AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-1889-2016A sensitivity study on the retrieval of aerosol vertical profiles using the oxygen A-bandColosimoSanto FedeleNatrajVijaySanderStanley P.StutzJochenhttps://orcid.org/0000-0001-6368-7629Department of Atmospheric and Oceanic Sciences, UCLA, Los Angeles, CA, USAJet Propulsion Laboratory, Caltech, Pasadena, CA, USAS. F. Colosimo (fedele@atmos.ucla.edu)29April201694188919051September201516November201513April201614April2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/1889/2016/amt-9-1889-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/1889/2016/amt-9-1889-2016.pdf
Atmospheric absorption in the O2 A-band
(12 950–13 200 cm-1) offers a unique opportunity to retrieve
aerosol extinction profiles from space-borne measurements due to the large
dynamic range of optical thickness in that spectral region. Absorptions in
strong O2 lines are saturated; therefore, any radiance measured in
these lines originates from scattering in the upper part of the atmosphere.
Outside of O2 lines, or in weak lines, the atmospheric column
absorption is small, and light penetrates to lower atmospheric layers,
allowing for the quantification of aerosols and other scatterers near the
surface.
While the principle of aerosol profile retrieval using O2
A-band absorption from space is well-known, a thorough quantification
of the information content, i.e., the amount of vertical profile
information that can be obtained, and the dependence of the
information content on the spectral resolution of the measurements,
has not been thoroughly conducted. Here, we use the linearized vector
radiative transfer model VLIDORT to perform spectrally resolved
simulations of atmospheric radiation in the O2 A-band for
four different aerosol extinction profile scenarios: urban
(urban–rural areas), highly polluted (megacity areas with large
aerosol extinction), elevated layer (identifying elevated plumes, for example for
biomass burning) and low extinction (representative of small aerosol
extinction, such as vegetated, marine and arctic areas).
The high-resolution radiances emerging from the top of the atmosphere measurements
are degraded to different spectral resolutions, simulating spectrometers
with different resolving powers. We use optimal estimation theory to
quantify the information content in the aerosol profile retrieval with
respect to different aerosol parameters and instrument spectral
resolutions.
The simulations show that better spectral resolution generally leads
to an increase in the total amount of information that can be
retrieved, with the number of degrees of freedom (DoF) varying between
0.34–2.01 at low resolution (5 cm-1) to 3.43–5.38 at high
resolution (0.05 cm-1) among all the different
cases. A particularly strong improvement was found in the retrieval of
tropospheric aerosol extinction profiles in the lowest 5 km of the
atmosphere. At high spectral resolutions (0.05 cm-1),
1.18–1.48 and 1.31–1.96 DoF can be obtained in the lower (0–2 km)
and middle (2–5 km) troposphere, respectively, for the different
cases. Consequently, a separation of lower and mid tropospheric
aerosols is possible, implying the feasibility of identification of
elevated biomass burning aerosol plumes (elevated layer scenario). We
find that a higher single scattering albedo (SSA) allows for the
retrieval of more aerosol information. However, the dependence on SSA
is weaker at higher spectral resolutions.
The vegetation (surface albedo 0.3), marine (surface albedo 0.05)
and arctic (surface albedo 0.9) cases show that the dependence of
DoF on the surface albedo decreases with higher resolution. At low
resolution (5 cm-1), the DoF are 1.19 for the marine case,
0.73 for the vegetation case and 0.34 for the arctic case, but
increase considerably at 0.05 cm-1 resolution to 3.84 (marine)
and 3.43 (both vegetation and arctic), showing an improvement of a factor
of 10 for the arctic case. Vegetation and arctic case also show the same
DoF at higher resolution, showing that an increase of albedo beyond a
certain value, i.e., 0.3 in our case, does not lead to a larger information content.
The simulations also reveal a moderate dependence of information content on the
integration time of the measurements, i.e., the noise of the spectra.
However, our results indicate that a larger increase in DoF is obtained by an
increase in spectral resolution despite lower signal-to-noise ratios.
Introduction
Atmospheric aerosols play a central role in the Earth's radiative
budget. Together with various greenhouse gases, aerosols represent the
most significant anthropogenic forcers responsible for climate
change. However, uncertainties about the origin and composition of
aerosol particles, their size distribution, concentration, spatial and
temporal variability, make climate change prediction challenging. In
order to quantify the influence of aerosols on the Earth's climate and
to better validate climate models, information about their global
abundance, properties and height distribution are needed.
Aerosol vertical and horizontal distribution significantly affects
total radiative forcing in the Earth's troposphere and
stratosphere. Aerosol particles transported by wind over long
distances in the free troposphere affect climate on larger spatial
scales than aerosols close to the surface. Those confined in the
boundary layer (BL), have shorter lifetimes and have a more local impact on
climate and air quality. Hence, knowledge of the vertical and
horizontal distribution is crucial to understanding the impact of
aerosols
. Similarly,
the climate impact through indirect effects, e.g., aerosol altering
cloud microphysical properties, also depends strongly on the altitude
of the aerosol .
To fully understand the impact of aerosols on a global scale, the use
of passive satellite remote sensing observations has been shown to be
extremely useful. Algorithms for aerosol and cloud retrieval,
e.g. over land
using MISR (Multi-angle Imaging SpectroRadiometer) data ,
and over the ocean
using the POLDER (Polarization and Directionality of the Earth's Reflectances)
and MERIS (Medium Resolution Imaging Spectrometer)
data, have been successfully developed in the last decade.
Many of these instruments include a window around the spectrally unresolved
O2 A-band.
Studies have shown how satellite measurements can be used to retrieve
aerosol and cloud properties using the absorption and continuum part
of the oxygen A-band .
Multispectral high-resolution radiance and polarization measurements in the absorption spectrum of
molecular O2 have been successfully used to retrieve the
vertical distribution of aerosols and clouds .
O2 A-band data from the SCIAMACHY (Scanning Imaging Absorption
Spectrometer for Atmospheric Chartography)
instrument has shown that the retrieval of aerosol vertical
distribution depends strongly on aerosol optical properties (single
scattering albedo and phase function especially) and surface
parameters .
demonstrated that neglecting
polarization in the forward modeling of O2 A-band backscatter
measurements from space-based instruments, can affect the retrieval
precision in different ways for different atmospheric scenarios. They
investigated errors resulting from ignoring polarization in retrievals
for varying geometry, surface reflectance and aerosol loading, using
simulated OCO (Orbiting Carbon Observatory) data.
showed that the
information content for both aerosols and trace gases can be improved
by adding off-nadir viewing angles in hyperspectral measurements. They
also investigated the dependence of the aerosol information content
for different instrument specifications in multi-angle
retrievals. Furthermore, over vegetated areas, chlorophyll fluorescence
provides an additional contribution to the signal in the oxygen A-band
. Investigation of this phenomenon
demonstrates that the signal from atmospheric
scattering is affected by this type of emission, and that neglecting
it can cause systematic biases in the retrieval of aerosol parameters
such as layer height and optical thickness, or surface properties such
as surface pressure and albedo.
used a combination of
oxygen A- and B-band SCIAMACHY data to infer a complete vertical
distribution of aerosols for a specific area in Kanpur
(India).
Recently, pointed out that the height and optical depth of aerosol layers can be
properly retrieved from satellites using the O2 A-band only if
the layers are close to the free troposphere. Their study shows that,
while satellite observations can provide good information on aerosol
plumes close to the free troposphere, information on boundary layer
aerosols is still limited.
A concept study by ,
investigated the role of instrument spectral resolution and noise
on aerosol profile retrievals from O2 A-band measurements,
using a forward operator based on look-up tables. They varied instrument
resolution, signal-to-noise ratio, and spectral sampling, and performed
optical depth and layer height retrievals for different aerosol types.
Their results indicate that the retrieval generally benefits from improved
spectral resolution, and strongly depends on the signal-to-noise level.
The aim of this work is to evaluate the amount of vertical information
on aerosol extinction profiles that can be extracted from
satellite-based remote sensing measurements of the O2
A-band. Of particular interest is the impact of improvements in
spectral resolution on the aerosol profile information content. To
this end, we perform simulations of high resolution O2 A-band
spectra as observed by a satellite instrument, and use optimal
estimation theory to determine the amount of vertical information with
respect to aerosol profile retrievals, and its dependence on spectral
resolution and noise. Since our primary focus is the amount of aerosol
information, we do not consider the effect of chlorophyll fluorescence
in this work. Methods to quantify the chlorophyll fluorescence have
been developed , and we assume that its
contribution can be independently assessed and corrected. By
performing a detailed calculation and comparison of the information
content retrieval for different altitude ranges (as opposed to the
total columnar value that is typically reported), we investigate, for
a variety of scenarios, if the tropospheric information can be
isolated and evaluated.
The paper is organized as follows: Sect. 2 provides an overview of the
atmospheric model and the a priori assumptions, along with
a description of the radiative transfer (RT) calculation and the
convolution for different instrument specifications. A description of
the signal-to-noise model is also given for a better understanding of
the results. Section 3 describes the theory behind the information
content analysis and defines the quantities involved in the
determination of the various retrieval metrics. Section 4 explains how
the information content tests were performed, and compares the results
for the different cases. Section 5 presents some concluding remarks.
Radiative transfer simulations
In order to test the sensitivity of different instrument
specifications to aerosol retrievals, high resolution O2
A-band spectra simulated with a RT model (described below) were
convolved with Gaussian instrument functions of different spectral
resolutions. To investigate the effects of noise, we used modeled
radiances, assumptions on the instrument response, and different
integration times. These calculations were performed for different
a priori aerosol profiles and covariances. This section will
describe the RT calculation and the convolution.
Atmospheric model
The interpretation of remotely sensed data often requires radiative transfer
models (RTMs) to simulate radiances and their analytic derivatives
(Jacobians) with respect to various atmospheric and surface
parameters.
The four different aerosol profiles used in this study. From the
left: urban (kext= 0.1 km-1, BLH = 1 km,
AOD = 0.5), highly polluted (kext= 0.2 km-1,
BLH = 1 km, AOD = 1), elevated layer
(kext= 0.2 km-1, altitude range = 2–4 km,
AOD = 0.44), low extinction (kext= 0.05 km-1,
BLH = 0.4 km, AOD = 0.12).
A variety of RTMs have been developed over the years. Our choice of RTM
was guided by the need to perform a large number of calculations to
fully resolve the O2 A-band, provide an accurate description
of the full Stokes vector, and have the ability to effectively
calculate Jacobians with respect to various atmospheric parameters.
With the above needs in mind, we chose VLIDORT as the RTM .
The VLIDORT model calculates all of the Stokes parameters in a multi-layer,
multiple scattering medium. The model is also fully linearized;
the advantage of this design is that an entire set of intensities
and weighting functions required for an iteration step in a multi-parameter
atmospheric retrieval can be generated simultaneously and in an analytic
fashion, with a single call to the model, saving a significant amount
of computing time. This feature makes the code ideally suited for retrieval
applications, where computational time and memory requirements may represent
a problem. A complete kernel-model Bidirectional Reflectance
Distribution Function/Matrix (BRDF/M) set is also implemented for
surface reflection and surface property weighting functions, including
surface thermal emission (isotropic). VLIDORT is not internally linked
with any databases for spectral optical properties and/or pre-computed
look-up tables. The code uses a user-defined environment, where
geophysical atmospheric inputs such as vertical profiles (thermal,
trace gases and aerosol), optical parameters (single scattering
albedo, asymmetry parameter, optical thickness, phase function
moments) or spectral properties (cross sections, wavelengths), depend
on the specific application and need to be defined by the user.
For this study, the atmosphere is divided into 60 layers, with a pressure
range between 1001.6 mbar (surface) and 0.3 mbar (top of the atmosphere)
and an altitude range between 0 and 55 km. Vertical temperature and pressure
profiles were based on the US Standard Atmosphere after an
interpolation to a customized altitude grid. The vertical grid spacing has
been set to 0.2 km in the 0–5 km range, 0.5 km in the 5–10 km range,
and 1 km above 10 km altitude. The temperature, pressure, molecular, and
aerosol properties are considered homogeneous within each layer. Molecular
oxygen is the only absorber considered in this study. O2 spectral
line parameters were taken from the HITRAN 2008 database .
Clouds were not considered and scattering is assumed to be caused by
molecules and aerosols only. A Lambertian reflecting surface is assumed, with
a surface albedo a=[0.05,0.1,0.3,0.9], depending on the type of
simulated atmospheric scenario. For a realistic satellite observation
geometry, we assume four solar zenith angle SZA = [30, 45, 60,
75∘] and an instrument looking down at nadir and 30∘
off-nadir, with a fixed relative azimuth angle of 45∘.
In this study, we consider the following four aerosol profiles, each representative
of a different atmospheric scenario, to cover a variety of
atmospheric/surface conditions (Fig. ).Urban –
This profile reflects conditions in
a moderately polluted urban or rural atmosphere, where high levels of
aerosol are constrained to a well-defined boundary layer
. The aerosol profile is thus
constructed assuming a constant a priori aerosol extinction
coefficient kext=0.1km-1 below a fixed boundary
layer height (BLH) of 1 km, with a total aerosol optical depth AOD = 0.5.
Above the BL, extinction decreases exponentially with altitude .
A value of a=0.1 for the surface albedo has been adopted for this test
case.
Highly polluted –
For highly polluted urban areas such as megacities, the
vertical profile shape is similar to that of the urban case, but the
aerosol extinction in the boundary layer is higher
. We thus use the same parameterization as in the
urban case, but with a larger extinction coefficient
kext=0.2km-1 (AOD = 1) below a BLH of 1 km.
Elevated layer –
We considered a typical biomass burning plume
to test the detection of aerosol layers in the free troposphere
.
The aerosol profile is confined to a layer between 2 and 4 km altitude with
constant kext=0.2km-1 (AOD = 0.44). BL aerosol
extinction and height are set to kext=0.1km-1 and 0.4 km,
respectively. Surface albedo is set to a=0.1.
Low extinction –
We also investigated a vegetation, marine and arctic
case with lower aerosol extinction at the surface and a lower BLH.
The arctic case is representative of an environment with pollution,
often referred to as “arctic haze” . We assume
the same kext and BLH for all cases to simulate
the aerosol profile, changing the surface albedo in the respective
environment. Many studies have shown the great variability of
extinction and BLH of aerosol in these environments
. Consequently,
these profiles are only meant to be representative of typical
conditions. For all profiles we adopted a constant value of
kext=0.05km-1 (above the BL, extinction
decreases exponentially with altitude) and a BLH of 0.4 km (AOD = 0.12).
In this case, surface albedo values of a=0.05, a=0.3 and
a=0.9 have been adopted for the marine, vegetation and
a snow-covered arctic scenario respectively.
Example of O2 A-band high resolution spectrum
Δν0= 0.002 cm-1 (black line) convolved with eight
different instrument function FWHM (red line).
It is worth mentioning that the adopted extinction profiles are not
representative of any particular area or location on Earth. They only
represent a plausible parameterization of the upper extinction limit for the
different scenarios. Figure shows the vertical profiles for all
cases. A set of four single scattering albedos ω= [0.8,0.85, 0.9,
0.95], together with an asymmetry parameter g=0.7, were used for this test
to describe the optical properties of the aerosol in the different cases
. The choice
of g=0.7 for the asymmetry factor represents a reasonable value for all of
the scenarios, especially since the results of our analysis do not strongly
depend on g.
High resolution spectra and convolution
VLIDORT was used to generate oxygen A-band high resolution radiance
spectra Lh(ν0) in the spectral range
Δ= 12 950–13 200 cm-1 (757–772 nm), with
a fixed spectral sampling Δν0= 0.002 cm-1
and a number of points given by ν0=Δ/Δν0= 125 000. At this high resolution, all of the
spectral features of the oxygen A-band are fully resolved.
To simulate the measurement of the O2 A-band by a space-borne
spectrometer, Gaussian instrument response functions for different
full widths at half maximum (FWHM) were calculated. The high
resolution O2 spectra were then convolved with this
function. The convolved spectrum was then down-sampled by integrating
the radiances over a grid point of width Δν. We chose to keep
the ratio of FWHM to Δν constant at 5, to avoid under or
oversampling of the spectrum during the convolution. Therefore, when
we increased the instrument resolution (decreasing FWHM), we decreased
Δν accordingly:
FWHMΔν=5.
The Gaussian function representing the instrumental response is
described as
G(ν′,FWHM)=1σ2π⋅exp[-ν′22σ2]
with σ defined as
σ=FWHM22ln2,
where the first factor in Eq. () takes into account
the normalization of the area of the function G(ν′,FWHM) to 1, and ν′ represents the
Gaussian spectral sampling extended to ±5 FWHM for each spectral
channel.
The spectral radiance (the term radiance refers to the directional
spectral intensity in frequency units [Wm-2sr-1cm])
is the result of the convolution of the simulated high resolution
radiance with the instrument spectral response function
(each channel is assumed to be independent of the others).
The convolved radiance Lc is expressed as
Lc(ν)=∑ν′Lh(ν′)G(ν′-ν)Δν,
calculated as the sum over the instrument response function spectral sampling
ν′, of the product of the spectral response function and the high
resolution radiance Lh, expressed over the set of frequencies
ν′. The index ν=Δ/Δν for each instrument channel
represents the chosen sampling grid covering the entire spectral range
Δ. Figure shows an example of the high resolution
spectrum (Δν0= 0.002 cm-1) convolved with different
instrument function FWHM. For high resolution the narrow absorption features
are well described, allowing a better retrieval of aerosol
information.
Noise model
The instrument noise model assumes that measurement noise is dominated
by photon shot noise. Shot noise comes from the statistical
uncertainty of the number of photons sampled by the detector, which
can be described by a Poisson distribution. This assumption is quite
accurate for modern spectrometers. Based on Poisson statistics we
calculate the standard deviation of the radiance measurement
σm as follows:
σm=N
with relative signal-to-noise ratio (SNR) proportional to the square
root of the number of photons N:
SNR=Nσm=N.
The measurement error is thus proportional to the standard deviation
σm (which can be calculated as the square root of N)
with a relative variance σm2. To calculate the
number of photons N falling on the detector, we use the following
expression:
N=Lc(ν)⋅Δt⋅ϵ⋅Ω⋅AEph⋅Δν,
where Eph is the energy of a photon at
13 150 cm-1 (760 nm), Δt is the integration time,
ϵ is the efficiency of the spectrometer/detector
combination, Ω is the solid angle of the field of view of the
instrument and A is the slit surface area. For our calculations we
use Eph=2.6×10-19Jphoton-1,
ϵ=0.05, Ω=0.01 steradians,
A=5.0×10-7m2.
We implemented this noise model without considering any particular
design or instrument specifications. Signal-to-noise ratios (SNR)
outside of the A-band, i.e., at 13 190 cm-1, for a Δt=1 s varies from 5000 at the lowest resolution to 500 at the
highest resolution. These SNR values are higher than those of
current satellite instruments, but in the range of high quality future
instruments. We also analyze the sensitivity of our simulations to
noise by varying the integration time between 0.1 and 5 s. The
sensitivity of the model to the radiance level (which depends on
resolution), and to the integration time, allows us to evaluate the
capability of different sensors to retrieve information for different
aerosol scenarios.
Information content analysis
To determine the information content of an aerosol retrieval based on
the spectroscopic observations in the O2 A-band at different
resolutions, we use an optimal estimation formalism, as described in
detail in Rodgers .
An atmospheric state vector x, containing the parameters of interest,
is related to a measurements vector y, through a forward model
F(x,b), which is a function of x and other
model parameters not retrieved (vector b):
y=F(x,b)+ϵ,
where ϵ is the measurements error vector (e.g., instrument
noise). For atmospheric remote sensing retrievals,
F(x,b) is normally a radiative transfer model.
The solution to the inverse problem is then to use the forward model and the
information from the measurement vector y to construct an estimate of
the atmospheric state. For an inverse problem, the a priori state vector
xa is often used as an initial estimate. xa
is ideally chosen to be close to the true state x, based on existing
knowledge of the atmosphere. Because of the nonlinear nature of the
dependence of the model on the true state vector (and the real measurements),
the solution for a full retrieval of the retrieved state vector,
x^, has to be found using an iterative process, which ends when
an optimal agreement between the model F(x,b) and the
real measurements vector y is reached. Such an approach, however,
requires a good knowledge of the properties of the parameters involved in the
measurements (aerosol micro-physical and optical properties, spectroscopic
parameters, model uncertainties, surface properties) and the relative errors
for every measurement (instrument noise).
Here we are interested in a theoretical and more general quantification of
the aerosol retrieval information content for different scenarios, with no
reference to any specific mission, instrument or location, we perform a
one-step retrieval instead, assuming the a priori parameters to be known and
a linear dependence of the forward model F(x,b) on
the state vector x. The forward model is directly applied to our
first guess (linear regime), assumed to be the true state, for which we
define an a priori uncertainty. We then derive the corresponding information
content, calculated using the optimal estimation method. We remark that this
methodology is not applicable for retrieval with real data, where the high
non-linearity of the physical process and the imperfect knowledge of the a
priori parameters requires the use of an iterative approach.
According to the Bayesian formalism, the linearization of the forward model about the state
vector can be expressed as
y=Kx+ϵ,
where K is the functional derivative matrix, also called
Jacobian, which represents the change in the measurement for a unit
change in the retrieved parameter.
For this study, the vertical aerosol extinction profile represents the
retrieved parameter, x, and the elements of K represent the
derivative of the radiance with respect to the aerosol extinction coefficients,
for every single layer and every single wavenumber. VLIDORT provides analytic
derivatives of the Stokes vector field with respect to any atmospheric or surface
property . The Jacobians required in Eq. () can be
calculated from these weighting functions by the application of the chain rule.
For every scenario, we fix the model parameters, calculating the radiance and the
Jacobian with a single run of the code, before using these quantities for the
estimation analysis.
The sensitivity of the retrieval of a set of parameters, to the measurement,
is expressed by the so-called gain matrix G:
G=(KTSϵ-1K+Sa-1)-1KTSϵ-1.
An important part of optimal estimation analysis is the covariance
matrix, Sa, which quantifies the uncertainty in our
knowledge of the retrieved parameter prior to the measurement, and
the measurement error covariance matrix, Sϵ,
which quantifies the error of the measurement. We calculate
Sϵ according to the instrument noise model described in
Eqs. ()–(). We will discuss the
choice of Sa further below. We assume both matrices to
be diagonal, with all of the off-diagonal elements equal to zero. Every
element of the diagonal thus represents the variance of the respective
element in the a priori parameter and the measurement vector,
respectively. We assume the errors to be uncorrelated.
The sensitivity of the retrieval to the true state of the parameter is
expressed by the Averaging Kernel (AK) matrix A:
A=GK,
which is equal to a unit matrix for an ideal retrieval, when a perfect match between the a priori and the retrieved
parameter is achieved. The trace of A represents the number of degrees of freedom (DoF), which represent the total
number of independent pieces of information derived by the retrieval. Combining Eqs. () and (),
the averaging kernel matrix A can be written as
A=(KTSϵ-1K+Sa-1)-1KTSϵ-1K,
which shows the dependence of DoF on the Sa,
Sϵ and K matrices.
The AK can be seen as a linear representation of the information
content of the retrieval parameters. It represents the relationship of
the retrieval to the true state vector, and ranges between 0 and 1. An
AK close to 1 means that a near-perfect agreement between the
retrieved parameter and the true state has been reached. As it
represents an indication of what the measurement is sensitive to, the
AK can also be interpreted as the vertical resolution of the
retrieval, because it provides important information on the vertical
sensitivity of the instrument, with its maximum corresponding to the
range of altitudes where the maximum sensitivity occurs.
A sample AK is shown in Fig. , where four different regions of
the atmosphere are color-coded. In this case, the region between 2 and 5 km
represents the altitude range with the highest sensitivity to aerosol
extinction.
Based on this formalism we perform three different sensitivity
studies. In the first, we investigate the dependence of the DoF on the observation geometry,
varying both solar and viewing angle. In the second test, we study the effect of instrument
resolution by varying the FWHM and Δν (Eq. ), and scale the
relative noise σm according to the radiances at the
various spectral resolutions; i.e., at higher spectral resolution fewer
photons are sampled per channel, resulting in larger relative errors
and vice versa. It should be noted that the number of channels is also
varied in this test, as discussed previously. In the final test, we keep
the instrument resolution constant and only vary the relative noise by
changing the integration time Δt in Eq. (). This
calculation serves to better understand the contribution of the change
in per-channel noise with resolution to the information content.
Example of averaging kernel vs. altitude. The simulation refers to
a convolved spectrum of spectral resolution
Δν= 0.01 cm-1 and FWHM = 0.05 cm-1 with
a solar zenith angle SZA = 45∘ and an instrument viewing angle of
30∘ off-nadir. The AK are color-coded for different altitude ranges.
On the right, a zoom between 0 and 12 km is shown.
Nadir and 30∘ off-nadir urban scenario total degrees of
freedom for different instrument resolution and four different solar zenith
angle (30, 45, 60, 75∘).
We thus investigate the dependence of the degrees of freedom of the retrieved
aerosol extinction profile on different instrument spectral resolution FWHM
(and relative spectral binning Δν) and different integration time
Δt. We also perform the second and third test for different
a priori aerosol extinction profile errors, expressed through the
Sa matrix in Eq. (), to investigate the
influence of prior knowledge of the aerosol extinction. We assume that the
diagonal elements of Sa, which represent the a priori
extinction profile variances for every single layer, are constant at all
altitudes.
Six different a priori variances are used: 0.7, 0.5, 0.2, 0.1, 0.05, 0.01,
which correspond to a relative error of 83.7, 70.7, 44.7, 31.6, 22.4 and
10.0 % of the a priori aerosol extinction profile, respectively. It
should be noted that performing this sensitivity calculation is very
important, as the total pieces of information of a system strongly depend on
the a priori knowledge of the retrieval parameter, as we will discuss in
the following section.
Results and discussionResolution sensitivity
In order to understand the impact of different instrument resolutions
on the total amount of information available, we perform calculations
for five pairs of values of FWHM and Δν: FWHM = (5, 1,
0.5, 0.1, 0.05) cm-1 and Δν= (1, 0.2, 0.1,
0.02, 0.01) cm-1. Tables showing the numerical values of
the DoF for the complete set of resolutions can be found in the
Supplement.
Using the relationships previously described, we calculate the total
DoF for every pair of these values.
All DoF values are calculated in the spectral range from
13 122 cm-1, where weak or small absorptions occur,
to the main absorption frequencies around 13 140 cm-1.
This range is able to account for almost the entire content of
information available in the main absorption band 13 122–13 170 cm-1,
because of the strong dynamics of the atmospheric absorption in that range.
An initial test with a wider spectral range (not described or shown
in the manuscript), covering the main absorption band, showed that the
DoF were similar (few percent higher) when compared with the range used in this work.
The reason for using a small wavenumber window was the long computational
time needed for all the simulations. The DoF evaluation requires the simulation
of high resolution radiance spectra (and relative Jacobian matrices) at
0.002 cm-1, and the convolution at different resolution for every
single geometry and every single scenarios. In the end, the chosen spectral
range represents a good compromise between the computational time and the
marginal loss of information.
An integration time of Δt= 1 s has been used for the tests.
The a priori knowledge of the aerosol profile is constrained by the
covariance matrices Sa.
Highly polluted scenario total degrees of freedom for different
aerosol extinction profile uncertainties Sa(a).
Degrees of freedom four values of single scattering albedo ω= [0.8,
0.85, 0.9, 0.95], to simulate different type of aerosol, are shown
in (b). SZA = 45∘ and a nadir instrument viewing have
been used.
In order to explore the dependence of DoF on observation geometry, we vary
the SZA (30, 45, 60 and 75∘) and the viewing angle (nadir and
30∘ off-nadir) for a fixed relative azimuth angle (45∘),
implementing the urban scenario for the aerosol and assuming ω= 0.95
as single scattering albedo. Figure shows the improvement of
the information content due to resolution and higher values of SZA, for both
observations. A different solar illumination changes the scattering angle of
the particles, allowing a better information retrieval. At 5 cm-1
resolution, DoF improve from 1.39 (SZA = 30∘) to 1.87
(SZA = 75∘) and from 4.49 to 5.22 (same SZA) at 0.05 cm-1,
for the nadir case. The same change in resolution for the 30∘
off-nadir viewing angle allows a similar improvement in the DoF with a change
from 1.57 (SZA = 30∘) to 1.96 (SZA = 75∘) at lower
resolution and from 4.74 to 5.33 at 0.05 cm-1 resolution, for the same
solar angles. As expected, the off-nadir viewing angle shows higher absolute
DoF due to the longer light path.
We investigate the effect of different Sa on the total DoF for the
highly polluted aerosol scenario, with ω= 0.85 and a nadir looking
geometry with a SZA = 45∘. Figure a shows that increasing
the resolution increases the amount of information available for the aerosol
extinction profile retrieval. This is true for every a priori value
of the diagonal elements of Sa, i.e., the a priori
aerosol extinction profile relative errors.
A change in the resolution of 2 orders of magnitude (i.e., FWHM from 5
to 0.05 cm-1) leads to a three times (or more) increase in the DoF
for all of the covariance values, showing the importance of fully resolving
the narrow features of the band. On the other hand, the DoF decreases
as the a priori knowledge (smaller relative error) of the retrieval
parameter increases. This is due to the nature of optimal estimation:
a better a priori knowledge of the parameter to be retrieved reduces the amount of
information that can be obtained from the measurement.
While the extreme values of Sa (aerosol relative errors of
83.7 and 10.0 % respectively) are not very realistic, we implement them
in this test to take into account a wider set of possible aerosol conditions.
For the rest of the tests, we choose a fixed value of
Sa= 0.2 (44.7 % relative error), which represents
a reasonable choice of average aerosol uncertainties for the different
profiles.
We also analyze the impact of different single scattering albedos on
the DoF for the same scenario (Fig. b). At
low spectral resolutions the DoF increases with an increase in
ω. This is due to the larger scattered radiance from the
aerosol at higher ω, which increases the signal, as well as the
contrast to the surface. The DoF increase from a ω of 0.8 to
0.95 is a factor of 1.76 at low resolution. The DoF also increases
with resolution (lower values of Δν and FWHM) for all
ω. Interestingly, however, the effect of the single scattering
albedo is significantly reduced at higher spectral resolutions. The DoF
improvement from a ω of 0.8 to 0.95 is only a factor of
1.25. It thus appears that resolving spectral features decreases the
dependence of the retrieval on ω. This reduction is likely due
to the fact that the retrieval at high spectral resolution extracts
information based on the O2 band shape, rather than the
radiances alone. It should be noted that increasing ω further
does not considerably change the results of the tests, as can already
be seen in Fig. b, where the dependence of DoF on
ω decreases with increasing ω.
For the rest of this study we will use a fixed viewing geometry of 30∘
off-nadir and SZA = 45∘, with ω= 0.95.
With ω, Sa and the observation geometry fixed,
we can now analyze the dependence of the DoF on spectral resolution
for the various aerosol profiles (Fig. ).
In the urban and highly polluted scenarios, the DoF increases from 1.7 and 2
at 5 cm-1 resolution to 4.9–5.4 at 0.05 cm-1 resolution
respectively. The DoF improvement with spectral resolution is of a factor of 2.8
for the urban case and of 2.7 for the high polluted case. In general, the high polluted
case has a 10–15 % higher DoF due to its higher BL aerosol extinction.
DoF comparison for the different aerosol scenarios.
Sa= 0.2 (44.7 % relative error) and ω= 0.95
for the a priori aerosol extinction profile uncertainty and single
scattering albedo are assumed. SZA = 45∘ and a 30∘
off-nadir instrument viewing geometry have been used in all the tests.
The DoF for the elevated layer scenario are smaller than those in the urban
polluted scenarios, changing from 1.4 to 4.6 as the resolution varies from 5
to 0.05 cm-1. The change in resolution for an aerosol layer
between 2 and 4 km provides an improvement in the DoF of a factor of 3.3. It
is evident that spectral resolution has a stronger impact on DoF for the
elevated layer scenario than for the urban polluted scenario.
As expected, DoF in the vegetation, marine and arctic scenario are lower
than in the previous cases, due to the generally lower aerosol extinction.
At 5 cm-1 resolution, only the marine scenario allows the retrieval
of pieces of information greater than unity, where for both vegetation and
arctic case no information is available at this resolution. At 0.05 cm-1,
3.84 pieces of information can be retrieved for the marine scenario and 3.43
for both vegetation and arctic case. Again, the low-to-high resolution change leads
to an improvement in the DoF of a factor of 3.2 (marine), 4.7 (vegetation)
and 10 (arctic).
As the aerosol extinction profile is the same in the vegetation, marine
and arctic scenarios, the difference in DoF among these three cases can be
attributed to a surface albedo effect. The higher albedo in the arctic case
leads to lower DoF, in particular for lower spectral resolutions. In fact, at low
resolutions, almost no information (DoF = 0.34) can be retrieved.
However, higher spectral resolutions lead to a DoF improvement of a factor
of 10, and the DoF at a resolution of 0.05 cm-1 is only
10 % lower than in the marine case. Similarly, the vegetation case shows
the number of pieces of information is below 1 at 5 cm-1 and improves
to 3.43 at higher resolution.
The dependence of the content of information on surface albedo for the vegetation,
marine and arctic scenarios is clearly visible in Fig. . where the vegetation
and arctic scenario show the same DoF at 0.5 cm-1 spectral resolution.
The retrieval results are thus independent of albedos above 0.3.
The likely explanation is that above a certain threshold, backscattered radiation
from the surface dominates over that from the aerosol and all the information comes
from the O2 absorption rather than the intensity contrast between surface and aerosol.
High spectral resolution thus seems to be crucial for aerosol retrievals over
medium and high surface albedo areas. The poor retrievals at low resolution over snow
are due to the well-known problem of distinguishing highly scattering aerosol over
a high albedo surface. The lack of contrast makes it impossible for
a radiance-based retrieval to determine the aerosol extinction profile
or even the column. In the marine case, where the albedo is 0.05, the
total DoF at low resolution is 1.19, indicating that a total
atmospheric aerosol optical depth can be retrieved. The vegetation scenario
allows the same retrieval with a DoF = 1 at 1 cm-1 resolution.
In the arctic case however, the total DoF is only 0.34 for low resolution
and 0.7 at 1 cm-1 resolution, making it difficult to even
retrieve a column value. On the other hand, since the retrievals
at high spectral resolutions use information from the O2
absorption band centers and wings, they are partially based on radiances
that do not penetrate all the way to the surface. There is, therefore,
less sensitivity to surface properties.
Altitude sensitivity
The total DoF provides information about the total column. On the
other hand, the AK matrix contains information related to the
atmospheric layers; therefore, a calculation of subsets of the
matrix A (and hence subsets of the Trace[A] = DoF)
allows for an evaluation of the DoF, and its dependence on spectral
resolution, for different altitude ranges. In order to get a better
understanding of the altitude with maximum amount of information for
the different scenarios, we divide the atmosphere in four altitude
regions: I = [0–2] km, II = [2–5] km,
III = [5–15] km, IV = [15–50] km, i.e., I and II for the
lower and mid-troposphere, III for the upper troposphere/lower
stratosphere, and IV for the stratosphere. Figure shows
the color-coded AK for the four regions for a generic simulation. The
results of the altitude-resolved DoF, and its dependence on spectral
resolution for the different scenarios are reported in
Fig. a–f. In all scenarios, an improvement in the DoF in
the different altitude intervals is observed. This follows the general
trend already observed in the whole-atmosphere DoF. However, the
improvement of information with improving spectral resolution is not
uniform with altitude.
In the urban and highly polluted scenarios (panel a and b respectively), the
stratospheric DoF increases from 0.64 and 0.8 to 1.05 and 1.19, respectively,
as the spectral resolution increases from 0.5 to 0.05 cm-1. This
change in DoF is smaller than that observed in the lower and mid-troposphere,
where the increase is a factor of 4.2 and 3, respectively. In fact, for both
regions, the DoF is below 1 for the low-resolution case and increases to
values of 1.4–2 for the high resolution case. While the highly polluted
scenarios requires a resolution of FWHM = 0.1 cm-1 to reach
a DoF greater than 1 in the lower and mid-troposphere, the urban scenario
requires the full resolution to achieve the same result. It is evident that
a high spectral resolution is crucial for tropospheric aerosol extinction
retrievals. At high resolution (FWHM = 0.05 cm-1), almost
65 % (urban) and 64 % (highly polluted) of the total DoF comes from
Regions I and II.
DoF calculated for four different altitude ranges to enhance the
tropospheric contribution with respect to the whole atmosphere for every
single aerosol scenario are shown in (a–f).
The impact of spectral resolution on tropospheric aerosol retrievals is even
more obvious in the elevated layer scenario. In this case a resolution of
FWHM = 0.05 cm-1 is required to reach a DoF greater than 1 in
Regions I and II (panel c). At this resolution, almost 60 % of the total
DoF originates from the lowest 5 km of the atmosphere with a DoF of 2.73 in
this region (sum of DoF in region I and II). The improvement in the
information content at higher resolutions is even clearer in this scenario,
with a factor of 8.5 and 5.6 increase in DoF in Regions I and II,
respectively. It should also be noted that, at higher resolutions, it is
possible to retrieve an elevated aerosol layer above the BL (see profile in
Fig. ).
The vegetation, marine and arctic scenario results (panel d, e and f
respectively) agree with the previous tests. A resolution
of FWHM = 0.05 cm-1 is needed to achieve DoF greater
than 1 in the lower and mid-troposphere. The total number of DoF for
the tropospheric levels (sum of Regions I and II) at FWHM = 0.05 cm-1
resolution is 2.72 (79 % of the total) for the vegetation case, 2.54
(66 % of the total) for the marine case and 2.49 (73 % of the total)
for the arctic scenario. The improvement in DoF with resolution is most pronounced
in the arctic case, where DoF in Region I and II are below 0.1 and increase
by a factor of 59 at high resolution.
It is worth mentioning again that the high resolution retrievals
substantially reduce the albedo effect of the retrieval. In
particular, in the lower and mid-troposphere, there is no difference
in the DoF at high resolutions. Again, this implies that the retrieval
is dominated by the information in the O2 absorption bands,
and in particular in the band wings, which allow probing different
altitudes in the atmosphere without relying on reflected radiances
from the surface.
Integration time sensitivity for highly polluted scenario for
different Sa(a), and different spectral
resolution (b).
Integration time sensitivity
Here we analyze the effects of different integration times (i.e., the
effect of sampling noise) Δt on DoF, investigating the
dependence due to the change in photon noise in two different
tests. The first test is a comparison among the same six different
a priori aerosol uncertainties used for the resolution test
(Fig. a), keeping the spectral resolution constant, with
fixed values of FWHM = 0.1 cm-1 and
Δν= 0.02 cm-1, and with
ω= 0.95. The second test is a comparison among the same
values of spectral resolutions used for all the previous
calculations. In this last test, we keep the a priori aerosol
uncertainty fixed to Sa= 0.2 (44.7 % relative
error), varying instrumental FWHM to investigate how photon noise
affects the dependence of the content of aerosol information on the
resolution. Six different integration times Δt (ranging among
5, 2, 1, 0.5, 0.2 and 0.1 s) have been used for these two tests.
In the first test, the effect of the aerosol uncertainties on the
content of information is investigated for different integration
times. When the spectral resolution is fixed, Eq. ()
shows that the noise depends only on the variation of the integration
time (all the other quantities are fixed). Integration time, however,
is related to the covariance matrix Sϵ through
its dependence on the measurement noise σm
(Eq. ). For a fixed a priori aerosol extinction
profile relative error, Eq. () shows that the averaging
kernel matrix A (and hence DoF) depends only on
Sϵ, because the K matrix (representing
the variation of the radiance with respect to the retrieved parameter)
depends on the spectral resolution, which is fixed for this test.
Figure a shows that the DoF increases with increasing Δt
for every value of Sa. While the change with integration
time seems reasonable, the DoF does not seem to follow the square-root
dependence on Δt shown in Eq. (). For
a realistic value of Sa= 0.2, between Δt= 5 s
and Δt= 0.1 s (50 times smaller), the DoF differ by about 23 %.
For a poor knowledge of the aerosol profile (83.7 % relative error) they
differ by 21 % while for a good a priori knowledge (10 % relative
error) the difference is about 37 %.
The second test (Fig. b) shows that resolution is the main
driver in aerosol profile retrievals for high resolution measurements. The
comparison with different photon noise shows the importance of a high
resolution vs. integration time. The gain, in terms of content of information
when a measurement at high resolution is performed, is greater than any
increase due to a change in Δt. A factor-of-10 change in the
integration time leads to an improvement of about 15 % in the content of
information, whereas the same change at high resolution
(0.5–0.05 cm-1) can lead up to a 43 % increase in the DoF.
Since spectral resolution and noise are linked to each other, our results
indicate that it is advantageous to use higher spectral resolution despite a
lower SNR.
Conclusions
This study investigates the dependence of the information content of
aerosol profile retrievals from high spectral resolution radiance
measurements in the O2 A-band. For this purpose, the DoF of
the aerosol state vector elements has been derived for different
spectral resolutions and sampling intervals. Four different
atmospheric scenarios, covering a variety of vertical aerosol
extinction profiles and albedos, were considered.
In general, our simulations show that high resolution measurements in
the O2 A-band considerably improve the aerosol profile
information content and thereby the retrieval of aerosol profiles.
Example of how different instrument channels
(different frequencies) probe different altitudes and thus different aerosol
layers in the atmosphere. A generic channel A, sounding a spectral window
of the oxygen A-band, penetrates more deeply in the atmosphere reaching
the deeper levels because no absorption occurs. Moving along the shoulder
of the band (channels B and C), the absorption process becomes more intense,
preventing the radiation to penetrate deeper.
At the bottom of the absorption band (channel D),
the light is able to reach the upper part of the atmosphere only.
This process is depicted in the sketch on top,
where shaded gray areas represent the different aerosol profiles
(arrows length is purely symbolic, not representing the actual altitude
sounded at the corresponding channel). Multiple scattering of the incoming
sunlight radiation (red arrows) between aerosol layers and surface at different
albedo is also depicted.
The following conclusions can be drawn based on our results.
The retrieval of tropospheric aerosol extinction profiles in the
lowest 5 km of the atmosphere is considerably improved at higher
spectral resolution. At the highest resolution considered here
(0.05 cm-1), the number of pieces of information that can
be retrieved varies from 3–3.5 in polluted urban cases, to around
2.7 in cleaner vegetation, marine and arctic cases.
The high-resolution retrievals have sufficient information
content in the mid-troposphere to allow the identification and
quantification of the extinction of elevated aerosol layers, such as
those from biomass burning. The DoF of 1.27 and 1.46 in the lower and
mid-troposphere allow for distinction between aerosol at the surface and
aerosol in an elevated layer.
The retrieval sensitivity to aerosol single scattering albedo is
diminished at a high spectral resolution. The total DoF varies from
4.2 for ω= 0.8, to 5.3 at ω values approaching
1. This is in contrast to the behavior at low resolutions, where the
difference is close to a factor of 2.
The influence of surface albedo is considerably reduced at high
spectral resolutions, as illustrated by a comparison of a vegetation,
a marine and an arctic case with identical aerosol profiles.
This is particularly true for the lower and mid-troposphere where retrievals at high
albedo are not possible at low resolutions, while DoF above 1 in the
lowest 2 km allow the retrieval of tropospheric aerosol extinction
when the resolution is high enough. This is because the high-resolution retrieval
is based on the spectroscopic information, i.e., the absorption band shape,
rather than continuum radiances.
Noise considerations indicate that higher-spectral resolution is
advantageous for the aerosol total information content despite the
lower signal-to-noise ratios. The high dynamic range of optical
thickness in the O2 absorption lines seem to outweigh the
noise that is introduced by the high resolution.
The results of this study clearly show how increases in spectral
resolution increase the amount of information available. Our results
agree with other studies investigating the amount of information
available for aerosol retrieval using the O2
A-band. Investigations on retrieval information content of aerosol
over sea and vegetated areas, simulating O2 A-band SCIAMACHY
nadir data at low spectral resolution (FWHM = 0.4 nm or
6.9 cm-1), infer DoF of 3.2 and 2.3 respectively
. The same study, however, points out that
increasing spectral resolution leads to a better vertical resolution
accuracy for the retrieval. A value of 4.1 for the DoF is obtained
when spectral resolution is increased to 0.05 nm
(0.86 cm-1).
Other studies find DoF ranging between 2 and 7, using both nadir and
multi-angle simulations for the retrieval process
. These values were obtained using
a combination of the O2 A-band with the weak
CO2/ CH4 absorption band at 1.61 µm and the
strong CO2 band at 2.06 µm. A spectral resolution
of 0.04 nm (0.7 cm-1), 0.075 nm (1.3 cm-1) and
0.1 nm (1.73 cm-1) for the O2 A-band, the weak
CO2/ CH4 and the strong CO2 band, respectively, were
used with 2.5 spectral samples per FWHM and a SNR = 200. These
studies show that the introduction of a multi-angle and
multi-wavelength approach substantially improves the aerosol
retrieval. The use of a broader wavelength interval or other O2
bands would further increase the information available at high
spectral resolution.
The influence of the surface albedo on the retrieval has been
evaluated by other groups as well, demonstrating that, at higher
spectral resolutions, the information content is less dependent on the
surface albedo . We found similar results in our
surface albedo test, showing that at higher resolutions the absorption
features are fully resolved, thus enhancing the aerosol contribution
to the retrieval and reducing the contribution from surface
reflectance.
It is a well understood fact that measurements at different
wavelengths in the O2 A-band are sensitive to different
altitudes in the atmosphere. As a result, across the spectral window
of the O2 A-band, different wavelengths will penetrate to
different depths in the atmosphere (Fig. ). The
wavelengths with the smallest absorption penetrate deepest into the
atmosphere. Conversely, wavelengths with greater absorption are more
sensitive to the upper parts of the atmosphere. Figure
shows that the spectral features are significantly degraded at low
resolutions: the shape, depth and shoulder slope of the absorption
bands are all changed. At a low resolution, much of the information
coming from the spectral absorption features is lost; as
a consequence, the information coming from different altitudes are
mixed, resulting in a less-resolved atmosphere. This effect also
explains why the dependence of the reflected radiation from the
surface on the retrieval is largest at lower resolutions. A less-resolved absorption band results in an enhancement of the signal
coming from the ground because the dynamic range of absorption optical
thicknesses (and hence altitudes probed) is reduced. Consequently, the
total amount of retrieved aerosol information increases with
increasing spectral resolution, while the dependence on surface albedo
decreases at the same time.
Many studies have pointed out that aerosol retrievals are strongly affected
by noise . In a sensitivity study based on
a fast forward operator, Hollstein and Fischer
investigates the aerosol information content at spectral resolution values of
up to 0.01 nm (0.17 cm-1) and with a SNR ranging between 100 and
1000. Their simulations show that aerosol optical depth retrievals benefit
from an increase in the resolution (1–0.1 nm specifically); in comparison,
aerosol height retrievals, could show a negative effect.
find a similar behavior in
a study simulating aerosol retrievals among four different
satellite-based instruments, GOSAT (Greenhouse Gases Observing
Satellite), OCO-2 (Orbiting Carbon Observatory), Sentinel 5-P and
CarbonSat, with spectral resolutions of 0.03 nm
(0.52 cm-1), 0.044 nm (0.76 cm-1), 0.5 nm
(8.65 cm-1) and 0.1 nm (1.73 cm-1),
respectively. Using SNR of roughly <200 (GOSAT), 400 (CarbonSat),
800 (OCO-2) and >1000 (Sentinel-5 P) at 2×1020photonss-1m-2sr-1µm-1 they
obtain DoF values between 4 and 5.
This comparison reveals that high instrument resolution does not
necessarily lead to an improvement in the amount of information
available, showing that the right combination of resolution and SNR is
crucial for aerosol retrievals, and that a low resolution combined
with a high SNR can lead to better results than a high resolution with
a low SNR. However, their results also suggest that a combination of
very high spectral resolution (>0.03 nm) coupled with high SNR
levels, leads to a larger amount of aerosol information (large values
of DoF).
Our study generally reproduces the impact of SNR, as shown in the
noise sensitivity tests, but the overall advantage of higher spectral
resolutions on the information content dominated. It seems to be
advantageous to use higher spectral resolution, even if this decreases
SNR. We believe that this is due to the fact that the improvements from
using an increased dynamic range of absorptions at higher spectral
resolution, i.e., stronger line center absorptions at higher resolution
(see Fig. ), outweigh the decreased SNR in the retrieval.
It should be noted that the pieces of information available depend on
the noise model adopted and the way of sampling the high resolution
spectra (in this study we have kept the ratio of FWHM and Δν
constant), which could impact the usefulness of higher
resolution. However, all of the DoF determined in this study were
calculated only for a portion of the O2 A-band
(13 122–13 140 cm-1), representing the main molecular
absorption feature, and not for the entire spectral range of the
O2 A-band. It is thus possible, although computationally
expensive, to further improve the DoF when considering a larger
spectra interval. More complex aerosol profiles, coupled with
multi-angle simulations to improve the content of information, will be
the subject of future research. Further, polarization can provide more
information on the aerosol properties, and its impact on the retrieval
needs to be investigated in more detail.
Our results give guidance for new satellite-based high resolution
instruments for future satellite missions, such as the Panchromatic
Fourier Transform Spectrometer (PanFTS) that is currently being
developed to make geostationary measurements of atmospheric
composition. In general, our results seem to indicate that
improvements in the ability to retrieve aerosol height profiles can be
achieved by sampling the O2 A-band at high spectral
resolution.
The Supplement related to this article is available online at doi:10.5194/amt-9-1889-2016-supplement.
Acknowledgements
This work was funded by NASA's Jet Propulsion Laboratory through the
Strategic University Research Partnership (SURP) program.
Edited by: P. Stammes
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