33 degrees on both sides of the nadir. Each
FOR consists of a 2 × 2 matrix of so-called instantaneous fields of view
(IFOVs); each IFOV has a diameter of 14.65 mrad, corresponding to a ground
resolution of 12 km at nadir at a satellite altitude of 819 km. The IFOV
matrix is centred on the viewing direction. Hence, at nadir, the FOR of the
four IASI pixels projects at the ground a square area of
∼ 50 × 50 km. A more exhaustive description of IASI, the mission objectives and its
achievements can be found in . IASI data were downloaded from
the EUMETSAT data centre.
For the present study, we delimited a geographic
box around the island of Lampedusa, defined by the latitude range
[35.0∘ N, 36.0∘ N] and longitude [12.0∘ E, 13.2∘ E]. Morning IASI
soundings were collected in this area on 22 and 28 June, and 3 July 2013.
These three days are characterized by different atmospheric desert dust loads
and vertical profile shapes: this is the ideal case to characterize the
effectiveness of aerosol parameters in different conditions. The geographic
area of interest, together with the exact location of IASI soundings, are
reported in Fig. . Only clear-sky IFOVs are indicated; they are
pre-selected through a scene analysis software based on a cumulative
discriminant analysis approach . A further selection is made
discarding all the spectra affected by sunglint contamination, which are not
useful to characterize the accuracy of aerosol spectral properties at wave
lengths shorter than 5 µm. This pre-filtering process yielded a total of
12 IASI IFOVs (out of 22 clear sky) actually used in this work, for the case
of 22 June; 15 IFOVs (out of 23) for the 28 June case; and, finally, 18 IFOVs for
the case of 3 July. The acquisition times are,
respectively, 08:45, 08:21, and 08:17 UTC.
From top to bottom: log-normal modes used to fit the observed size distributions on
22 June (top), 28 June (middle) and 3 July (bottom) for each altitude range.
Mode 1
Mode 2
Mode 3
Mode 4
Origin
Ntot
500.00
900.00
5.00
0.40
140–1400 m
R0 (µm)
0.045
0.070
0.140
0.675
Southern Morocco
σ0
1.50
1.50
1.55
2.80
Ntot
500.00
900.00
8.00
1.20
1400–3600 m
R0 (µm)
0.039
0.070
0.185
0.650
Southern Morocco
σ0
1.28
1.50
1.55
2.18
Ntot
130.00
100.00
20.00
3.20
3600–5800 m
R0 (µm)
0.039
0.070
0.200
0.775
Southern Algeria
σ0
1.28
1.50
1.55
2.18
Mode 1
Mode 2
Mode 3
Mode 4
Mode 5
Origin
Ntot
800.00
200.00
10.00
0.35
0.01
150–1000 m
R0 (µm)
0.045
0.065
0.150
0.250
3.940
Tunisia
σ0
1.30
1.50
1.35
2.40
2.00
Ntot
500.00
400.00
20.00
1.70
1000–5400 m
R0 (µm)
0.039
0.070
0.175
0.500
Tunisia
σ0
1.25
1.50
1.55
2.70
Mode 1
Mode 2
Mode 3
Mode 4
Origin
Ntot
300.00
500.00
20.00
2.50
1600–3500 m
R0 (µm)
0.035
0.055
0.165
0.450
Southern Morocco
σ0
1.25
1.50
1.55
2.50
Ntot
100.00
160.00
15.00
2.00
3500–4800 m
R0 (µm)
0.040
0.060
0.185
0.800
Tunisia
σ0
1.25
1.50
1.55
1.80
From left to right: ECMWF spatial–temporal average profiles for temperature (left), water
vapour (middle), and ozone (right) on the days involved in this analysis. Together with the
temperature profiles, ECMWF average surface temperatures for each day are also indicated by markers.
Radiative transfer input data and methods
In relation to simulations, the radiative transfer model relies on proper
input data for surface properties, atmospheric state, and aerosol vertical
distributions. As far as emissivity is concerned, since all the IASI IFOVs
that we examine involve sea surface, emissivity is computed according to
Masuda's emissivity model . We have developed a look-up table
with sea surface emissivity over the IASI spectral range and resolution. The
emissivity has been pre-computed for VZA values spanning the interval from
0 to 50∘ (in steps of 1∘) and for an average wind speed
of 5 m s-1.
Measured dimensional distributions of dust aerosol by the instruments on board the ATR-42 at
different altitudes: (a) 22 June, (b) 28 June, and (c) 3 July 2013. In the first case, there are three
distinct dust layers, instead of two. The extra layer (blue curve) is that closest to the surface:
on that day, dust aerosol is more abundant than in the other two cases.
Complex refractive indices used as input in the radiative transfer calculations. Left plot:
real part of (black curve) and of the three indices (red, green, and blue curves)
derived by ; right plot: same as left, but for the imaginary part.
Sample IASI spectrum (black) acquired on 22 June (higher dust aerosol load) compared with
radiative transfer simulations in absence (blue) and presence (green) of dust aerosol. Refractive
indices are from .
For surface temperature and atmospheric status, we used the
analyses provided by the European Centre for Medium-Range Weather Forecasts
(ECMWF) collocated with IASI measurements. ECMWF provides data at the four
canonical hours (00:00, 06:00, 12:00, and 18:00 UTC) on a spatial grid mesh of
0.125∘ × 0.125∘. Since we do not retrieve atmospheric and
surface parameters, they are estimated as the temporal average between the
two consecutive ECMWF analyses closest to IASI soundings (mostly acquired
around 09:00 UTC), namely those at 06:00 and 12:00 UTC of the three days that we
consider. More specifically, we use ECMWF temperature, water vapour mixing
ratio, and ozone mixing ratio profiles (T, Q, O), and surface temperature Ts.
All these profiles are originally provided for either 91 pressure levels
(until 25 June 2013) or 137 pressure levels (from 26 June 2013). Since the
ECMWF pressure grid is not fixed, depending on surface pressure, all the
atmospheric profiles are preliminarily averaged on the 60-layer pressure grid
of σ-IASI-as. The other gases required as input from the radiative
transfer model are tuned according to the most recent updates of climatology
. The final, average (T, Q, O) input profiles used for the IASI
IFOVs effectively exploited for the three selected dates are shown in Fig. .
The last input required by radiative transfer is the
aerosol vertical profile and dimensional distribution. Such data were
acquired from the Forward Scattering Spectrometer Probe (FSSP-300), the Ultra
High Sensitivity Aerosol Spectrometer (UHSAS),
the two particle counters GRIMM-1 and GRIMM-2, and the scanning mobility
particle sizer (SMPS) on board the ATR-42 aircraft . These
instruments measure the particle concentration in different size ranges; for
this radiative closure experiment, we use the particle concentration
measurements. Their diameters lie in the interval 0.05–30 µm at any
altitude. The dimensional distributions have been fitted with a variable
number (typically 4 or 5) of log-normal modes. Different sets of log-normal
modes have been found to be representative of the size distributions observed
at different altitudes. The observed particle size distributions on the
selected days are represented in Fig. , while the retrieved
log-normal modes to reproduce them are summarized in Table .
This ensemble of input data has been used to perform a double run of
radiative transfer simulations: the first one is done including aerosol
vertical profiles, the second one without them (clear-sky simulations). The
double simulation is aimed to better characterize the radiative impact of
aerosol with respect to IASI real observations. Results reported in
show that, on the selected dates, the observed
atmospheric aerosols have a variable geographic origin: that observed over
Lampedusa on 22 June has two distinct origins, according to the altitude,
coming from southern Morocco for altitudes below 1500 m, and from
southern Algeria for altitudes above 3500 m. On 28 June, instead, the
observed aerosol comes entirely from Tunisia, while for the case of 3 July,
the lower layer (below 3000 m) comes from southern Morocco, the upper from
Tunisia. All this information is summarized in Table , and
has been properly considered in the choice of refractive indices used for
radiative transfer simulations. Indeed, to probe the sensitivity of IASI data
to the heterogeneous origin of dust, two sets of simulations in the presence of
aerosol have been performed: the first using the indices borrowed from
, and included in the OPAC database; the second using sets of
refractive indices which account for the parent soils of the observed
aerosol, initially derived in and more
extensively in . In the first study, extinction spectra in
the 2–16 µm range were measured in situ (T=293 K, RH < 2 %) for
poly-dispersed pure dust aerosols generated from natural parent soils in
Tunisia, Niger, and the Gobi desert. Such data were used in combination with
particle size distributions to calculate the complex refractive index of each
dust sample. All the sets of refractive indices chosen for the present work
are plotted in Fig. . They are averaged on a 15 cm-1 spectral
grid, the same used by σ-IASI-as to compute aerosol extinction.
It
is fair to stress that a more complex simulation should also consider the
presence of polluted aerosol particles in the boundary layer, and their
mixing with dust particles. However, we have neglected such effects, since
IASI radiances (as shown e.g. in ) have usually a very
limited sensitivity to aerosols in the atmospheric layers where mixing takes
place; also, in cases like this, where the observed target is sea surface,
the thermal contrast between surface and the boundary layer is limited to
2–4 K (see Fig. ), and cuts down the sensitivity to aerosols in
the boundary layer. Moreover, the concentration of polluting particles is at
its seasonal minimum, because of the absence of sources such as biomass
burning. As a concluding remark on this aspect, in it is
shown that pollution particles have only a small influence on the optical
properties of the dust plumes over the western Mediterranean, and that other
mixing effects, like coating of polluting species on dust particles, have no
relevant effect on their optical behaviour. Hence, in these conditions, the
direct comparison between simulations and IASI observations in the thermal
infrared, relying on well-characterized dimensional distributions, can be a
way of validating the goodness of dust indices, and to probe the sensitivity
of satellite-based, hyperspectral, infrared Earth observations to the
geographical origin of aerosol. We perform a direct comparison between the
spectral residuals obtained with the two sets of indices, both spectrum by
spectrum, and on the day-by-day average, in order to see whether or not
significant, systematic discrepancies occur.
Results
Single spectra results
Here we first show sample spectra with the aim to give an idea of the
accuracy of the model with respect to the single spectrum simulation, and to
provide a first, qualitative feedback about the difference between clear-sky
calculations, those in presence of dust aerosol, and IASI spectra. Figures and represent two single spectra (one for 22 June and
the second acquired on 3 July) among those (see Fig. ) selected
for the analysis. The two plots show the most prominent spectral signatures
that affect IASI radiances; among them, the ν2 and ν3 CO2
absorption bands, centred at 667 and 2385 cm-1, the O3 band
at 1040 cm-1, and the wide H2O ν2 absorption band centred at
1590 cm-1, and many other minor features in the three atmospheric
windows 780–980 cm-1, 1070–1200 cm-1, and beyond 2440 cm-1. A
more detailed focus on these regions is given in the same figures, which is
important since the aerosol extinction effect is heavily manifested therein,
with differences in brightness temperature units that can be as large as 5 K for the case of 22 June. At first glance, we can see that the radiance
computed with σ-IASI-as including the dust aerosol impact reproduces
fairly the radiance as observed by IASI. The extinction due to the behaviour
of complex refractive index at both sides of the O3 band is well
manifested both in the computed and observed radiances. It is proper to point
out that both the surface temperature and the water vapour columnar amount
provided by ECMWF and used by the model have been slightly tuned in order to
better match IASI observations, which is necessary to correct some well-known
biases typical of ECMWF re-analyses, and because we do not perform any
retrieval of the true state vector.
As a general remark, we
observe that, in both cases, the simulated radiance is closer to that
observed by IASI in the longwave atmospheric windows than in the shortwave.
Part of this inconsistency is certainly due to the fact that σ-IASI-as
does not reproduce non-LTE effects, which is of interest especially for the ν3
CO2 absorption band. This leads to biases that can be as large as 8 K in
the core of the CO2 ν3 band. However, since aerosol extinction
effects are dominant in the atmospheric windows, it is likely that
the observed residuals at wave numbers >2400 cm-1 are due to a poor
characterization of refractive indices in that spectral range.
As Fig. , with a second spectrum acquired on 3 July (low aerosol load).
Residuals analysis: refractive index variability
The conclusion expressed above is reinforced if we perform a more
detailed analysis on spectral residuals on the average of the IASI soundings
for each of the days we have considered. In this discussion, we
examine also in detail the difference between the radiance simulated with the
two sets of refractive indices cited before.
Figure shows the
whole, average observed and simulated spectra (with and without dust
aerosol), a close-up of the spectral windows, and the differences between
observed and calculated radiances computed over the 12 selected spectra for
the first case (22 June). The first three plots seem to confirm what has been
already observed on the single spectra, while new insights are revealed by
residuals (last panel). Having not retrieved in any way atmospheric and
surface parameters from spectra, some of the structures seen in the residuals
are those due to biases within ECMWF analyses – among them, the evident misfit
of the CO2 band at 667, 720, and 750 cm-1
identified in , and related to the fact that ECMWF analyses
actually tend to overestimate stratopause temperature and to underestimate
the tropopause and high troposphere temperature, both by 2–4 K. An average
bias of 1 K is also manifested in the spectral region related to water
vapour absorption, which again is related to the uncertainties typical of
ECMWF water vapour profiles (10–20 %). Moreover, CH4 and HDO/H2O
isotopic ratio profiles are not tuned with respect to IASI observations, and
this results in the residual spectral features around 1300 and beyond 2600 cm-1 . The last major bias, due to radiative transfer
limitations, is the large misfit in the ν3 band of CO2, caused by the
already cited negligence of non-LTE effects in the code.
Top panel: IASI average spectrum (black) of the 12 soundings acquired on 22 June, compared to
the average of the computed spectra: clear sky (blue), with dust aerosol, using
refractive indices (red), and indices (green). Middle panels: close-up of the
spectra in the IASI longwave (left) and shortwave (right) atmospheric windows. Bottom panel:
residuals computed in the three cases.
As far as aerosols
are concerned, their average effect is to adjust the spectral slope in the
atmospheric windows, reducing the difference with the observed IASI average
spectrum. In fact, there are significant discrepancies between the two sets of
refractive indices: if we use the indices by , they
generate an inconsistency between the atmospheric windows at the two sides of
the ozone band, with an evident misfit around 950 cm-1, and a continuous
residual as large as 1.5 K in the interval 1070–1200 cm-1. Such
residuals are largely suppressed in the interval 780–980 cm-1 by
adopting the more recently derived indices of , while
they do not solve the issues in the interval 1070–1200 cm-1. The same
conclusion can be drawn for the shortwave spectral window, where the same
continuous slope appears using both refractive index sets. To summarize,
in this first case it seems that the choice of using refractive indices like
those of , which account for the geographic source of
the aerosol, solves part of the inconsistencies which are observed with a standard set of indices. In this sense, IASI data reflect the specific
provenance of the observed aerosol. Nevertheless, some spurious features in
the residuals still remain using both sets, suggesting that further efforts
are needed to characterize the specific refractive indices in the thermal
infrared.
Similar effects appear in the comparison between observations and
simulations for the two other days, which are reported in Figs.
and . In these two cases, the most abundant aerosol is that
coming from Tunisia. In the case of 28 June, we observe that the new indices
of suppress the slope introduced using the indices of
, which is as large as 1.5 K, and attenuate the misfit in
the shortwave, while inconsistencies still hold between the two atmospheric
windows at the sides of the ozone band. Analogous observations can be done in
the case of 3 July, both in the longwave and in the shortwave windows. In
particular, in this last case it can be seen that the newer indices partially
improve the average residuals in the shortwave, reducing them below 1 K,
modifying also the slope in this spectral interval. This is a peculiarity of
this last case study but, overall, the results shown so far seem to suggest
that the inconsistencies observed in the shortwave can be ascribed to a
still poor characterization of dust refractive indices in this spectral
region.
Summary of root mean square differences and related standard deviations computed using the
two sets of refractive indices in the three selected scenarios. Data are computed averaging the
residuals on a spectral grid with a sampling of 15 cm-1, the same used by σ-IASI-as to
compute aerosol extinction, in order to suppress most of the variability due to gas spectral lines,
retaining only the variability due to aerosol extinction.
Ref. index
RMSD ± SD (K)
780–980 cm-1
1070–1200 cm-1
2440–2760 cm-1
22 June
0.134 ± 0.460
0.288 ± 0.160
0.165 ± 0.760
0.039 ± 0.130
0.161 ± 0.380
0.224 ± 0.780
28 June
0.175 ± 0.400
0.082 ± 0.260
0.338 ± 0.160
0.077 ± 0.080
0.225 ± 0.220
0.156 ± 0.160
3 July
0.057 ± 0.140
0.085 ± 0.090
0.117 ± 0.380
0.019 ± 0.055
0.185 ± 0.130
0.081 ± 0.370
IASI av. noise (K)
0.123
0.137
0.577
A quantitative comparison between residuals is summarized in
Table . We stress that the standard deviations in Table are computed on spectral channels, and not on single
spectra. To clarify, if N is the number of spectral points (at a resolution
of 15 cm-1) in the spectral window we are analysing, and R(σi),
i={1,…,N} the average, spectrally binned radiance vector, where the
average is made over the M spectra we consider, the standard deviation is
computed as
1N-1∑i=1NR(σi)-R¯212,
where R¯ is the average spectral residual in that particular spectral
window. In this way, the standard deviation brings information about the
spurious variability introduced by an incorrect characterization of the
refractive index, and can be compared with IASI radiometric noise.
Top and bottom panels: same as middle and bottom panels of Fig. , for the case of 28 June (medium
aerosol load). Average residuals are computed over 15 IASI soundings.
In
the shortwave spectral window (last column), the two indices exhibit the
same standard deviations, which are above the IASI noise
only in the case of 22 June. This shows the need to better
characterize dust aerosol spectral properties in this region for aerosols
coming from Algeria and Morocco, while the input indices for Tunisian aerosol
give satisfactory results. In the longwave, instead, the indices of
steadily reduce the average RMSD and standard
deviations below the IASI average noise level in all three cases in the
spectral window 780–980 cm-1. Instead, in the atmospheric window
1070–1200 cm-1, the average residual is better for these indices than
those computed using the indices of in the case of 22
June, while for the cases where the dominant aerosol is that from Tunisia,
the new indices yield double average residuals, and generally larger standard
deviations. To summarize, in the longwave the refractive indices for the
aerosol coming from Algeria and Morocco seem to perform better than those of
, which is the opposite of what we observe in the shortwave.
As Fig. , for the case of 3 July (low aerosol load). Average residuals are
computed over 18 IASI soundings.
Residuals analysis: dimensional distribution variability
A more complete picture of the sensitivity of IASI data with respect to dust
aerosol properties can be achieved by quantifying the effect of the
dimensional distribution of particles on the observed radiance. They should
be compared to the magnitude of the effects caused by the heterogeneity of
refractive indices. Moreover, there is a number of studies
e.g. in which the relation between aerosol particle
size and its radiative properties is investigated; hence this kind of
analysis is necessary to assess the possibility to exploit infrared satellite
data to characterize desert dust particles.
To this end, the IASI
data sets of 22 and 28 June have been considered, while the data of 3 July have
been discarded because of the limited radiative impact of aerosol.
Simulations have been performed using the refractive indices of
, and varying the average radius of the log-normal
distributions at different altitudes – those reported in Table .
Let us denote by R the modified particle radius; to preserve the dust
total column, we simply rescale the radius by the ratio R/R0, and the
particle concentration by R0/R3, where R0 is that
reported in Table . For this exercise, the radius has been
increased and decreased by 25 %, hence it is R/R0=1.25 or R/R0=0.75.
From the top. Left: average residuals (computed as in the previous cases) varying the average
particle radii of 25 % in the altitude range 140–1400 m (blue and red), compared with the residuals
obtained used the radii reported in Table (black, green in previous figures). Middle
plot: same as top, but changing the radii in the altitude range 1400–3600 m. Bottom: same as before,
but changing the radii in the interval 3600–5800 m. Right plots: same as left, but in the spectral interval
1950–2600 cm-1.
As Fig. , but for the case of 28 June.
Figure reports the residuals in the same three spectral
windows as before, computed varying the average radii in each altitude
interval for the case of 22 June. Here it is nicely evidenced that a change
of 25 % of the average dimension of dust particles in the lower layers, below
1400 m, yields a change in the observed radiance which is confined below IASI
radiometric noise (<0.1 K throughout all the spectral windows). A similar
conclusion can be drawn if the particle radius is modified in the altitude
range 1400–3600 m. Instead, simulations show that the dimensional properties
of the upper layers of desert dust have a major influence on the observed
radiance. In this case, we can see that an increase/decrease of 25 % of
particle average radii is reflected in a 0.1 K radiance decrease/increase in
the spectral windows in the longwave, and a 0.5 K radiance decrease/increase
in the shortwave. This is consistent with the fact that, in the shortwave,
aerosol extinction is essentially due to scattering efficiency, which
increases with respect to absorption efficiency as particles become smaller.
Similar effects are observed in the case of 28 June (see Fig. ): again, a change of 25 % of the average particle size in the
lower atmospheric layers does not produce any net observable effect above
IASI radiometric noise, neither in the longwave, nor in the shortwave
portion of the IASI spectrum. The same change applied to dust particles in the
altitude range 1000–5400 m conveys changes in the observed radiance which are
around 0.7 K in the shortwave windows, and up to 0.7 K in the longwave. In
this case the impact on the observed radiance is greater than in the
previous case, since the thickness of the aerosol distribution in which we
modify the particle average size is greater. However, in both cases there is
an inconsistency between the residuals' behaviour in the shortwave
atmospheric windows, and that in the longwave, particularly in the interval
1070–1200 cm-1. These effects are well above the IASI radiometric noise
within the selected atmospheric windows, and demonstrate, again, the need
of a better characterization of the radiative behaviour of desert dust
aerosols, especially in the shortwave portion of IASI spectrum, where major
residuals are found.
Regarding the radiative transfer scheme, taking into
account that the shortwave slope decreases with the aerosol load,
we can argue that the multiple scattering scheme adopted by σ-IASI-as
is actually effective in reproducing the observed radiance, at least with the
aerosol loads we have observed in these cases. In the longwave atmospheric
windows, where scattering effects are of second order with respect to pure
absorption, simulations demonstrate that IASI is capable to capture salient
characteristics of the desert dust well above the radiometric noise average
level. In this case, the “absorption approach” adopted by the radiative
transfer model is effective.
Conclusions
In this work we have provided an in-depth description of the new
σ-IASI-as radiative transfer model, and used it to probe the goodness
of the characterization of the microphysical properties and refractive
indices of desert dust aerosol over Lampedusa in the context of the ChArMEx
experiment, in three cases with a variable aerosol load.
The radiative
transfer model we have presented is based on a pseudo-monochromatic approach
as far as gas optical depth calculation is concerned, while it exploits
ab initio Mie calculations to calculate aerosol extinction. This makes the model
extremely feasible with respect to the aerosol properties, as has been
shown in this paper. Moreover, the model has not been tailored
to any particular instrument, thus it is immediately available, in
particular, for the next generation of hyper-spectral sensors (e.g. IASI-NG,
MTG-IRS) whose operativity is scheduled in the next 10 years.
The
calculations we have performed have exploited the characterization of
dimensional distributions of desert dust particles over Lampedusa performed
by dedicated instruments. The high level of detail of these observations has
enabled us to assess, in particular, the effectiveness of refractive indices
used to model desert dust aerosol extinction, by comparing different models.
Overall, we find that the dimensional distributions derived from ChArMEx
observations show a fair consistency between observations and
calculations in the thermal infrared wavelengths. In contrast, the
discrepancies are significant in the shortwave portion of the IASI spectrum
where, in any case, also other major effects occur (e.g. non-LTE). The
discrepancies observed between IASI radiances and calculated spectra in this
region evidence the need to better characterize dust extinction in the
mid-near infrared (as already stated in other contexts,
e.g. ), especially in cases where the observed aerosol has
heterogeneous geographical source. In the thermal infrared, instead, the
observed residuals are generally better, and compatible with the
uncertainties that affect the other radiative transfer input data (ECMWF
atmospheric profiles and surface properties). However, better consistency has
to be achieved between different spectral intervals.
This validation study is
a further step toward the possibility to retrieve directly from IASI
radiances the microphysical properties of clouds and aerosols, namely their
vertical distribution and/or particle dimensional distributions, thanks to
the fact that the radiative transfer model we have elaborated has the
ability to compute analytical or semi-analytical Jacobians of the radiance
also with respect to particle concentration.