AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-2085-2018Aerosol particle size distribution in the stratosphere retrieved from SCIAMACHY limb measurementsSCIAMACHY aerosol particle sizeMalininaElizavetamalininaep@iup.physik.uni-bremen.dehttps://orcid.org/0000-0002-4102-2877RozanovAlexeiRozanovVladimirLiebingPatriciaBovensmannHeinrichBurrowsJohn P.https://orcid.org/0000-0003-1547-8130Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germanynow at: Leiden Observatory, University of Leiden, Leiden, the NetherlandsElizaveta Malinina (malininaep@iup.physik.uni-bremen.de)12April20181142085210027October20176November20179March201814March2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/2085/2018/amt-11-2085-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/2085/2018/amt-11-2085-2018.pdf
Information about aerosols in the Earth's atmosphere is of a great
importance in the scientific community. While tropospheric aerosol influences
the radiative balance of the troposphere and affects human health,
stratospheric aerosol plays an important role in atmospheric chemistry and
climate change. In particular, information about the amount and distribution
of stratospheric aerosols is required to initialize climate models, as well
as validate aerosol microphysics models and investigate geoengineering. In
addition, good knowledge of stratospheric aerosol loading is needed to
increase the retrieval accuracy of key trace gases (e.g. ozone or water
vapour) when interpreting remote sensing measurements of the scattered solar
light. The most commonly used characteristics to describe stratospheric
aerosols are the aerosol extinction coefficient and Ångström
coefficient. However, the use of particle size distribution parameters along
with the aerosol number density is a more optimal approach. In this paper we
present a new retrieval algorithm to obtain the particle size distribution of
stratospheric aerosol from space-borne observations of the scattered solar
light in the limb-viewing geometry. While the mode radius and width of the
aerosol particle size distribution are retrieved, the aerosol particle number
density profile remains unchanged. The latter is justified by a lower
sensitivity of the limb-scattering measurements to changes in this parameter.
To our knowledge this is the first data set providing two parameters of the
particle size distribution of stratospheric aerosol from space-borne
measurements of scattered solar light. Typically, the mode radius and w can
be retrieved with an uncertainty of less than 20 %. The algorithm was
successfully applied to the tropical region (20∘ N–20∘ S)
for 10 years (2002–2012) of SCIAMACHY observations in limb-viewing geometry,
establishing a unique data set. Analysis of this new climatology for the
particle size distribution parameters showed clear increases in the mode
radius after the tropical volcanic eruptions, whereas no distinct behaviour
of the absolute distribution width could be identified. A tape recorder,
which describes the time lag as the perturbation propagates to higher
altitudes, was identified for both parameters after the volcanic eruptions. A
quasi-biannual oscillation (QBO) pattern at upper altitudes (28–32 km) is
prominent in the anomalies of the analysed parameters. A comparison of the
aerosol effective radii derived from SCIAMACHY and SAGE II data was
performed. The average difference is found to be around 30 % at the lower
altitudes, decreasing with increasing height to almost zero around 30 km.
The data sample available for the comparison is, however, relatively small.
Introduction
Stratospheric aerosols are of a great interest for researchers because of
their impact on the climate. The radiative budget of the Earth is altered by
stratospheric aerosol, which scatters electromagnetic radiation in the
atmosphere. A large amount of aerosol emitted by volcanic eruptions
significantly changes the radiative forcing affecting global temperatures.
For example, the super-colossal Tambora eruption in 1815 is claimed to have
caused
“the year without a summer” in 1816. The more recent colossal eruption of Mount
Pinatubo in 1991 caused a surface temperature cooling of a few tenths of a
kelvin . According to
, smaller volcanic eruptions also noticeably
affect the global temperature. Another important role of aerosols is their
ability to act as condensation nuclei for polar stratospheric clouds (PSCs),
which provide surface for heterogeneous chlorine activation and
denitrification processes, thus increasing ozone depletion
.
Accurate information about stratospheric aerosols is important for different
research fields. For example, stratospheric aerosol parameters are needed
for modelling the processes related to the stratosphere, including aerosol
microphysics model validation. Modellers require stratospheric aerosol
climatologies as initial conditions for climate model simulations and
predictions e.g..
concluded that it is not only temperature that is affected by
the volcanic eruptions but also precipitation, sea level pressure, and wind
speed change in response to the changing aerosol load. All the above-mentioned data are not only needed for improving global climate models but
also required for the investigation of geoengineering. In addition, accurate
knowledge of the stratospheric aerosol loading is essential to improve trace
gases retrievals from measurements of the scattered solar light
e.g..
As summarized by , stratospheric aerosols are commonly
described as spherical droplets composed of a mixture of sulfuric acid
(H2SO4) and water (H2O). They may also be comprised of
meteoric dust as well as other nonsulfate material. The most important
sources of stratospheric aerosols are carbonyl sulfide (OCS) and
sulfur dioxide (SO2), which are oxidized to sulfuric acid. Short-lived
SO2 is mostly injected directly into the lower stratosphere by large
volcanic eruptions, while OCS, the longest-lived sulfur gas, which has
mostly marine origin, is transported from the troposphere into the
stratosphere by convection in the tropics. Aerosol composition in the
stratosphere is controlled by the Brewer–Dobson circulation, as well as by
chemistry and microphysics during aerosol formation, growth, and removal
through sedimentation. Although some studies show increased SO2 from
fossil fuel combustion transported to the lower stratosphere via the Asian
monsoon , stratospheric aerosol precursors have
predominantly natural sources (e.g. volcanic eruptions and OCS
emissions from the ocean) . Biomass burning, which is
mostly of anthropogenic origin, can also lead to the enhanced stratospheric
aerosols by emitting SO2, OCS, and black carbon. Multiple
instruments registered the increase in the measured quantities during the
Australian “Black Saturday” bushfires e.g.
One of the most commonly used characteristics to describe stratospheric
aerosols is their extinction coefficient. Although it was employed in many
previous studies dealing with space-borne measurements in UV–visible–IR
spectral range , the usage of this parameter is associated with several issues.
First the extinction coefficient is wavelength dependent and this dependence
is determined by the composition of the aerosol droplets and their particle
size distribution. Second, while a direct evaluation of the aerosol
extinction can be done for occultation measurements, for limb measurements
this parameter is dependent on the assumed particle size distribution
parameters.
In contrast to the extinction coefficient, the retrieval presented in this
paper characterizes not only the amount of the stratospheric aerosol but
also its particle size distribution as it is widely done in in situ
measurements . Most commonly, mode radius, distribution
width, and the particle number density are used to describe the particle size
distribution of stratospheric aerosols. These parameters are, however,
challenging to retrieve. Additionally, some information about the particle
size distribution can be obtained from the Ångström coefficient, which is
determined by the wavelength dependence of the aerosol extinction
coefficient. However, the Ångström coefficient cannot be uniquely
transformed into particle size distribution parameters. Generally, different
combinations of the mode radius and distribution width may result in the same
value of the Ångström coefficient. Furthermore, for each particular
particle size distribution the resulting Ångström coefficient usually
depends on the wavelength range.
Highly vertically resolved information on stratospheric aerosols is provided
by in situ balloon-borne and aircraft measurements, as well as by remote
sensing measurements from ground-based lidars . However,
these measurements are quite sparse, and therefore satellite observations are
commonly used to obtain knowledge about the global behaviour of stratospheric
aerosols. Space-borne measurements of the stratospheric aerosols started in
the late 1970s with SAM (Stratospheric Aerosol Measurement), SAM II, SAGE
(Stratospheric Aerosol and Gas Experiment) and SAGE II instruments
. The latter was one of the most distinguished
instruments of this era; it operated from 1984 to 2005 and used the solar
occultation technique. The current v7.0 SAGE II product provides the aerosol
extinction at four wavelengths and some information on particle size
distribution parameters (more detailed in Sect. ). At the
beginning of the 2000s several space-borne instruments of a newer generation
using stellar occultation and limb measurement techniques were launched.
SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric
CHartographY) was one of these instruments operated on the Envisat satellite
from 2002 to 2012. SCIAMACHY had three main measurement modes: nadir,
solar/lunar occultation, and limb scattering. Other instruments on board
Envisat providing stratospheric aerosol information were Global Ozone
Monitoring by Occultation of Stars (GOMOS) and the Michelson Interferometer
for Passive Atmospheric Sounding (MIPAS). GOMOS made measurements using the
stellar occultation technique and provided aerosol extinction coefficient
, while the MIPAS instrument measured limb
emission spectra and provided vertical profiles of
SO2 and sulfate aerosol (H2SO4) volume densities
. OSIRIS (Optical Spectrograph and InfraRed Imager
System), on board the Odin satellite, which was launched in 2001 and is still
operating, measures scattered solar light in the limb-viewing geometry
. Using these data, aerosol extinction profiles and Ångström coefficient are retrieved . The most
recent instrument, which combines nadir and limb scattering measurements, is
the Ozone Mapping Profiler Suite (OMPS) . OMPS was launched
by NASA at the end of 2011 and is still in operation. First results with
respect to the retrievals of aerosol extinction coefficient from OMPS-LP
(Limb Profiler) are presented in several publications
. In addition, it is important
to mention the space-based lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal
Polarization Lidar), which provides measurements of aerosol backscatter
coefficient, which is afterwards converted to the aerosol extinction
coefficient. Profiles of the aerosol extinction from CALIOP have the highest
vertical resolution among the space-borne instruments
.
This paper has the following structure: in Sect. the
most essential information about the SCIAMACHY instrument and the retrieval
method used in the study is presented. In Sect. the
sensitivity of the retrieval algorithm is investigated. Results of the
SCIAMACHY data retrieval along with the discussion are presented in
Sect. . In Sect. retrieval results from
SCIAMACHY are compared to the SAGE II particle size distribution product.
Conclusions are given in Sect. .
Instrument and applied algorithmSCIAMACHY on Envisat
SCIAMACHY was a joint German, Dutch, and Belgian contribution to Envisat
(European Environmental Satellite) launched by the European Space Agency (ESA) on 1 March 2002 into a
sun-synchronous orbit at 800 km altitude with a 10:00 descending node
Equator-crossing time. SCIAMACHY provided data from August 2002 to April
2012, when the connection with the satellite was suddenly lost. The
instrument operated in three observational modes: nadir, limb, and solar/lunar
occultation. Measurements of the solar irradiance were provided on a daily
basis. SCIAMACHY took measurements in eight spectral channels, which covered the
spectral interval between 214 and 2386 nm. Spectral resolution varied from
0.2 to 1.5 nm depending on the wavelength.
In this study, measurements taken in the limb-viewing geometry were used. In
this geometry the instrument observed the atmosphere tangentially to the
Earth's surface in the altitude range from about 3 km below the surface,
i.e. when Earth's surface is still within the field of view of the
instrument, and then scanning vertically up to about 100 km. The measurements
were performed with a vertical instantaneous geometrical field of view at the
tangent point of 2.6 km and vertical sampling of 3.3 km. The horizontal
cross-track field of view was 110 km. A horizontal scan with the total swath
of about 960 km was performed during each observation sequence. The
horizontal cross-track resolution was mainly determined by the integration
time during the horizontal scan, usually reaching about 240 km. For a typical
limb measurement, the observed signal integrated by the instrument was read
out four times per horizontal scan, which resulted in four independent limb
radiance profiles obtained during a horizontal scan. The horizontal
along-track resolution is estimated to be about 400 km. Further information
about the instrument can be found in
, , and .
The retrievals performed for this study were done using version 8 of the
SCIAMACHY Level 1 data with calibration flags 1, 2, 4, 5, and 7, i.e. the
leakage current, pixel-to-pixel gain, stray-light correction, wavelength
calibration, and radiometric calibration, respectively. Level 1 data were
provided by ESA.
Aerosol parametrization
A balance of the processes creating and removing the stratospheric aerosols
as well as of those influencing the size of aerosol particles commonly
results in a size distribution which is well described by a single- or
multiple-mode log-normal shape
.
For example, in some cases use a bimodal particle size
distribution to achieve the best approximation to their measurements from
in situ optical particle counters (OPCs). However, this approach is not
suitable for remote sensing instruments working in the limb or occultation
geometry. This is because six independent pieces of information at each
altitude level are needed to describe a bimodal log-normal particle size
distribution, while measurements from space-borne instruments commonly
provide two to three parameters per altitude level
. Here, similar to other
studies cited above, the distribution is considered to be unimodal and is
described by
dndr=N2πln(σ)rexp-(ln(rg)-ln(r))22ln2(σ),
where N
is the total number density of the aerosol particles, rg is the median
radius, and ln(σ) is the standard deviation of the
dln(n)dln(r) distribution. Though rg is directly used in the
formula and indicates the maximum of dln(n)dln(r) distribution,
we prefer to work with the mode radius Rmod=rg/exp(ln2(σ)),
which determines the maximum of dndr distribution and, thus, is
more evident for the interpretation of results. Furthermore, throughout this
study a representation of the absolute distribution width by the standard
deviation of the dndr distribution will be used:
w=rg2expln2(σ)exp(ln2(σ))-1.
For visual interpretation, w is more convenient than σ, as the
latter is defined relative to the rg parameter. In the following text
σ is used when describing retrieval settings, while w is used when
discussing the retrieval results.
Algorithm description
Unlike the algorithms aimed to retrieve aerosol particle size distribution
parameters from the occultation measurements
e.g., our algorithm does not
include aerosol extinction retrieval as an intermediate step. Instead, the
measured limb radiances at seven wavelengths are directly employed to obtain
the parameters. The wavelengths are chosen outside the intervals near the
spectral channel boundaries to avoid artifacts of the optical system. In
order to reduce the measurement noise the sun-normalized radiances are
averaged in the following intervals: λ1= 750 ± 2 nm,
λ2= 807 ± 2 nm, λ3= 870 ± 2 nm,
λ4= 1090 ± 2 nm, λ5= 1235 ± 20 nm,
λ6= 1300 ± 6 nm, and λ7= 1530 ± 30 nm. The wavelengths below
750 nm are not considered because of a lower sensitivity to aerosols, and
those above 1530 nm are rejected because of an insufficient signal-to-noise
ratio. The intervals are chosen to avoid absorption signatures of other
atmospheric constituents, minimizing uncertainties caused by incorrect
absorber profiles knowledge. Limb radiances are normalized by the measured
solar irradiance spectrum (Isol).
Both forward modelling and the retrieval are performed with the software
package SCIATRAN-3.8 . The retrieval approach employs an
iterative regularized inversion technique similar to that described by
to solve the inverse problem. In this approach the
inversion of the linearized radiative transfer equation is required, which is
formulated as
y-y0=Kx^+ϵ,
where y is the measurement vector, whose components
[y]m=lnImeas(λj,hk)Isol(λj)
are the logarithms of the normalized limb radiances at all selected
wavelengths (λ) and tangent heights (h). Here, index m is defined
as m=(k-1)Nλ+j, where Nλ is the total number of
wavelengths, k runs over selected h, and j runs over selected λ.
Components of the vector y0 are given by
[y0]m=lnImod(λj,hk)Isol(λj),
where Imod(λj,hk) are modelled limb spectra at
wavelength λj and tangent height hk. State vector
x^ contains relative deviations of the retrieved atmospheric
parameters, x, from their initial values, x0, e.g.
[x^]i=[x]i-[x0]i[x0]i is
the ith component of the vector x^.
[K]m,i=∂[y]m∂[x]i|x=x0 defines components of
the weighting function matrix (Jacobian matrix). All kinds of errors are
denoted by ϵ. The solution of the linear inverse problem
given by Eq. () can be found according to
as
x^=KTSy-1K+Sa-1-1KTSy-1y-y0,
where Sa represents the a priori covariance matrix, and
Sy is the noise covariance matrix.
Taking into consideration the non-linearity of the inverse problem, an
iterative approach is implemented. Here, we do not follow the maximum
a posteriori probability (MAP) approach suggested by
. As the inverse problem is strongly non-linear,
the use of a reasonable a priori covariance in the MAP approach results in a
diverging solution, while the a priori variance which results in a stable
solution strongly constrains it to the initial guess. To overcome this issue
the weighted regularized inversion similar to zeroth-order Tikhonov method is
used in this study. Thereby, the weight of the a priori information is
decreased by replacing the initial state vector x0 at each
iterative step by the state vector obtained at the previous iteration,
xn. The variance of xn is set to 1 %. This
parameter was selected empirically to achieve a trade-off between the
retrieval stability and sensitivity. Thus the solution at the (n+1) step is
given by
x̃=KnTSy-1Kn+Sa-1-1KnTSy-1y-yn,
where
[x̃]i=[xn+1]i-[xn]i[xn]i.
The iterative process stops when the maximum difference between the
components of the solution vector at two subsequent iterative steps does not
exceed 1 %, the relative change in the root mean square of the fit residuals
at two subsequent iterations is less than 0.001 or the limit of 100 iterations is reached. Since a strict constraint is put on deviations from
the a priori vector, there are typically 20–30 iterations needed for the
retrieval process to converge.
The noise covariance matrix is assumed to be diagonal – i.e. the errors are
spectrally uncorrelated. The signal-to-noise ratio was estimated for each scan
from SCIAMACHY measurements and varies from 65 to 5000 depending on the
wavelength and tangent height. For each of the particle size distribution
parameters, the diagonal elements of the a priori covariance matrix are set
empirically to 0.01, and the non-diagonal elements drop off exponentially
with a correlation radius of 3.3 km, while the elements describing the
correlation between different parameters are chosen to be 0.
At present, only the retrieval of Rmod and σ is performed, and
from these parameters w is calculated using Eq. (). The
particle number density, N, profile is selected in accordance with the
ECSTRA model climatology for background aerosol loading conditions
. N is assumed to decrease exponentially from
15.2 cm-3 at 18 km to 0.5 cm-3 at 35 km and remains unchanged
during the whole retrieval process. This is justified by a low sensitivity of
the retrieval to this parameter (see Sect. for details).
The initial guess parameter values are arbitrarily chosen as
Rmod= 0.11 µm and σ= 1.37. Scattering phase functions as well
as extinction and scattering coefficients per particle are calculated using
the Mie scattering theory. Aerosol parameters are defined from 12 to 46 km,
where the particles are assumed to be sulfate droplets (75 % H2SO4
and 25 % H2O) with 0 % relative humidity in the surrounding
atmosphere. Below 12 km and above 46 km the aerosol number density is set to
0. The refractive indices are taken from the OPAC database
. Although the chosen representation of the aerosol
composition might be inadequate below the tropopause and/or above 35 km, it
enables us to avoid jumps and unreasonable values at the lowermost and the
uppermost retrieval altitudes and does not affect significantly the retrieval
sensitivity region (18–32 km; see Sect. for details).
The retrieval is performed for the tropical zone (from 20∘ S to
20∘ N) in the altitude range from about 18 up to about 35 km (the
actual altitudes depend on the latitude and season). The choice of the
altitude range is justified by lower sensitivity below 18 km
and higher biases due to stray light above 35 km. To
minimize the need for constraints and to avoid additional errors, e.g.
related to altitude interpolation, the measurement grid is used for the
retrieval. The exact levels of the measurement grid depend on time and
latitude, but usually the grid ranges from about 0 to 100 km with a 3.3 km
step. Outside the retrieval range, for the altitudes lower than the minimum
retrieval height down to 12 km, the initial guess profile scaled relative to
the result at the lowermost retrieved altitude is used, while for the
altitudes above the upper border up to 46 km no additional constrains are
set. We focus our initial study in the tropics because the transport
mechanisms here are less complicated, which makes the interpretation of the
obtained results more straightforward. To extend the retrieval to the
extratropical latitude bands, issues related to the scattering angles close
to backward and forward scattering as well as large solar zenith angles need
to be dealt with.
To reduce the sensitivity of the retrieval to the reflection properties of
the underlying surface, effective spectral Lambertian albedo is concurrently
retrieved based on the limb radiances at the same tangent heights where the
aerosol particle size retrieval is performed. As will be shown further, at
35 km aerosol influence on the limb radiances is rather small; thus the
information from this tangent altitude contributes mainly to the albedo
retrieval, while other tangent altitudes are employed for the stability
reason. The initial albedo guess is 0.5 for all wavelengths. As for the
particle size parameters, albedo for different wavelengths is uncorrelated
with the other parameters and with the albedo at the other wavelengths.
Clouds below and within the field of view remain an issue, even with the
albedo retrieval. Therefore, only completely cloud-free profiles (from 0 km)
are used in this work. For this research, instead of the standard SCODA cloud-filtering algorithm , a cloud-filtering algorithm
based on a statistical approach was used . The algorithm
designed by is more preferable for use in aerosol
retrievals, because the approach has the
disadvantage of flagging the pixels with high aerosol loading (i.e. after the
volcanic eruptions) as cloudy.
Atmospheric pressure and temperature background profiles are from ECMWF
(European Centre for Medium-Range Weather Forecasts) operational analysis
data for the specific date, time, and location of each SCIAMACHY limb
measurement.
Sensitivity studiesModel simulations
In this section sensitivity of the limb radiances to the aerosol particle
size distribution parameters is analysed by performing simulations with the
radiative transfer model SCIATRAN. For this study the model was run for an
observational geometry typical for the tropical region in the middle of July
(a solar zenith angle of 41∘ and solar azimuth angle of
141∘). The extraterrestrial solar flux measured by SCIAMACHY for the
chosen day was used. In the radiative transfer model, multiple scattering was
taken into account and the albedo was set to 0 (representation of the ocean
surface).
To understand the sensitivity of the retrieval to the different particle size
distribution parameters, three sets of limb radiance simulations were
performed. For each set, one of the particle size distribution parameters was
varied, while the other two parameters were fixed. The modelled intensities
for these simulation sets are presented in Fig. at the
tangent height of 25 km. As the results at other tangent heights show similar
behaviour, we analyse the sensitivity only at this arbitrarily chosen
geometry. Natural logarithms of the simulated sun-normalized intensities for
different values of the selected parameter (this representation corresponds
to the way that intensities are treated by the retrieval algorithm) are shown
as lines. The shaded areas represent the amplitude of the intensity
variations resulting from variations in the other two parameters. In this
study the following simulation sets were performed: the first set employed a
fixed σ=1.37 and the same number density profile as used in the
retrieval, while the Rmod varied from 0.05 to 0.15 µm
(Fig. a). Another set of conditions
(Fig. b) was simulated by changing σ from 1.1 to
2.0 with the fixed Rmod= 0.08 µm and the same number density as in
previous simulation set. This resulted in the variation in w from 0.008 to
0.13 µm. The last set of simulations (Fig. c) was
made by scaling the whole number density profile by factors ranging from 1 to
2, with the fixed Rmod= 0.08 µm and σ= 1.37. The parameter set
with standard non-scaled N profile with
Rmod= 0.08 µm and σ= 1.37 was chosen as a reference, as this
set of parameters is considered to be typical for a background (free of
volcanic perturbations) atmosphere. We note that this investigation
aimed to analyse a relative sensitivity of the observed limb radiance to the
variations in different parameters and, thus, its results are not influenced
much by their absolute values. Nevertheless, the considered amplitude of the
parameter variations is in qualitative agreement with the reported variations
in Rmod, σ, and N in the stratosphere .
Logarithms of sun-normalized intensities spectra at a tangent height
of 25 km, modelled with different particle size distribution parameters.
The black line represents the “standard” background conditions.
Analysing Fig. it is evident that variations in the limb
radiance due to variations in the particle number density profile, N, are
remarkably smaller than those due to variations in Rmod or σ (or
w). The variation in N profile by a factor of 2 (or 200 %) results in a
similar response in the limb radiance to the variation in Rmod by
0.01 µm (≈ 13 %) or variation in σ by 0.13 (≈ 10 %). This effect can be explained as follows: in a rough approximation the
scattered solar radiance observed by a limb-viewing instrument is
proportional to the product of N, scattering phase function (p), and
aerosol extinction cross section (αaer) see
e.g.. Both p and αaer depend on Rmod
and σ. As the dependency of p and αaer on Rmod and
σ is non-linear, these parameters contribute to the radiance stronger
than N. The weaker dependence is also illustrated by much smaller weighting
functions for N than those for Rmod or σ. The weighting
functions (Rmod, σ, and N) for each retrieved altitude
are shown in Fig. . As the weighting functions (see
Sect. ) show the variations in the observed signal due to
changes in the considered parameter, much smaller weighting functions of N
(Fig. c) mean much smaller contribution due to
variations in the parameter in the measured signal. As a consequence, the
variations in N are neglected in the retrieval, and the altitudinal
behaviour in the N profile is considered to be constant with the time.
It is worth emphasizing again that the number density profile used in the
above described retrieval is derived from the ECSTRA climatology for
background aerosol loading conditions (see Sect. ). Even though we
increase the uncertainty for the volcanic active periods, for the background
period the errors are rather small because we use the profile which is
expected to be close to reality. The magnitude of the errors in the
retrieved Rmod, σ, and w due to unknown N is discussed in the
next section.
Relative logarithmic weighting functions at
λ7= 1530 nm in the retrieval height range for
Rmod(a), σ(b), and particle number
density N(c). The different tangent heights are colour coded.
Characterization of the retrieval
The effect of the unknown number density on the retrieved Rmod and
σ, as well as general retrieval characteristics, is discussed in this
section.
To characterize the sensitivity of retrieval algorithms averaging kernels are
commonly used. In general, this characteristic is specific to the measurement
setup, algorithm implementation, and retrieval parameters settings. Averaging
kernels for the limb scatter space-borne instruments in the relevant altitude
region are distinctly peaked at the tangent altitudes, where a bulk of
information is originating from. Their shape characterizes the vertical
sensitivity and resolution of the measurement-retrieval system and provides
information on the contribution of the a priori information to the retrieved
profiles.
In Fig. averaging kernels for both Rmod (panel a)
and σ (panel b) are presented for typical tropical observation
conditions. Since variations in the observational geometry (viewing angle and
solar zenith angle) in the tropics are rather small, only one specific
example for typical illumination and background conditions is presented here.
At all altitudes in the retrieval range (in this case 18.5–34.9 km), except
for the uppermost one, averaging kernels for both parameters have pronounced
peaks at the measurement tangent altitudes, indicating that no significant
smearing of the results in the vertical domain has occurred and optimal
sensitivity of the retrieval is achieved for each tangent altitude. At the
uppermost altitude (34.9 km) averaging kernels are clearly broader and the
peak is much less distinct in comparison to the other altitudes. Such a shape
of the averaging kernels at this altitude is evidence of a strong influence
of the neighbouring altitude levels and partial loss of sensitivity.
Selected scenarios and associated maximum of absolute (relative)
errors.
∗ Colour of the lines in Figs. –.In the last three columns maximum absolute error for the profile is given by the
number without brackets, while the maximum relative error is presented in
brackets.
As pointed out above, in the performed retrieval the relative deviations from
the solution obtained at the previous iterative step are determined. The
state vector variance is set to 1 % of the parameter values resulting from
the previous iteration. For this reason such widely used characteristics as
the averaging kernel peak value, a posteriori covariance, and measurement
response are meaningful only within one iterative step but not applicable to
the full iterative process.
An additional assessment of the retrieval performance was done by simulating
the limb radiance using perturbed values for the retrieved parameters and
then performing the retrieval using synthetic data. For all retrieval runs
the same settings as in the nominal retrieval process were used. Ideally, the
retrieved values are expected to be the same as those used to simulate the
radiances. To test the retrieval under different conditions, five scenarios
were used. All scenarios were simulated for one observation geometry (viewing
and solar zenith angles) typical for SCIAMACHY limb measurements in the
tropical region. Detailed information on the selected scenarios is
presented in Table . Generally, the scenarios can be
divided into three types depending on the perturbation of the particle size
parameters. The first type includes “small”, “background”, and
“volcanic”
scenarios. The intensities for these scenarios were modelled using Rmod
and σ as listed in Table with the same N
profile, as assumed in the retrieval algorithm – i.e. only
Rmod and σ were perturbed. The second type is represented
by the
“volcanic (2N)” scenario, which was modelled with the same Rmod and
σ as the “volcanic scenario”, but the N profile
was multiplied by a factor of 2 between 12 and 23 km. The perturbation in the
number density profile was performed only in the lower layers because
significant changes in the aerosol loading due to volcanic eruptions during
the SCIAMACHY lifetime were shown to reach maximum altitude of about 23 km
. The third type is represented by the “unperturbed”
scenario. For this scenario intensities were simulated with
Rmod= 0.11 µm, σ= 1.37, and the N profile
assumed in the retrieval, i.e. with the same parameter values as used to
initialize the retrieval algorithm. All these values with slight adjustments
were taken from and are expected to be close to the
reality. As there is no reliable information on the altitudinal behaviour of
Rmod and σ, the values for those parameters are kept constant with
the height. In all the scenarios modelled surface albedo was assumed to be
0.15 at all wavelengths. Gaussian noise has been added to all simulated limb
radiances based on the signal-to-noise ratios estimated from SCIAMACHY
measurements. To ensure reliable statistics, 100 independent noise sequences
were generated.
Averaging kernels for the aerosol particle size distribution
parameters: Rmod(a) and σ(b). Results
were obtained using the spectra modelled with perturbation of all three
parameters. The different tangent heights are colour coded.
Figure presents the retrieved profiles of Rmod (panel a) and their relative errors (panel b) for the above discussed
scenarios. For σ, retrieved profiles and relative errors are plotted
in Fig. . Solid lines in
Figs. a and a refer to the median retrieved
profiles for the scenario, and dashed lines represent true modelled
conditions. In Figs. b and b
solid lines show relative median errors of the retrieved profiles with
respect to the true value. In both figures, shaded areas show ±1 standard
deviation from the median value.
Rmod profiles (a) and their relative errors
(b) for a typical tropical observation geometry. The solid lines
show the retrieval results, while the dashed lines represent the true values.
The shaded areas stand for ± 1 standard deviation. The
scenarios used for the simulations are listed in Table 1.
σ profiles (a) and their relative errors
(b) for a typical tropical observation geometry. The solid lines
show the retrieval results, while the dashed lines represent the true values.
The shaded areas stand for ± 1 standard deviation. The
scenarios used for the simulations are listed in Table 1.
Analysis of Figs. and shows that the
retrieval results using “unperturbed” profiles (brown lines) of Rmod and
σ as well as the results for profiles with the perturbed Rmod and
σ (cyan, blue, and green lines) are very close to the true values. The
relative error is within 20 % for Rmod and within 5 % for σ.
Maxima of the absolute (ϵ=|retrieved-true|) and relative errors for
all scenarios and parameters are summarized in Table . It
is worth mentioning that the maximum deviation for the scenarios with
unperturbed N is about ±0.01 µm for Rmod. For σ, the
absolute error differs, but it does not exceed 0.07. For the volcanic scenario
with a perturbed N profile it is obvious that a wrong number density
assumption influences the retrieved profile, although this influence is
rather small. The relative error for that case is less than 20 % for
Rmod and less than 10 % for σ, maximum absolute errors for
Rmod and σ are respectively 0.04 µm and 0.1. The retrieval
scenario with “unperturbed” particle size distribution parameters resulted in
the profiles which differ less than 2 % from the true values. This
characterizes the retrieval error resulting from the measurement noise. The
retrieved albedo varies from 0.11 to 0.23 depending on the scenario and the
wavelength.
Referring to Sect. it is important to remember that in
this study we analyse w as given by Eq. () rather than
σ. Even though σ as a parameter is widely used in the
retrievals and in the climate models, it does not provide visually
interpretable information about the width of the particle size distribution,
because it is defined relative to rg. For this reason we use σ
as a retrieval parameter, while w is used in the interpretation and
discussion of the retrieved results. For that reason it is important to
assess
retrieval uncertainties for w as well.
In Fig. a, profiles of the absolute distribution width (solid
lines), derived from the retrieved Rmod and σ, for each
scenario are depicted. As in Figs. and , the
dashed lines depict the true values. In of Fig. b relative
errors are plotted by solid lines. Shaded areas in Fig. a and
b show the ± standard deviation.
Looking at Fig. , it is obvious that the retrieval errors from
the distribution width for small (cyan lines), background (blue lines) and
unperturbed (brown lines) scenarios are rather small: absolute errors
(Table ) are less than 0.001 µm, and relative
errors are smaller than 2 %. In the volcanic case with unperturbed N
(green lines) differences are within 0.005 µm, which corresponds to
14 %. Conversely, for the volcanic scenario with perturbed N (red
lines), the derived w deviates from the true value by up to 40 %
(0.015 µm).
Selected scenarios and associated maximum of absolute (relative)
errors.
∗ Colour of the lines in
Figs. –.In the last 3 columns maximum absolute error for the profile is given by the
number without brackets, while the maximum relative error is presented in
brackets.
Absolute distribution width, w, profiles (a) and their
relative errors (b) for a typical tropical observation geometry. The
solid lines show the retrieval results, while the dashed lines represent the
true values. The shaded areas stand for ± 1 standard
deviation. The scenarios used for the simulations are listed in Table 1.
Although the differences for Rmod and σ are comparably
small, it is important to mention that, from all the modelled scenarios, w
for the volcanic scenarios is generally the smallest, and larger relative
errors are often associated with the division by a small true value. To test
this hypothesis another volcanic scenario with larger w (“wide”) was
simulated (Rmod= 0.20 µm, σ= 1.27; N
profile is perturbed by the factor of 2 in a layer between 12 and 23 km) and
run through the synthetic retrieval. The retrieved profiles of
Rmod, σ, and absolute distribution width are shown in
Fig. , where solid lines show the retrieved profiles and
dashed lines represent true values. In red, the standard “narrow” volcanic
scenario with perturbed N is presented, whereas the magenta colours depict
the “wide” scenario (see Table ). Relative errors for
these scenarios and parameters are presented in Fig. . From
Figs. and it is clearly seen that even
though the behaviour of Rmod and σ did not change
significantly, and the absolute/relative errors are very similar for all
three parameters, the maximum relative error for w decreased to 23 %.
To summarize Sect. it can be concluded that, for
background conditions, the algorithm is capable of retrieving
Rmod with relative accuracy of better than 20 % (absolute
uncertainty of about 0.01 µm) and σ better than 5 %
(absolute uncertainty less than 0.07). The relative accuracy of the derived
w is dependent on its absolute value and is about 2 %
(0.001 µm) for background conditions. As N is fixed in the
retrieval, all uncertainties associated with this parameter are interpreted
as changes in Rmod and/or σ. Considering previous studies
on stratospheric aerosol size distribution
, we
believe that for the period from 2002 to 2012 a variation in N within a
factor of 2 is a realistic assumption. Thus, possible retrieval errors after
volcanic eruptions occurred during the SCIAMACHY observation period are
estimated to be about 20–25 % for Rmod and less than
10 % for σ. For the volcanic scenarios the relative error for w
can reach up to 40 %, while the absolute error does not exceed
0.015 µm.
Retrieved profiles (solid lines) of
Rmod, σ and derived values of w for the volcanic
scenarios with perturbed Rmod, σ, N for a typical
tropical observation geometry. The dashed lines represent the true values.
The shaded areas stand for ±1 standard deviation. The scenarios used for
the simulations are listed in Table 2.
Relative errors for Rmod, σ
and derived w for the volcanic scenarios with perturbed Rmod,
σ, N for a typical tropical observation geometry. The shaded areas
stand for ±1 standard deviation. The scenarios used for the simulations
are listed in Table 2.
Results and discussion
In this section, first results of the retrieval of the aerosol particle size
distribution parameters from SCIAMACHY limb observations are presented. As
was mentioned above (see Sect. ), the retrieval algorithm
was applied to data for the tropical region and completely cloud-free (from
0 km) scenes, resulting in 9727 profiles for the entire SCIAMACHY observation
period. The cloud-filtering algorithm by used in this
study employs a probability approach, which means that there is still a 10 %
chance in cloud or its fraction to influence the profile. Thus, to reject
possible unreasonable results, post-retrieval filtering criteria were applied
as follows: values with Rmod< 0.03 µm were rejected, as they are
laying outside the sensitivity range of the instrument, and aerosol
extinction at 750 nm calculated using the retrieved particle size
distribution parameters was not allowed to exceed 0.0015 km-1. A similar
approach to filter the aerosol extinction at 750 nm was used to reject cloudy
scenes in SCIAMACHY V1.4 aerosol extinction product .
Non-converged retrievals with 100 iterations (4.6 % of the whole amount of
retrieved profiles) were also excluded. No additional filtering criteria were
implemented. The maximum mode radius in the retrieved data set reached the
value Rmod= 0.21 µm, and σ varied from 1.02 to 2.9. All the
values are considered to be realistic within the reported errors.
As the temporal sampling of the current product is not sufficient to analyse
the volcanic plumes, monthly zonal (20∘ N–20∘ S) means were
used to evaluate the overall state of the stratosphere during the SCIAMACHY
observation period. These monthly averaged values of Rmod and w are
presented in Figs. and , respectively. Here,
some obvious patterns such as the increase in the values after most of the
volcanic eruptions (dashed lines) can be readily identified. In addition,
there is a pronounced seasonality in both Rmod and w. As the seasonal
cycle of stratospheric aerosols has already been discussed by several authors
, we focus our study on the analysis of
the anomalies of the particle size distribution parameters. Anomalies or
deseasonalized values for Rmod and w, as shown in
Figs. and , respectively, were obtained by
subtracting from each monthly mean value an average over all corresponding
months in the whole observation period (e.g. the average value for all the
Januaries of the 10-year period was subtracted from each January monthly mean
value in the observation period).
Analysing the anomalies for Rmod presented in Fig. it
can be noticed that there is a distinct increase in Rmod in the lower
altitudes after most of the volcanic eruptions, except for the eruptions of
Ruang and Reventador in late 2002 and Merapi in late 2010. For these tropical
eruptions only a slight increase in Rmod is observed. This may be
related to a smaller amount of SO2 released during these eruptions
(see database of the , or ). Another
important feature is a periodic variation in Rmod in the 28–32 km range,
related to the quasi-biennial oscillation (QBO). A similar QBO signature in
the aerosol extinction coefficients retrieved from SCIAMACHY limb measurement
at altitudes around 30 km was reported by and
explained by the influence of the secondary meridional circulation. The QBO
pattern is also seen in the anomalies of w (Fig. ). Volcanic
eruptions influence aerosol w in different ways. As for Rmod there is
an increase in w after Tavurvur, Kasatochi, Sarychev, and Merapi eruptions,
while there is a slight decrease in w at some altitudes after Nabro eruption and no
change in w at all altitudes after the Manam eruption is observed.
Following the strong tropical eruptions (Manam, Tavurvur, Nabro) there is a
well-defined increase in Rmod shortly after the eruptions in the 18–21 km altitudes, while at higher altitudes (22–26 km) the volcanic
perturbation is observed with a specific time lag. This is the so-called tape
recorder effect, which is associated with the vertical transport of air
masses in the tropical stratosphere. After strong mid-latitude eruptions
(Sarychev, Kasatochi) the increase in Rmod is less pronounced as
compared to the tropical volcanoes, though there is a definite increase in
w. There are two possible explanations for this effect. First, the initial
and longer-term growth in particles is a result of the oxidation of
SO2 to H2SO4, with chemical rates depending on the physical
conditions at a given altitude and latitude. Second, during the transport of
the air masses from the mid-latitudes to the tropics the aerosol particle
size distribution is modified as a result of sedimentation of heavier
particles with large radii. Unfortunately the justification for these
hypotheses can be provided only after implementing the current algorithm for
the extratropical latitudes and modelling the eruptions accounting for the
aerosol microphysics, which is outside the scope of this study.
Monthly zonal mean values of Rmod retrieved from
SCIAMACHY limb data in the tropics (20∘ N–20∘ S).
Monthly zonal mean values of w as defined by
Eq. () retrieved from SCIAMACHY limb data in the tropics
(20∘ N–20∘ S).
Deseasonalized time series (anomalies) of Rmod retrieved
from SCIAMACHY limb data in the tropics (20∘ N–20∘ S).
Deseasonalized time series (anomalies) of w as defined by
Eq. () retrieved from SCIAMACHY limb data in the tropics
(20∘ N–20∘ S).
For a better understanding of the variations in the particle size
distribution after different volcanic eruptions, we considered in more detail
the temporal evolution of the aerosol particle size distributions at 18, 22
and 25 km altitudes after the Manam and Tavurvur eruptions. This was done
using the profiles retrieved from the individual measurements. The resulting
distributions are presented in Fig. . For ease of comparison
the distributions in the Manam series are plotted in the left column and for
Tavurvur in the right column. As in Eq. (), N
is a multiplicative factor of dndr,
N= 1 cm-3 was used for each altitude to make the
Fig. more descriptive. In each panel of
Fig. four distributions corresponding to different time lags
before and after the eruptions obtained at similar latitudes
(±2∘) are presented. There were four time slots chosen: before
the eruption (green lines), in the first months after the eruption (red
lines), more than half a year after the eruption (blue lines), and over a year after the eruption
(cyan lines). The exact dates corresponding to the curves are presented in
the legend below the panels.
Evolution of the
aerosol particle size distribution at different altitudes (18, 22, 25 km)
after the Manam (a) and Tavuvur (b) eruptions.
As mentioned above, the Manam eruption was characterized by an increase in
Rmod, but almost no change in w after the eruption in comparison to
the background conditions. This can be seen looking at the depicted
distributions. At 18 km (upper left panel in Fig. ) in the
first months after the eruption (red line) the distribution shifts to the
larger values in comparison to the distribution before the eruption (green
line). Around 7 months after the eruption (blue line), distribution
shifts to slightly smaller values, but its peak is still located at a
distinctively larger radius value than for the background distribution. At
the end of March 2006, 14 months after the eruption (cyan line), the
atmosphere at this altitude is “relaxed” and returns to a state close to that
before the eruption, albeit with a slightly larger Rmod. This can be
related to a weaker eruption of Manam at the end of February to the beginning of
March 2006 (see database of , or ). It is
worth mention that w after the eruption (red, blue, and cyan lines)
does not seem to change much in comparison to that of background conditions
(green line), but it is obvious that the distribution before the eruption
(green line) has a distinctively stronger relative contribution of larger
particles than the one after the eruption. The distributions at 22 km (middle
left panel in Fig. ) before and shortly after the eruption
look similar, with the perturbation to the particle size distribution shape
being observed 7 and 14 months after eruption (blue and cyan lines). These
time delays are attributed to the vertical transport velocity. For the same
reason, the 25 km altitude (lower left panel in Fig. )
distributions before the eruption, as well as 2 and 7 months after the
eruption are similar to each other, but the one 14 months after the eruption
is shifted to larger radii. For all distributions there is no noticeable
change observed for w.
The temporal evolution of the aerosol particle size distribution after the
Tavurvur eruption showed a different behaviour than that after Manam. At the
lowermost retrieved altitude (18 km, upper right panel) aerosol particle
size distribution shifts to the larger Rmod and gets remarkably
wider shortly after the eruption (red line). It appears that the
stratospheric aerosol formed after the Tavurvur eruption
overlapped with the one
after the weaker Soufrière Hills eruption, which occurred several months
before (see database of , or ).
Although the Soufriere Hills eruption might have influenced the observed
distributions, it is not possible to distinguish between these two eruptions
and we will consider them as one event. About 8 months after the event, the
distribution shifts to the smaller Rmod and gets narrower. After
1 year (cyan line) the distribution returns almost to the same shape as
before the eruption (green line), similar to the evolution after the Manam
eruption. At 22 km (middle right panel of Fig. ) the
distribution responds as expected with the time lag of around half a year,
and at 25 km the changes are most likely dominated by other processes and
not significant in general.
Another remarkable event was the eruption of Nabro in the middle of June of
2011. As presented by Figs. and it was
characterized by an increased Rmod and a decreased w. The latter might
be a result of significant volcanic activity preceding the Nabro eruption.
Since this eruption occurred less than a year before the connection to
ENVISAT was lost, we cannot fully track it.
Comparison with SAGE II
As mentioned in Sect. , SAGE II was one of the key instruments
providing aerosol particle size information. Operating from 1984 to 2005,
SAGE II has a 3-year overlap with SCIAMACHY, enabling us to use SAGE II as a
comparison instrument in the assessment of SCIAMACHY aerosol particle size
distribution product.
In this study the current version v7.0 of the SAGE II stratospheric aerosol
data was used . This version reports extinction
coefficients at multiple wavelengths along with the effective radius.
Assuming the unimodal log-normal particle size distribution, effective radius
can be related to the particle size distribution parameters as follows:
reff=rgexp2.5ln2(σ).
As SAGE II is a solar occultation instrument, it provides 30 profiles (both
sunrise and sunset events) per day. In contrast, SCIAMACHY is a limb scatter
instrument, providing around 1400 measurements per day. However, only
tropical and completely cloud-free profiles were used, so the resulting
sampling was much sparser. Taking into consideration this sampling, fairly
loose collocation criteria of ±5∘ latitude, ±20∘
longitude and ±24 h were used for the comparison, resulting in
57 collocations during the 3 years of SCIAMACHY and SAGE II overlap. As
SCIAMACHY vertical sampling is coarser than that of SAGE II, and SCIAMACHY
retrievals were performed on a coarser altitude grid (3.3 km compared to
0.5 km by SAGE II), SAGE II data were first smoothed to the SCIAMACHY vertical
resolution and afterwards interpolated onto the SCIAMACHY vertical grid.
Mean relative difference between the reff from SCIAMACHY and
SAGE II is presented in Fig. . The relative difference is
about 30 % in the lower altitudes, decreasing with increasing height to
less than 20 % at 26 km and around 0 % at 30 km. As it can be seen
in Fig. , where both collocated data sets are plotted
versus time, the offset between SCIAMACHY (blue dots) and SAGE II (red dots)
effective radii is constant with time, with SCIAMACHY being systematically
lower at 18 and 21.3 km. One of the possible reasons for the observed
differences is the low sensitivity of SAGE II to the particles with radius
smaller than 0.1 µm . In contrast, our
investigations (not shown here) demonstrate that, for SCIAMACHY limb
measurements, the sensitivity drops for particles smaller than
0.06 µm. This is explained by the differences in the measurement
techniques. According to text books on radiative transfer, e.g.
, the signal observed by an occultation instrument
is determined by the aerosol extinction coefficient, while for the
measurements of the scattered solar light the product of the aerosol
extinction coefficient and the scattering phase function is relevant. As the
period from 2002 to 2005 was considered to be volcanically quiescent, smaller
particles were dominant at the time, increasing the possible biases. However,
for a more detailed evaluation of this aspect, a larger number of
collocations need to be analysed. Possible reasons for the observed lopsided
differences are still under investigation.
Mean relative difference ((SCIAMACHY - SAGE II) / SAGE II)
between effective radii from collocated SCIAMACHY and SAGE II measurements.
Effective radii from collocated SCIAMACHY and SAGE II measurements
at 18, 21.3, and 24.6 km altitude.
Conclusions
In this study a retrieval algorithm to obtain two parameters of the
stratospheric aerosol particle size distribution (Rmod and σ)
from SCIAMACHY limb measurements was presented. In this retrieval a fixed
vertical distribution of the aerosol particle number density, N, is
assumed, and N is not retrieved. Wavelength dependent surface albedo is
included in the retrieval. The algorithm uses the measurements of the
scattered light at 7 wavelengths and a normalization to the extraterrestrial
solar irradiance. Investigation of the averaging kernels showed a good
sensitivity in the altitude range from 18 to 32 km for both retrieved
parameters. Synthetic retrievals demonstrated that, using the presented
algorithm for SCIAMACHY limb measurements, errors for Rmod are typically
in the range of 10–20 % for the considered unperturbed N profile (the reported
absolute error is 0.01 µm). For σ, the errors are even smaller and within 5 % (absolute error value is less than 0.07). For a perturbed N
profile, errors increase to 20 % for Rmod and to 10 % for σ. For
easier interpretation of the retrieval results, the absolute distribution
width, w, was used instead of σ. For w, the error is insignificant
(less than 2 %) for background conditions and can reach 40 % after volcanic
eruptions; however, it does not exceed 0.015 µm in terms of the absolute
differences. Implementation of this retrieval algorithm to SCIAMACHY
measurements allowed us to generate the first, and for now unique, data set
of two particle size distribution parameters from an instrument measuring
scattered light in the limb-viewing geometry. Analysing the retrieval
results, an increase in Rmod in the altitude range from 18 to 25 km
after the volcanic eruptions was identified, while w after the volcanic
eruptions did not show any distinct behaviour (can increase, decrease or
remain unchanged). The tape recorder effect or delayed response of the parameters
to the volcanic eruptions for higher altitudes was observed for both
parameters. Variations in 28–32 km altitude range in both Rmod and w
due to quasi-biannual oscillation (QBO) were identified. The retrieval
results were compared to the SAGE II data and showed an agreement within 30 %
for effective radii in the lower altitudes, improving with increasing
altitude to better than 10 % above 25 km. For all altitudes a systematic
negative bias was observed.
SCIAMACHY aerosol particle size distribution data are
available after registration at
http://www.iup.uni-bremen.de/scia-arc/.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was funded in part by the European Space Agency (ESA) through the SQWG and
SPIN projects, the German Aerospace Center (DLR) through the SADOS project, and the German
Federal Ministry of Education and Research (BMBF) through the ROMIC-ROSA
project, as well as the University and state of Bremen. We thank ECMWF for providing pressure and
temperature information. We also thank NASA for SAGE II data, which were
downloaded from the NASA Langley Research Center EOSDIS Distributed Active
Archive Center.
The article processing charges for this open-access publication
were covered by the University of Bremen.
Edited by: Andrew Sayer
Reviewed by: two anonymous referees
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