AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-4701-2016AerGOM, an improved algorithm for stratospheric aerosol extinction retrieval from GOMOS observations – Part 2: IntercomparisonsRobertCharles Étiennecharles.robert@aeronomie.behttps://orcid.org/0000-0003-3883-8821BingenChristineVanhellemontFilipMateshviliNinahttps://orcid.org/0000-0003-3800-8051DekemperEmmanuelTétardCédricFussenDidierBourassaAdamZehnerClausInstitut d'Aéronomie Spatiale de Belgique, Brussels, BelgiumInstitute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, CanadaESA European Space Research Institute, Frascati, ItalyCharles Étienne Robert (charles.robert@aeronomie.be)21September201699470147181February201616March20165August20168August2016This 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/9/4701/2016/amt-9-4701-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/4701/2016/amt-9-4701-2016.pdf
AerGOM is a retrieval algorithm developed for the GOMOS instrument onboard
Envisat as an alternative to the operational retrieval (IPF). AerGOM enhances
the quality of the stratospheric aerosol extinction retrieval due to the
extension of the spectral range used, refines the aerosol spectral
parameterization, the simultaneous inversion of all atmospheric species as
well as an improvement of the Rayleigh scattering correction. The retrieval
algorithm allows for a good characterization of the stratospheric aerosol
extinction for a wide range of wavelengths.
In this work, we present the results of stratospheric aerosol extinction
comparisons between AerGOM and various spaceborne instruments (SAGE II,
SAGE III, POAM III, ACE-MAESTRO and OSIRIS) for different wavelengths. The
aerosol extinction intercomparisons for λ<700 nm and above 20 km
show agreements with SAGE II version 7 and SAGE III version 4.0 within ±15% and ±45%, respectively. There is a strong positive bias
below 20 km at λ<700 nm, which suggests that cirrus clouds at
these altitudes have a large impact on the extinction values. Comparisons
performed with GOMOS IPF v6.01 alongside AerGOM show that at short
wavelengths and altitudes below 20 km, IPF retrievals are more accurate when
evaluated against SAGE II and SAGE III but are much less precise than AerGOM.
A modified aerosol spectral parameterization can improve AerGOM in this
spectral and altitude range and leads to results that have an accuracy
similar to IPF retrievals. Comparisons of AerGOM aerosol extinction
coefficients with OSIRIS and SAGE III measurements at wavelengths larger than
700 nm show a very large negative bias at altitudes above 25 km. Therefore,
the use of AerGOM aerosol extinction data is not recommended for λ>700 nm.
Due to the unique observational technique of GOMOS, some of the results
appear to be dependent on the star occultation parameters such as star
apparent temperature and magnitude, solar zenith angle and latitude of
observation. A systematic analysis is carried out to identify biases in the
dataset, using the various spaceborne instruments as references. The quality
of the aerosol retrieval is mainly influenced by the star magnitude, as well
as star temperature to a lesser degree. To ensure good-quality profiles, we
suggest to select occultations performed with star magnitude M<2.5 and
star temperature T>6×103 K. Stray-light contamination is
negligible for extinction coefficients below 700 nm using occultations
performed with a solar zenith angle >110∘ but becomes important at larger
wavelengths. Comparison of AerGOM results in the tropics shows an enhanced
bias below 20 km that seem to confirm cirrus clouds as its cause. There are
also differences between mid-latitude and tropical observations that cannot
yet be explained, with a bias difference of up to 25 %.
This bias characterization is extremely important for data users and might
prove valuable for the production of unbiased long-term merged dataset.
Introduction
Stratospheric aerosols are an important part of the Earth system due to their
impact on the planet's radiative balance and the crucial role they play in
heterogeneous chemistry . They can be produced either
via binary homogeneous nucleation of H2SO4 and H2O
close to the tropical tropopause (so-called background aerosols) or during
volcanic eruptions, and form the so-called Junge layer, extending from the
tropopause to approximately 35 km .
In order to better understand their behaviour and evolution, it is critical
to observe these particles globally and over an extended period of time.
Various techniques have been employed to retrieve stratospheric aerosols such
as solar occultation
e.g.,
balloon-borne measurements , satellite limb sounding ,
ground-based lidar , and twilight
brightness variation .
Another measurement technique, stellar occultation from space, was utilized
by the Global Ozone Monitoring by Occultation of Stars (GOMOS). This
instrument collected transmission spectra from the Earth's limb in the
UV–Vis–NIR, allowing the retrieval of atmospheric profiles from various
species, such as O3, NO2, NO3, as well as
aerosol extinction profiles . These
species are currently retrieved by the latest GOMOS operational data
processing algorithm , hereafter referred to as IPF
v6.01.
Recently, a new stratospheric aerosol retrieval algorithm called AerGOM,
extensively covered in a companion paper , has been
applied to the GOMOS transmission data in order to obtain improved
stratospheric aerosol profiles. AerGOM is currently the main algorithm used
to produce the stratospheric aerosol dataset for the Aerosol Climate Change
Initiative (Aerosol_CCI), an ESA project focusing on both tropospheric and
stratospheric aerosols .
The purpose of this paper is to assess the agreement and discrepancy between
AerGOM stratospheric extinction measurements at different wavelengths and
those of various spaceborne instruments that observed the stratosphere in the
same spectral range during the Envisat mission, namely SAGE II, SAGE III,
POAM III, MAESTRO and OSIRIS. Beside the general comparison between AerGOM
and other instruments, the influence of various stellar occultation
parameters such as star magnitude and temperature, solar zenith angle as well
as the spatio-temporal variability is studied.
The GOMOS instrument
GOMOS was on-board the successful ESA Environmental Satellite (Envisat)
mission. Envisat payloads gathered information about the state of the Earth's
atmosphere from shortly after its launch in March 2002 until communication
was lost with the satellite in April 2012. The GOMOS instrument functioned
almost continuously during its lifetime, except in 2005 when problems with
the instrument forced the ground segment team to switch to the redundant
measurement system due to errors with the scanning mirrors, impacting
measurements during several months .
The instrument measured the light transmission from up to 300 stars through
the Earth's atmosphere using four spectrometers covering the following
spectral regions: 248–371, 387–693, 750–776 and 915–956 nm. The
vertical sampling ranges from 200 m to 1.7 km, depending on the obliquity and
the tangent altitude of the observation.
The starlight transmission is not only affected by scattering and absorption
but also modified by refractive effects such as chromatic refraction and
refractive dilution. More problematic for the analysis of the transmission
spectra however is scintillation, i.e. random fluctuations in the measured
intensity of stellar light caused by refractive irregularities due to
atmospheric instability. Two fast photometers measuring in the blue
(473–527 nm) and the red (646–698 nm) part of the visible spectrum were
used for the scintillation correction and also provided high-resolution
temperature profiles .
Beyond these issues, the uncertainty of the retrieval is largely determined
by the temperature and magnitude of the observed star. Even bright stars are
point sources of low-intensity compared with the sun. Hence, profiles
obtained from stellar occultations have larger uncertainties compared with
solar occultation measurements. However, this drawback is compensated by the
fact that stars are abundant in the sky: 30–40 occultations per orbit have been
typically performed (compared with the 2 occultations available in the case
of solar occultation), although this number decreased to 20–30
occultations per orbit after the instrument malfunction in 2005. The retrieval of
species using stellar occultation is possible in both bright and dark limb,
but in the case of bright limb geometry, the weakness of the signal compared
to the ambient light makes the retrieval even more challenging. At this
stage, bright limb measurements are not used for the retrieval of
stratospheric aerosol extinctions.
GOMOS operational stratospheric aerosol retrieval
The GOMOS operational stratospheric aerosol extinction is retrieved in a
two-step process as described in . The first step
consists of the spectral inversion, where measured transmittance spectra at
each tangent altitude are inverted to slant path integrated column
densities/optical thicknesses (for gases and aerosols, respectively). The
second step is the spatial inversion, where the slant path columns for each
species are inverted to local concentration/extinction profiles. The spatial
inversion uses the Tikhonov altitude smoothing technique
to remove the residual scintillation
perturbations in measurements.
The current choice of the Tikhonov parameters leads to the removal of all
strong oscillations, at the cost of the vertical resolution, chosen as 4 km
.
The specification of the aerosol scattering cross section is difficult since
the aerosol content may be very different depending on the state of the
atmosphere and the nature of the dominant aerosol mode (background, volcanic,
etc.). The current (v6.01) spectral inversion assumes that the stratospheric
aerosol extinction obeys a quadratic polynomial as a function of wavelength:
β(λ)=βref(c0+c1(λ-λref)+c2(λ-λref)2),
where c0, c1 and c2 are coefficients to be retrieved, and
λref is a reference wavelength arbitrarily fixed at
500 nm. This is a versatile approach that can represent large and small
particle spectra within good approximation. Rayleigh scattering is not
retrieved directly from the measurements but removed using external European
Centre for Medium-range Weather Forecasts (ECMWF) air density data. This
approach sidesteps problems of interferences with the residual scintillation
and the spectrally similar aerosol contribution. NO2 and
NO3 are retrieved separately using a DOAS approach
.
The resulting stratospheric aerosol extinction profiles are of good quality
around 500 nm, despite being oversmoothed. At other wavelengths, the profile
quality is poor. The main reason for this is that only the coefficient c0
is directly smoothed by the Tikhonov approach. This makes the extinction very
noisy when departing from the reference wavelength.
Aerosol extinction relative error estimates for bright stars (providing the
best signal-to-noise ratio) are of the order of 30 % at 10 km, 2–10 % from
15 to 25 km, and 10–50 % from 25 to 40 km . The
extinction profiles become increasingly uncertain at lower tangent altitudes
because the transmitted light becomes weaker due to increasing atmospheric
absorption by gases, aerosols and clouds.
AerGOM stratospheric aerosol retrieval
AerGOM is an improved stratospheric aerosol extinction retrieval method
developed for the GOMOS experiment and designed to rectify some of the
problems of the operational retrieval, namely the difficulty to obtain proper
stratospheric aerosol extinction profiles at λ≠ 500 nm and the
inadequate error characterization of the extinction.
The most important improvements implemented are (1) the extension of the
spectral range used for the retrieval using information from spectrometer B1
(755–759, 770–775 nm); (2) the refinement of the aerosol spectral
parameterization using a second-order polynomial in
λ-1; (3) the simultaneous retrieval of all species (O3,
NO2, NO3, aerosols); (4) a better Rayleigh scattering
correction by considering the spectral dependence of the King factor
Fair; and (5) the inclusion of covariances
between species after spectral inversion. A detailed description of the
algorithm and its improvements is given in a companion paper
.
The main steps of the AerGOM algorithm are similar to the operational
retrieval. First, GOMOS transmittance data are read, along with the ECMWF
temperature and pressure profile coincident with the stellar occultation
measurements. Based on this data, temperature-dependent gas absorption
cross sections are calculated for each tangent height to create the spectral
matrix. One can choose either to calculate the Rayleigh scattering
contribution based on the ECMWF data or to retrieve it along with the other
species. Climatological profiles of various species are provided as a
starting point for the non-linear Levenberg–Marquardt spectral inversion,
leading to slant path integrated column densities and aerosol optical
thicknesses at each tangent height. This is finally followed by a spatial
inversion using the Tikhonov approach that leads to local aerosol
extinctions, along with the density profiles of the different gaseous species
considered. It should be noted that the Tikhonov parameters used for the
spatial inversion can be tuned to optimize the removal of residual
scintillation. In particular, imposing weak regularization to gaseous species
with respect to particulate species leads to noisier profiles for gas
concentrations but smoother and more realistic profiles for aerosols.
The improved aerosol spectral law in AerGOM is more flexible since the
polynomial can be of any degree and can be based on either λ or
λ-1. The formulation as a spectral interpolation formula between a
number of discrete extinction coefficients β(λi) that are to be
retrieved is also better conditioned and physically more clear than for the
operational retrieval.
(a) Proportion of anomalous profiles as a function of star
temperature and magnitude. (b) Median gas and aerosol extinction
profiles for normal and anomalous AerGOM retrievals calculated using
200 profiles.
For the quadratic spectral law, this gives
βaero(λ,r)=∑i=13qi(λ)βaero(λi,r)
with
qi(λ)=(λ-λj)(λ-λk)(λi-λj)(λi-λk)
with λi, λj, and λk different wavelengths to be
specified ahead of time.
Given that the aerosol spectral law chosen for the AerGOM processing is of
degree N, the AerGOM data product consists of extinction values at N+1
wavelengths but can be interpolated at other wavelengths using Eqs. () and (). The data used for the
current work are based on a quadratic polynomial in λ-1 with
350, 550 and 750 nm set as reference wavelengths.
For more details on the AerGOM retrieval algorithm, we refer the interested
reader to Sect. 3 of the companion paper .
Anomalous profiles and stellar occultation parameters
During the development phase of AerGOM, it was discovered that while the
algorithm had beneficial properties regarding the retrieval of stratospheric
aerosol profiles, it did have a drawback compared to the operational
algorithm, namely that some of the converging retrievals exhibited some
non-physical behaviour leading to incorrect retrievals of O3,
NO2, NO3 and aerosol extinction profiles. These
so-called “anomalous profiles” were mostly retrieved for occultations
carried out with either a dim (Mstar>2) and/or a
cold (Tstar<5×103 K) star, as shown in
Fig. a. A comparison of gaseous and aerosol profiles
between normal AerGOM retrievals and anomalous profiles is shown in
Fig. b. The data, calculated from the median of 200
profiles, show that anomalous profiles have no ozone, which is compensated
above 20 km by negative NO2 profiles, very large values of
NO3 and enhanced aerosol extinction.
The reason for the retrieval of such profiles by AerGOM was due to a
combination of low signal-to-noise ratio (SNR) of the transmittance at
shorter wavelengths for dim and cold stars, and an inadequate a priori of
gaseous and aerosol species. The operational retrieval sidestepped this issue
by using first a DOAS method to retrieve NO2 and NO3,
removing their contribution from the measured signal before carrying out
ozone and aerosol retrieval.
This problem has now been fixed by using full climatologies of gas and
aerosol species as a priori for the spectral inversion. However, this finding
prompted the consideration that some of the retrievals might be affected by
occultation parameters such as star properties and solar zenith angle (SZA)
that could lead to stray light, and occultation obliquity which is an
important factor in the imperfect correction of atmospheric scintillation
. Therefore, another aspect of this intercomparison
involves studying the consistency of the agreement of AerGOM aerosol
retrievals with those of other instruments under various occultation
conditions. Section 6 presents the results of these comparisons.
Characteristics
of the stratospheric aerosol extinction datasets used in this work.
Latitude and temporal coverage for the various instruments used for
the intercomparisons. The number of observations per month is calculated for
a 10∘ latitude bin. The colour code gives the number of observations
per month.
Intercomparison instruments
It has been pointed out by that aerosol validation is
challenging because there is no standard measurement with which to compare.
Occultation instruments often validated aerosol data by comparing with each
other and a small number of other space-based instruments. A difficulty
encountered with this approach is that the aerosol extinction measurements in
one experiment do not always have their spectral counterpart in other
experiments and cannot be directly compared. Another possibility would be to
perform a validation with lidar measurements. Although it could prove useful
for periods following volcanic eruptions, it would be non-trivial for periods
of low stratospheric aerosol loading, as the corresponding lidar backscatter
ratios are too small to convert them to extinction with the required
precision and/or accuracy for validation.
In this paper, we opted for the approach of comparing measurements with
multiple instruments' datasets. The power of this approach is that by
uncovering similar and/or consistent features across the many available
measurements, some sort of consensus can be reached on the agreement of the
data. The weakness of such methodology is that there is no clear independent
source of high-quality information, and consensus does not imply any form of
absolute truth.
For this work, the SAGE II, SAGE III, POAM III, MAESTRO and OSIRIS
instruments are used as a basis for the intercomparison efforts with AerGOM.
Table provides some general information about these
instruments and their respective stratospheric aerosol products.
Figure shows the spatio-temporal coverage of the datasets
used for this study. Note that the colour scale indicating zonally averaged
observations per month in a 10∘ latitude bin is different for each
experiment. There is a vast difference (a factor of 3–4) in coverage from a
limb instrument (OSIRIS) compared with solar occultation experiments. GOMOS
coverage is more extensive than what is shown in Fig.
for AerGOM, but to ensure high-quality data, all observations that could
potentially be stray-light-contaminated were filtered out resulting in a
limited coverage at high latitudes.
SAGE II
The Stratospheric Aerosol and Gas Experiment (SAGE II) on-board the Earth
Radiation Budget Satellite (ERBS) provided high-quality vertical profiles of
important atmospheric species from the mid-troposphere through the
stratosphere during a mission that lasted from October 1984 until August
2005. The instrument recorded the attenuation of sunlight by the Earth's
atmosphere in seven spectral channels between 386 and 1020 nm during each
sunrise and sunset encountered by the spacecraft. The measurements were
separated into slant path optical depth contributions for O3,
NO2, H2O and aerosol at four channels (386, 452, 525
and 1020 nm) using a least-squares technique .
In this work, we use the SAGE II v7.0 stratospheric aerosol dataset
for which the 386 nm aerosol channel is not
recommended due to some unexplained contribution that can be substantial
(approaching 30 %) at low extinction levels. Therefore, this channel is not
considered in the comparisons. Note also that the aerosol extinction
coefficient measurements at 452 nm do not reliably extend below 12 km and
will not be used below this altitude, whereas measurements made at 525 nm are
reliable in the UTLS and available as low as 5 km despite substantial impacts
by ozone absorption and molecular scattering .
SAGE III
SAGE III was launched in December 2001 on-board the Russian METEOR 3M
spacecraft. It gathered data from February 2002 until the end of the mission
in March 2006, using the technique of solar occultation. It observed the
line-of-sight (LOS) transmission profiles from 0.5 to 100 km at 87
wavelengths from the ultraviolet to the near-infrared with an estimated
0.7 km vertical resolution.
Aerosol extinction is derived in nine spectral channels by removing the
effects of molecular scattering, O3 and NO2
absorption. The precision and accuracy of the aerosol product is linked to
the measurement noise in the channel, the quality of the Global Modeling and
Assimilation Office (GMAO) density product, the noise and bias in the
retrieved O3 and NO2, and the consistency of the
cross sections used in the O3/NO2 multi-linear
regression retrieval and those at the aerosol channel wavelengths.
It was found that the aerosol extinction coefficient measurements at 448,
520, 755, 869, and 1021 nm are reliable with accuracies and precisions on the
order of 10 % in the 15–25 km range . It is
recommended to only exploit the 385 nm measurements above 16 km where the
accuracy is on a par with other aerosol channels. Aerosol measurements at
601 and 676 nm will not be used in the present study because of the large
measurement noise of the channel and poor accuracy of the retrieved
extinction, respectively.
POAM III
The Polar Ozone and Aerosol Measurement Instrument (POAM III)
was launched in March 1998 on the
Satellite Pour l'Observation de la Terre (SPOT 4) in a sun-synchronous polar
orbit.
The instrument used the solar occultation technique to measure atmospheric
transmission across nine spectral channels in the UV–Vis range. From these
measurements, O3, NO2, H2O and
O2 vertical profiles can be retrieved. Stratospheric aerosols are
also retrieved at several wavelengths (354, 439, 602, 778, 922, 1020 nm) up
to an altitude of approximately 25 km.
POAM III sunrise aerosol extinction measurements at both 1020 and 450 nm
are within ± 30 % of SAGE II. However, POAM III exhibits a significant
sunrise–sunset bias in its extinction measurements that leads to poorer
agreement between SAGE II and the POAM III sunset data. This is important for
this work since collocations with GOMOS observations are only found for
POAM III sunset occultations. POAM III sunset aerosol extinctions at 1020 nm
and 440 nm both exhibit a positive bias with respect to SAGE II, whose
magnitude changes with altitude but can be as large as 50 %.
MAESTRO
The Atmospheric Chemistry Experiment (ACE) mission was
launched on 12 August 2003 on-board the SCISAT satellite and is still
currently operational. The satellite is in a low-Earth circular orbit at an
altitude of 650 km and 74∘ inclination. The ACE mission is comprised
of two instruments: a Fourier transform spectrometer (ACE-FTS) and the
Measurement of Aerosol Extinction in the Stratosphere and Troposphere
Retrieved by Occultation instrument (MAESTRO) . The MAESTRO instrument uses the solar occultation technique
and is made of two independent spectrophotometers, one measuring in the UV
(285–550 nm, 1.5 nm spectral resolution) while the other observes in the
VIS–NIR spectral region (525–1020 nm, 2 nm spectral resolution). These
measurements allow the retrieval of atmospheric species such as
O3, NO2, H2O, O2 and
aerosols. Measurements are made at tangent altitudes between 0 and 150 km
(using measurements between 100 and 150 km to determine the sun reference
spectrum) and allow for a best-case vertical resolution of 1.2 km at a
tangent height of 22 km. The aerosol extinction is retrieved at 525, 530,
560, 603, 675, 779, 875, 922, 995 and 1012 nm wavelengths. Cirrus clouds are
not filtered from the dataset.
Though able to retrieve high-resolution aerosol extinction profiles, MAESTRO
has two issues affecting its measurements: (1) an altitude assignment problem
that can lead to outlier data and (2) an unidentified problem (suspected to be
a dark count model issue) that makes small optical depths too large.
OSIRIS
The Optical Spectrograph and InfraRed Imaging System
, on-board Odin, measures the vertical distribution
of atmospheric limb radiance spectra. The satellite was launched in February
2001 in a sun-synchronous polar orbit and continues full operation at the
time of writing. The local time of the ascending node is 18:00 LT,
providing measurements of the sunlit summer hemisphere, global measurements
during equinox, and a limited coverage of the winter hemisphere.
The two sub-systems of OSIRIS are an optical spectrograph (OS) and an
infrared imager (IRI). The optical spectrograph consists essentially of a
grating and a CCD detector, and measures the limb radiance spectra from 280
to 800 nm with a spectral resolution of approximately 1 nm
. The sampling resolution of the measurements is
approximately 2 km.
The IRI is composed of three vertical near-infrared channels that capture
one-dimensional images of the limb radiance at 1.26, 1.27, and 1.53 µm at a tangent altitude resolution of approximately 1 km. OSIRIS aerosol
data product version 5 is derived using only the optical spectrograph
measurements, and an alternate dataset (version 6) exploits both instruments
for the retrieval of the aerosol extinction profiles ,
allowing a better characterization of the aerosol scattering phase function
and improving the retrieval substantially. The drawback of this new approach
is that retrievals are noisier and have a tendency to saturate at low
altitudes and high aerosol loadings such as in the centre of volcanic plumes.
It is this latest version (v6) of the OSIRIS dataset that is used in this
study. However, the comparison results presented in this paper can be
generalized to OSIRIS aerosol extinction v5 dataset at 750 nm, as there are
very little differences between both datasets when it comes to AerGOM
comparisons.
It is important to note that the OSIRIS dataset v6 is based on measured
radiances at 750 and 1530 nm, from which an Ångström exponent is
derived. In this work, we perform comparisons with OSIRIS extinction at
750 nm but also at wavelengths outside the range used to determine the
Ångström exponent (350 and 550 nm).
Methodology
The comparison of datasets is based on the statistical analysis of collocated
events, defined here as observations within a distance Δr=500 km and
a period ±Δt=12 h from each other. Since stratospheric aerosols
are assumed to be slowly varying over time and space in the absence of
volcanic activity, these criteria are deemed acceptable. It should be noted
that processes such as pyro-convective events, other tropospheric intrusions
and polar stratospheric clouds can also significantly change the extinction
signal in the stratosphere and therefore make it more difficult to compare
data points that are further apart. The key is to strike a balance between
the proximity of observations in time and space and the number of sample
observations available for analysis. Evaluation of different collocation
criteria showed that constraining further the Δt and Δr
generally does not affect the final results but sometimes lead to
undersampling.
The relative difference between the AerGOM extinction
βAerGOM and the extinction from a collocated measurement
βi from dataset i (in %) is
100×βAerGOM-βi(λ)βi(λ).
All observations with a relative uncertainty larger than 100 % are discarded
before performing the analysis in order to avoid biasing the results due to
inferior quality data, but it should be noted that using all observations
does not alter the results significantly. The profiles are interpolated on a
common 1 km spacing vertical grid using a linear interpolation method. AerGOM
extinctions are interpolated at the wavelength(s) of the other instruments
using Eq. (). In this way, a distribution of values is
obtained for each tangent altitude z and wavelength λ and the
final results are derived from this distribution by calculating the
interquartile mean and the semi-interquartile range, which should be robust
estimates of the average value and the variability, respectively.
Comparison of collocated profiles
Figure shows the results of the intercomparison of
AerGOM against all datasets from Table using the method
outlined in Sect. . Three different aspects of the
comparison are shown: the relative difference (interquartile mean), the
relative difference variability (semi-interquartile range), and the absolute
aerosol extinction profiles of AerGOM and the other datasets (interquartile
mean). The total number of collocations is also indicated and varies widely
from one dataset to the next. To quantify the effect of the change in
retrieval algorithm from IPF to AerGOM, the comparisons were also performed
using IPF and the results are shown as dashed lines in the relative
difference and the variability plots.
Relative difference (left panels), variability of the relative
difference (central panels) and absolute aerosol extinction vertical profiles
(right panels) for each dataset (SAGE II, SAGE III, POAM III, MAESTRO, OSIRIS
and IPF) at various wavelengths compared with collocated AerGOM profiles. The
dashed curves in the relative difference plots were calculated using IPF
v6.01 instead of AerGOM. The total number of collocated profiles N is also
indicated.
AerGOM comparison with other datasets
Overall, the agreement between AerGOM and other datasets for tangent
altitudes between 15 and 30 km is typically within ±50 % for
extinctions in the 400–600 nm spectral range. The comparison with SAGE II
between 20 and 30 km shows good results, with a bias within ±15%,
which is close to the reported 10 % accuracy and precision of
SAGE II for these altitudes. Results for comparisons between
AerGOM and SAGE III are not as good, with mostly positive biases that vary
from -10 % at 20 km up to 40 % at 30 km, depending on the
wavelength. This bias is larger than the expected precision and accuracy of
the SAGE III instrument (10 % up to 25 km), indicating potential issues
with the data. It is also surprising that the 520 nm extinction is the most
biased within this altitude and spectral range, as one would expect it to be
most accurate for AerGOM.
According to the work of , the 525 nm aerosol
extinction measurements from SAGE II version 7.0 and SAGE III version 4.0
should agree to within a few percent. It is therefore puzzling to see such
differences in the intercomparisons of AerGOM with both instruments. The
reason for the discrepancy is that the SAGE II and SAGE III data are not
sampled in the same way when it comes to AerGOM collocations, with SAGE III
data found solely in the southern hemispheric mid-latitudes, whereas
collocations with SAGE II measurements are found at all latitudes. Results
from Sect. show that the bias varies based on the
latitude of observation, and when comparing results from the same latitude
bands, the intercomparisons are consistent with the results from
. For POAM III comparisons below 700 nm and above
20 km, biases are similar to what is seen in SAGE III but shifted by 15 %.
Below 20 km, comparisons between AerGOM extinction at shorter (λ<700 nm) wavelengths and SAGE II, SAGE III and POAM III extinctions show a
strong positive bias, increasing with decreasing altitude. This positive bias
is larger for shorter wavelengths. These features could be the result of
subvisible cirrus clouds present in the field of view, but it is unclear why
only these datasets are affected while comparisons with MAESTRO and OSIRIS
show no such large positive biases, and why the effect is much more
pronounced in the case of AerGOM than for comparisons with IPF. The latter
result suggests that the AerGOM retrieval algorithm itself must be the cause
of this bias, not the GOMOS instrument, despite its known decreasing SNR with
decreasing tangent altitude. Section takes a closer look
at the potential effect of clouds on the results from the perspective of
latitude of observation.
The results of the comparison between AerGOM and MAESTRO extinction profiles
at shorter (λ<700 nm) wavelengths show a different behaviour of the
relative difference than seen in the other comparisons. AerGOM is negatively
biased compared with MAESTRO, with values of the biases ranging from -35 to
-50 %. The bias is quite constant within an altitude range of 10 to 25 km.
Above 25 km, all AerGOM extinctions become increasingly negatively biased
with regards to MAESTRO with increasing altitude and wavelength. These
results seem to confirm the issues suspected with the MAESTRO dataset, namely
that it retrieves too large aerosol extinctions. Based on the AerGOM
comparison, this effect increases with the wavelength of observation. One
surprising feature of the comparison is the small variability (25 %) of the
relative difference with AerGOM, almost constant between 10 and 25 km and for
all wavelengths.
OSIRIS data at wavelengths below 750 nm are extrapolated and should be used
cautiously, but it is nevertheless interesting to see that there is a pretty good
agreement between AerGOM and OSIRIS at 550 nm, with an almost constant negative bias
of 25 % between 15 and 30 km. For shorter wavelengths however, the comparison shows
that OSIRIS extinctions are much larger than AerGOM above 20 km.
Looking at comparisons of AerGOM aerosol extinction profiles for λ>700 nm, one can see that there is clearly a problem with AerGOM results at
larger wavelengths, despite the use of GOMOS transmission data from
spectrometer B1 that should have improved the aerosol retrieval in this
spectral region. There is a strong negative bias above 25–30 km with respect
to all other datasets (especially clear with SAGE III, MAESTRO and OSIRIS)
that increases towards higher altitudes. Above 27 km, retrieved extinctions
at λ>700 nm are mostly negative, hence the large negative biases
observed. These large discrepancies could very well be due to the use of
outdated ozone cross sections. It was mentioned in
that anomalous aerosol extinctions in the SAGE III 755 nm channel from
previous versions of the dataset were caused by the use of an outdated ozone
cross section that had errors of the order of 10 % in the Chappuis band.
Preliminary work to improve the trace gas cross sections used with AerGOM
seems to confirm that such changes can lead to a significant improvement of
the aerosol extinction values for λ>700 nm, especially above 25 km.
Looking at the AerGOM absolute extinction profiles at λ>700 nm, one
notices that there are more vertical structures than for the other
wavelengths, with a small peak around 16 km, and troughs near 13 and 20 km.
Interestingly, these structures are also seen in the comparison results with
IPF at all wavelengths and, hence, seem to be somewhat linked to the
measurement method or to some aspect of the retrieval that is common to both
AerGOM and IPF algorithms.
The variability of the extinction comparisons in the 350–600 nm spectral
range increases with decreasing tangent altitudes and is larger for shorter
wavelengths. This is expected, as it simply follows the spatial and spectral
behaviour of the GOMOS SNR, and is confirmed by the similar IPF comparison
variability. The dispersion of the comparisons at λ>700 nm is less
systematic but tends to increase dramatically with tangent altitudes above 20–25 km,
correlated with the strong negative bias.
Standard deviation of the aerosol extinction relative difference for
AerGOM (solid) and IPF (dashed) comparisons with SAGE II, SAGE III and
OSIRIS. Note that the abscissa is scaled logarithmically.
For reference purposes, we also show the comparison between AerGOM and IPF profiles
in the bottom panel of Fig. . The results are based on 20 000
randomly chosen GOMOS observations spanning different geolocations and occultation parameters.
Even though the raw data come from the same instrument, the comparison shows substantial differences.
Differences between AerGOM and IPF
Figure also displays the results of the comparisons
with regards to IPF, and it is surprising to see that in some instances,
these results are not clearly favouring AerGOM over IPF. In several cases
(SAGE II, SAGE III and POAM III) and more specifically for observations below
20 km and for λ<700 nm, the IPF results are in better agreement
with the correlative measurements than AerGOM, giving rise to the question of
whether AerGOM can be considered an improvement over IPF.
show that the conceptual improvements of AerGOM are
translated into aerosol extinction profiles that are better behaved than
those of IPF v6.01, with AerGOM results being particularly less noisy than
their IPF counterparts and having a more realistic spectral dependence.
However, since the present work averages a large number of profiles to obtain
the results and does so in a robust way by using the interquartile mean and
the semi-interquartile range as a metric of the central tendency and
dispersion, respectively, the noise in the IPF data is no longer a concern.
Therefore under certain conditions, despite the fact that the IPF v6.01
aerosol dataset is noisier and hence less precise, it is more accurate than
AerGOM.
One of the great strengths of AerGOM however is its higher precision, which is
quantified in the variability. It can already be seen from Fig. that the variability of AerGOM comparison results
is typically smaller than those made with IPF, especially between 15 and
30 km. Then again, these results are only based on 50 % of the data. If
instead of the semi-interquartile range, one uses the standard deviation as a
measure of the variability, consequently using the entire distribution of
data, the real advantage of AerGOM over IPF becomes clear. Figure shows the standard deviation of the relative
difference on a logarithmic scale for comparisons of AerGOM (solid) and IPF
(dashed) with SAGE II, SAGE III and OSIRIS as a function of altitude. Not
only is the dispersion of IPF results larger than those of AerGOM below
30 km, it is not uncommon for it to be more than an order of magnitude
larger. The variability of the IPF comparisons becomes larger as the
wavelength considered is far from 500 nm, the reference wavelength for the
spectral model used in IPF.
Relative difference of aerosol extinction comparisons between a
modified AerGOM dataset using a different spectral law (polynomial of degree
1 in λ-1) and SAGE II and SAGE III datasets at different
wavelengths. These results show a better agreement between AerGOM and both
SAGE instruments below 20 km.
Of course, one would hope that a dataset is both precise and accurate, and it
is meaningful to search for ways to improve the current AerGOM dataset so
that it agrees better with SAGE II and SAGE III, both considered excellent
aerosol extinction datasets. There are several ways in which the AerGOM
algorithm settings can be modified to improve its retrieval of stratospheric
aerosols such as using improved trace gas absorption cross sections or using
a full covariance matrix when performing the inversion. But one particular
aspect of AerGOM seems to have a large effect on the aerosol extinction in
the UTLS, namely the aerosol spectral law chosen to model the aerosol
extinction cross section. The current version of AerGOM uses a second-degree polynomial in λ-1, but some preliminary results show that
using a polynomial of degree 1 instead significantly improves the results
below 20 km. These results are shown in Fig. ,
where a modified AerGOM algorithm using a polynomial of degree 1 in
λ-1 as spectral law was used to generate a new dataset that was
then compared with SAGE II and SAGE III. The improvements below 20 km are
clear, with biases being limited to within 25 % above 12 km for SAGE II, and
within ±50% for SAGE III. While more work is needed, this seems to show
that slight modifications to the algorithm settings can lead to results that
are more in line with those of the SAGE instruments.
Bias variability with star and occultation parameters
Section described AerGOM's bias relative to other
instruments, but it did not take into account the very specific features of
GOMOS which do not concern the other sensors but may dramatically affect the
quality of AerGOM extinction. Specifically, the use of a wide range of
stellar sources with very different characteristics, the subsequent low value
of the SNR, and the versatility of the occultation configuration reflected in
the obliquity and the solar zenith angle may all affect the GOMOS
measurements.
The purpose of this section is to perform a more detailed analysis and assess
whether occultation parameters may affect the extent and consistency of the
bias between AerGOM and other instruments. For each instrument, comparisons
were carried out as explained in Sect. , except that
only a specific subset of collocated profiles corresponding to particular
criteria was used to calculate the interquartile mean. The parameters under
investigation are star properties, solar zenith angle and latitude of
observation. A study of the effect of the obliquity on the bias was carried
out, but the results did not bear any concluding evidence of a repercussion
on the AerGOM measurements and were therefore omitted from the discussion.
Note that this analysis is only valid for the AerGOM retrieval and cannot be
generalized to the IPF dataset. Due to the very large variability of the
comparisons between POAM III and AerGOM observed in the last section,
POAM III results are not included in this part of the work.
Studying the effects of a given occultation parameter or star property
assumes that only one variable will change while all other parameters are
constant, but this is not always the case for GOMOS. Most parameters are
somewhat interdependent, albeit very loosely in some cases. Figure gives an overview of the interdependence of
several parameters: star magnitude, star temperature, latitude and SZA. The
figure shows 2-D histograms of the number of observations for different
combinations of these occultation parameters, taking into account only dark
limb GOMOS observations. From these graphs, one can see for instance that at
low SZA (≤120∘), almost no bright stars are available and mostly
only dim stars will be used for occultation. Maybe the clearest and most
important dependence among the occultation parameters is observed for solar
zenith angles and latitudes of observations, where low SZA values correspond
to high latitudes and equatorial observations to high SZA values. We must
therefore be cautious when analysing the results of comparisons with regards
to certain occultation parameters and take into account this interdependence.
Interdependence of the GOMOS occultation parameters depicted using
2-D histograms of the number of observations for different combinations of
occultation parameters (star temperature and magnitude, solar zenith angle
and latitude of observation).
Star properties
The properties of stars (star temperature Tstar and magnitude
Mstar) used as light source by GOMOS largely determine the
shape of its spectral irradiance: cold (hot) stars have larger spectral
irradiance at longer (shorter) wavelengths. In addition, the magnitude of the
star might mitigate or aggravate the impact of the shape of the spectral
irradiance on the quality of the retrieval by altering further the SNR in
different spectral regions. In particular, it could be expected that dim
stars seriously affect results at short (long) wavelengths for cold (hot)
stars. Table details the nine distinct categories
of stars that we have defined for this work and that we will consider,
ranging from dim and cold to hot and bright.
Classes of star
properties (as defined in this work).
Star temperaturesDescriptorStarDescriptor103 Kmagnitudes0–6cold-1.5–1.5bright6–26mid-cold1.5–2.3mid-bright26–40hot2.3–3dim
Figure presents the results of the comparisons
between AerGOM and the other datasets at various wavelengths and according to
the defined star classes. The rightmost panel shows the number of
observations available to perform the comparisons. Note that in some cases,
the number of observations is very limited so that the effects seen in these
cases may be strongly affected by subsampling.
There is a clear departure from the consensus across the various experiments
at long wavelengths (> 700 nm) for dim and hot star occultations. More
particularly, the effect is visible for occultations using stars with
Mstar>1.5, except for cold stars. In these cases, AerGOM is more
negatively biased than usual, starting around 25 km and worsening towards
lower altitudes. The star magnitude plays a major role on the bias
variability for hot stars. Case in point, for the AerGOM–OSIRIS comparison at
750 nm, the bias can vary from -300 % for dim–hot stars to +50 % for
bright–hot stars at 17 km.
The occultation star properties have the largest influence on AerGOM
extinctions when considering dim–hot stars, especially for AerGOM aerosol
extinction at wavelengths larger than 650 nm. The effect can also be seen at
550 nm but to a more limited extent. For SAGE II (452, 525 nm), SAGE III
(520 nm), MAESTRO (525 nm) and OSIRIS (550 nm), AerGOM is more negatively
biased between approximately 15 and 20 km.
At shorter wavelengths (< 400 nm), the AerGOM comparison for cold star
occultations also shows a different behaviour with respect to the other star
property comparisons, but the effect is much less dramatic and not
consistent across the various datasets. For SAGE III, dim–cold star
occultations are very negatively biased between 17 and 23 km with regards to
the other occultations but are positively biased between 25 and 35 km.
Overall, in this short wavelength range, the weakness of the signal from
dim–cold stars is responsible for the erratic behaviour of the bias profile
at the lowest altitudes toward the troposphere.
Relative differences between AerGOM and SAGE II, SAGE III, MAESTRO
and OSIRIS datasets for different star categories, ranging from dim–cold to
bright–hot (left panels). The rightmost panel presents the number of
observations for each dataset comparison and star category.
Solar zenith angle
Another parameter studied is the solar zenith angle at which the stellar
occultation was carried out. SZA is an indicator of the local time, although
there is no reason to believe that this can affect the extinction
comparisons. Here, we assume that the dependence of the AerGOM results on the
SZA, if any, is due to stray light as there is a larger probability that
stray light finds its way into the instrument as the SZA decreases.
The AerGOM aerosol retrieval is only carried out for observations made in
partially or completely dark limb, meaning that the SZA of observations will
vary between 100 and 180∘. There is already a stray-light
correction applied to GOMOS Level 1 product, but an evaluation of the AerGOM
dataset seems to show some residual stray-light contamination in the data.
Figure shows the results of the comparisons between AerGOM
and other datasets according to different values of the SZA. The common
feature to these comparisons is that AerGOM is indeed more negatively biased
for low SZA, but this is only clearly visible for wavelengths larger than
600 nm. The impact of the SZA on the bias seems to be progressive, which is
clear when looking for instance at the AerGOM–OSIRIS comparisons at 750 nm.
The SZA impacts the comparisons mostly above 15 km, although this could be
linked to poor sampling below that tangent altitude. The effect of the SZA on
the comparisons at wavelengths around below 600 nm seems to be minor for most
altitude of observations but gains in importance above 27 km.
These results strongly suggest the presence of stray light, as it should
increase the number of photons detected by the spectrometer, hence
artificially increasing the value of the transmission and decreasing the
retrieved extinction. If this decrease in extinction is attributed to the
aerosol (which has a slow varying spectral dependence unlike ozone,
NO2 and NO3), then the comparison should show a
decrease of the positive bias. If we assume the stray light to be more or less
constant with altitude, its relative effect should be larger at high
altitudes (>25 km) and longer wavelengths, due to the generally much
smaller aerosol extinction values typically found for such cases.
Relative differences between AerGOM and SAGE II, SAGE III, MAESTRO
and OSIRIS datasets for different categories of SZA, ranging from 110
to 180 in 10∘ increments (left panels). The rightmost panel
presents the number of observations for each comparison and SZA category.
Latitude
The latitude of the occultation can strongly affect comparisons, especially
because cloud phenomena are involved. At high latitudes, polar stratospheric
clouds (PSCs) can affect the mean extinction profiles if not filtered out,
while in the tropics and mid-latitudes, high-altitude cirrus clouds can have
an impact on the lower stratospheric extinction. Note that, for the
comparisons performed in this work, no filtering of cirrus clouds or PSCs was
carried out. It should also be mentioned that there could be an indirect
effect of the latitude on the extinction profiles due to the strong
correlation between SZA and latitude (see Fig. ). The influence of SZA on the extinction has
already been examined in Sect. .
Relative differences between AerGOM and SAGE II, MAESTRO and OSIRIS
datasets for different categories of latitudes of occultations, based on
30∘ latitude bands (left panels). The rightmost panel presents the
number of observations for each comparison and latitude band.
Figure shows that, for SAGE II, MAESTRO and OSIRIS,
there are some effects on the comparisons pertaining to the latitudes at
which the occultations were made. The most prominent effect is seen in the
tropics, for latitudes between -30 and 30∘, where the
comparisons show an increased bias between 15 and 20 km at all wavelengths.
Interestingly, also reported similar biases in the
lower stratosphere when comparing SCIAMACHY and SAGE II stratospheric aerosol
extinction, although the bias increased with decreasing altitudes. This seems
to indicate that cirrus clouds do have an impact on the comparisons. For
OSIRIS, the aerosol data are not cloud filtered per se but they do provide a
dataset that is restricted to the stratosphere by cutting off profile
information at the tropopause, which effectively work as a cloud filter in
the tropics and might explain the positive bias between AerGOM and OSIRIS.
For the other datasets, the reason for the fact that AerGOM is more
positively biased is unclear.
There is also a marked difference at all altitudes between observations
carried out in the tropics and those made at mid to high latitudes.
Comparisons with SAGE II show much more variability across the mid-latitude
occultations above 20 km. For the MAESTRO comparisons, there is an almost
constant bias difference of 25 % between tropical and mid-latitude
observations at shorter wavelengths. These discrepancies cannot be explained
only by a difference in sampling, nor due to the SZA as it was shown that
stray-light contamination does not affect AerGOM measurements below 700 nm
significantly. There is also no indication that PSCs have an impact on the
comparisons. The AerGOM–OSIRIS comparison at 750 nm also shows a notable
difference for the Southern Hemisphere (latitude ≤-30∘) compared
with the other latitude bands, with AerGOM data being more negatively biased
in these cases. This effect is also seen at shorter wavelengths between 12
and 20 km but to a lesser extent. However, these differences could well be
explained by the low SZA values of the observations in these categories,
since a majority of observations were made with a SZA ≤ 120∘,
which was shown to affect the AerGOM larger-wavelength aerosol extinctions
significantly.
No results are shown for SAGE III because the collocations with AerGOM were
only contained within one latitude band.
Conclusion
In this study, we compared the results of stratospheric aerosol extinction
coefficients retrieved by AerGOM in the UV–Vis with several datasets
observing in the same spectral range and during the same period as GOMOS.
Overall, the intercomparisons with almost all sensors show an agreement
within ±50 % in the 400–600 nm spectral range, between 20 and 30 km.
More specifically, the agreement in this spectral and altitude range with
SAGE II version 7 and SAGE III version 4.0 is within ±15% and ±45%, respectively. There is a strong positive bias below 20 km at λ<700 nm, consistent with the presence of cirrus clouds at these altitudes.
It is shown that IPF v6.01 results are not impacted as much under these
conditions and are more accurate than AerGOM, despite a much lower precision.
Using a different spectral parameterization of the aerosol extinction
cross section based on a degree 1 polynomial in λ-1 can improve
the comparison results below 20 km significantly, and results obtained in
this manner show agreement with SAGE II and SAGE III that are on a par with
IPF. Due to AerGOM aerosol extinction strong negative bias observed at
wavelengths larger than 700 nm when compared against SAGE III and OSIRIS
datasets at altitudes above 25 km, we do not recommend using AerGOM version
1.0 extinction data in this spectral range. The reason for this discrepancy
is not clear, but preliminary work suggests a wrong attribution of extinction
to aerosol and gases, which could be improved by the use of better trace gas
cross sections, especially ozone.
Section covered different aspects of the
AerGOM data comparisons with other datasets as they related to the occultation
parameters. It was shown that the quality of the retrieval is mainly
influenced by the star parameters that directly impact the SNR of the
measurement. The dominant parameter is the star magnitude quantifying the
strength of the star signal, and we suggest to use a threshold of M=2.5 in
order to obtain high-quality profiles. Hot stars perform better than cold
stars and the recommended threshold is T=6×103 K. Another important
aspect that influences the quality of the retrieval is the SZA. Using a
threshold of 110∘ gives good-quality results for extinction at
λ<700 nm, but at longer wavelengths, one should use a threshold
value of 130∘. The influence of the latitude of observation on the
bias is probably partly related to cloud detection that is not performed yet
in AerGOM, in contrast to some of the other algorithms. The signature of
cirrus clouds is clearly seen in the tropical region. There is also a
difference in the overall bias if one considers tropical observations and
mid-latitude occultations that cannot be explained at the moment. No
influence of PSCs on the bias has been identified. Finally, the
intercomparison between AerGOM and OSIRIS shows a particular behaviour in the
13–17 km altitude range, with an increasing negative bias polewards. This is
probably due to the poor statistics in this case, and to the
over-representation of very low values of the SZA and of dim stars in these
regions.
These results can prove useful as guidelines to AerGOM data users as they
shed light on the aspects of the occultations which might affect the results
systematically.
Data availability
Data for the AerGOM v1.0 processing version are stored in Network Common Data
Form version 4 (NetCDF4) format and can be obtained by contacting Christine
Bingen (Christine.Bingen@aeronomie.be).
Acknowledgements
This work was supported by a Marie Curie Career Integration Grant within the
7th European Community Framework Programme under grant agreement no. 293560,
the European Space Agency as part of the Aerosol_cci project and the Belgian
Space Science Office (BELSPO) through the “Chercheur Supplementaire”
programme. The AerGom project was financed by the European Space Agency
(contract number 22022/OP/I-OL). We would also like to thank the associate
editor, the anonymous reviewers and Steffen Dörner, whose comments helped
considerably improve this paper.
Edited by: M. Penning de Vries
Reviewed by: S. Dörner and two anonymous referees
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