AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-3107-2015GOMOS bright limb ozone data setTukiainenS.simo.tukiainen@fmi.fihttps://orcid.org/0000-0002-0651-4622KyröläE.https://orcid.org/0000-0001-9197-9549TamminenJ.https://orcid.org/0000-0003-3095-0069KujanpääJ.https://orcid.org/0000-0002-7878-7515BlanotL.Finnish Meteorological Institute, Earth Observation Unit, Helsinki, FinlandACRI-ST, Sophia Antipolis, FranceS. Tukiainen (simo.tukiainen@fmi.fi)5August201588310731155December201427January201526June201510July2015This 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/8/3107/2015/amt-8-3107-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/3107/2015/amt-8-3107-2015.pdf
We have created a daytime ozone profile data set from the measurements of the
Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument on board
the Envisat satellite. This so-called GOMOS bright limb (GBL) data set
contains ∼358 000 stratospheric daytime ozone profiles measured by
GOMOS in 2002–2012. The GBL data set complements the widely used GOMOS
nighttime data based on stellar occultation measurements. The GBL data set is
based on the GOMOS daytime occultations but instead of the transmitted star
light we use limb-scattered solar light. The ozone profiles retrieved from
these radiance spectra cover the 18–60 km altitude range and have
approximately 2–3 km vertical resolution. We show that these
profiles are generally in better than 10 % agreement with the NDACC
(Network for the Detection of Atmospheric Composition Change) ozonesonde
profiles and with the GOMOS nighttime, MLS (Microwave Limb Sounder), and
OSIRIS (Optical Spectrograph and InfraRed Imager System) satellite
measurements. However, there is a 10–13 % negative bias at 40 km
altitude and a 10–50 % positive bias at 50 km for solar zenith
angles >75∘. These biases are most likely caused by stray light
which is difficult to characterize and to remove entirely from the measured
spectra. Nevertheless, the GBL data set approximately doubles the amount of
useful GOMOS ozone profiles and improves coverage of the summer pole.
Introduction
The GOMOS (Global Ozone Monitoring by Occultation of Stars)
instrument on board the Envisat satellite uses the stellar occultation
technique for monitoring ozone and other trace gases in the
middle atmosphere . Envisat operated from
2002 to 2012 and during that time GOMOS measured altogether around
880 000 occultations. While the GOMOS nighttime ozone profiles
have generally less than 5 % bias in the stratosphere
, the majority of
the daytime occultation profiles are poor due to weak signal to
noise ratio . For this reason, the GOMOS
daytime occultation profiles have not been used in scientific
studies.
To improve the GOMOS daytime ozone profiles,
suggested using atmospheric limb radiance of scattered sunlight
instead of star spectra for the daytime retrievals. GOMOS
measured limb radiances above and below the occulting star using
a separate optical path so that the star and the limb
contributions can be distinguished from each other (see
, for a detailed description of the
instrument). In principle, the subtraction of the pure limb
signal from the central band, containing both the star and the
limb contribution, should produce an uncontaminated star
spectrum. However, it seems that this removal leads to large
and poorly understood uncertainties in the daytime transmission
spectra, ruining the operational GOMOS occultation retrieval that
works fine for the nighttime data.
In their study, used an optimal-estimation-based method to
retrieve ozone from the limb scatter radiances and reported up to 10–15 % agreement
with the reference data from Stratospheric Aerosol and Gas Experiment II (SAGE II)
between 25–53 km. The authors retrieved separate ozone profiles
from both bands (upper/lower) and obtained consistent results.
Following the promising early results of ,
developed an alternative method for
retrieving ozone profiles from the GOMOS limb scattered
radiances, or GOMOS bright limb (GBL) measurements as they are
referred to from now on. This paper is a continuation of that study. We
have processed all GOMOS daytime measurements and in this study
we estimate the quality of this novel data set.
We also tested the sensitivity of the retrieval method
to the selected spectral band and decided to use the
lower band radiance to process the GBL data set.
Another important decision is the choice of
the stray light removal method (GOMOS daytime radiances are badly contaminated by stray light).
In this work, we adapted a simple method
that estimates the average stray light from the high tangent
altitude GOMOS spectra.
The structure of
this paper is as follows. In Sect. 2 we describe the retrieval
method, test its sensitivity, and explain some general
aspects of the GOMOS daytime data.
In Sect. 3 we describe the correlative data sets used to
validate the retrieved GBL ozone profiles, present the comparison
method, and show the results of the comparisons. In Sect. 4 we
conclude our study and discuss the results.
GOMOS bright limb data
The GOMOS bright limb data set consists of ∼358 000 limb-scattered radiance spectra measured around 10:00 LT
between March 2002 and April 2012, from the launch of Envisat to the communication
failure that stopped the mission. From these measurements we
have retrieved vertical ozone profiles in the 18–60 km
altitude range.
The retrieved profiles have approximately
2–3 km vertical resolution. The data are processed
using the ESA IPF (Instrument Processing Facility) Level 1 version 6.01 and the current GBL
Level 2 version 1.2. The Level 2 retrieval scheme is based on
with a few modifications. We describe the
retrieval method briefly below.
One GOMOS limb “scan” includes typically 120–140 individual
radiance measurements at different tangent heights.
Since GOMOS records two separate radiance spectra at each tangent
height, above and below the central band (which collects the combined
star and limb signal), there are actually twice as many spectra.
The GBL data set was processed using the lower band
radiances but the upper band, or possibly a combination of both
bands, could be used as well. The upper and lower band radiances
are separated by around 1.5 km in tangent height. One
particular advantage of GOMOS is that the tangent height
registration is very accurate: on the order of 30 m.
Stars are point sources and their
positions are well known. Because the GOMOS central band always follows
the occulting star, the uncertainty in the tangent height,
which is often a significant problem in limb scatter satellite
observations, is a negligible issue in the GOMOS retrievals
.
In the retrieval, the model atmosphere is discretized
into homogeneous layers whose center point heights match the
tangent heights of the corresponding radiance measurements.
We use an onion peeling retrieval approach
to estimate trace gas densities, starting from the topmost layer
used in the retrieval (at ∼60 km) and proceeding
layer by layer towards the bottom layer (at ∼18 km).
At each layer, we minimize the cost function
χ2(z)=[H(λ,z)-M(λ,z)]C-1H(λ,z)-M(λ,z)T,
where M is the (stray-light-corrected) radiance
measurement at layer z and a function of wavelength λ.
It is normalized with the first measurement below 47 km
of the same scan. The diagonal uncertainty covariance
matrix C includes the standard deviation of the
measurement error. Currently, no modeling error is assumed, see
details in . The modeled radiance,
H(λ,z)=R(λ,z)Iss(λ,z,ρ)Iref(λ),
consists of the modeled total to single scattering ratio
R, calculated in advance as a look-up table, and the single scattering
radiance Iss divided by the modeled reference
spectrum Iref.
R depends only weakly on the actual trace gas profiles, allowing us to keep
it fixed during the fitting process.
With this assumption, we only need to solve the single scattering
radiance Iss which
can be effectively calculated using simple numerical integration.
The reference spectrum Iref is estimated using
neutral air density from the ECMWF (European Centre for Medium-Range Weather Forecasts) model analysis data (MSIS-90 climatology
above 1 hPa) and climatological trace gas profiles.
The retrieved gas densities,
ρ, include ozone, aerosols, and neutral air. NO2 is
taken from a climatology and kept fixed. The NO2 climatology
is based on OSIRIS (Optical Spectrograph and InfraRed Imager System)
data . Aerosol
scattering is modeled with the Henyey–Greenstein phase function
and aerosol extinction is modeled with the
Ångström λ-1 law. Rayleigh scattering is assumed
for neutral air. The minimization of χ2 in
Eq. () is done with the Levenberg–Marquardt
method. The error covariance matrix of the retrieved densities
is estimated at the minimum assuming Gaussian posteriors:
Cr=(J′J)-1χ2(n-p),
where J is the Jacobian, n is the number of spectral
points in the fit, and p is the number of retrieved gases. The error
estimates of the retrieved densities are the square roots of the diagonal elements
of Cr.
An example of the ozone profile errors is shown in the left panel of Fig. . The relative error
(error/density×100 (%)) is 2–15 % depending on the altitude,
which is quite a typical range of error values for stratospheric ozone profiles.
Scaling the covariance
matrix with the reduced χ2 in Eq. () leads to more realistic
error bars for the profiles. In theory, the reduced χ2 should be unity but
the average χ2 of GBL is around 0.5 between 20 and 45 km and the scaling is needed
(Fig. , right panel). The GBL χ2 values of less than unity indicate
some issue in the measurement error characterization.
Interquartile range of the relative error (left) and reduced
χ2 (right). Data from the tropics, year 2004.
GOMOS daytime radiances are significantly contaminated by stray
light, especially at visible wavelengths and at high tangent
altitudes. For example, below 40 km at 500 nm the stray
light accounts roughly for a few percents of the signal but already
several 10 % at 60 km. We remove the stray
light by calculating the average spectrum above 100 km
and by subtracting this constant spectrum from each tangent
height. The removal is done independently for each scan. This
approach ignores a possible altitude dependence of the stray light
but, on the other hand, is a simple and robust method. To avoid
using the most corrupted wavelength regions, we use three
different sets of retrieval wavelengths depending on the tangent
height (Table ). This reduces bias due to
inconsistencies in the GOMOS spectra but also introduces
a discontinuity at 40 km where we start using only
visible wavelengths. We reduce the discontinuity by scaling the
amount of stray light (at layers below 40 km) by an
iteratively found constant factor, requiring that the ozone
profile remains smooth in the 40 km transition.
We tested the sensitivity of the GBL ozone to the two important
assumptions in the retrieval: the choice of the
charge-coupled device (CCD) band of the spectrometer (upper
or lower) and the stray light correction method. The test data
included 10 orbits (142 scans) from 1 April 2004. The two
available CCD bands yield ozone profiles within 1 %
(Fig. left panel). The difference is calculated,
after linear interpolation in altitude, as
(upper–lower) / lower ×100 (%). This
figure visualizes the propagation of the random measurement error
in the GOMOS limb retrieval. For the stray light correction, we
tested two methods: the constrained extrapolation method
introduced in and the simple average
method described above. The difference in the retrieved ozone
profiles is below 1 % (Fig. right panel). The
difference is defined as
(average–constrained) / constrained ×100 (%). As both methods produce, at least in this case,
almost identical ozone profiles it is probably better to use the
average method because of its simplicity.
Sensitivity of GBL ozone profile to CCD band (left) and stray light
correction method (right). Shown are the mean (solid line) and standard
deviation (shaded area) of the relative individual differences of the
retrieved profiles in both cases. See text for details.
Each GBL Level 2 file contains geolocation information, ECMWF
model values for the temperature and density, a fixed NO2
profile (from the OSIRIS climatology), and residuals of the
fit. Densities and error estimates are provided for the three
simultaneously retrieved species: ozone, aerosols, and neutral
air. In this paper we show only the results related to the ozone
profiles. Figure shows the number of GBL profiles
and GOMOS nighttime occultation profiles during the whole
Envisat mission. The GBL data set roughly doubles the amount of
useful GOMOS ozone profiles. Figure shows
a typical 1-day coverage of GOMOS day and night measurements,
and Fig. shows the number of GBL profiles during
1-year (2004) as a function of latitude. The GBL data
complements the night occultations in the tropics and mid
latitudes and furthermore expands the global coverage towards the
summer pole.
Number of GOMOS nighttime and GBL measurements (weekly).
Typical 1-day coverage of GOMOS night occultations (red) and GBL
(blue). Data from 15 December 2004.
Figure shows an example of the zonally averaged
vertical distribution of ozone from the GOMOS nighttime
occultations, GOMOS daytime occultations, and GBL. The
measurements are from January 2005 and from the latitude
35∘ N. The nighttime data are from the star number 2
and the daytime data from the star number 175. As practically
always, the shapes of the day occultation profiles are
significantly different than the shapes of the GBL and night
occultation profiles. The huge fluctuations and large negative
values seen in the day occultation profiles are not realistic. At
least in the stratosphere, the quality of the day occultation
profiles is clearly inferior to the GBL and GOMOS nighttime
data.
Correlative data sets
First, we compared the retrieved GBL ozone profiles against NDACC
(Network for the Detection of Atmospheric Composition Change)
balloon-borne ozonesonde measurements. The NDACC data can be downloaded from
http://www.ndacc.org and the stations that were used in the
comparison are listed in Table . While ozonesondes
typically reach only about 30–35 km altitude,
they measure tropospheric and lower stratospheric ozone with very
good vertical resolution. Also, in general, the accuracy and
precision of ozonesondes is at least as good as satellite
measurements.
To validate the GBL profiles also for altitudes above
35 km, we compared the GBL data against satellite
measurements from the GOMOS nighttime occultations, MLS
(Microwave Limb Sounder) on EOS (Earth Observing System) Aura , and
OSIRIS on Odin .
Number of GBL measurements as a function of latitude and time during
2004.
Example of the vertical distribution of ozone from the GOMOS
night/day occultations and GBL. Data from January 2005, latitude
35∘ N (zonal medians and interquartile ranges).
For GOMOS night occultations, we used the latest Level 2 version
6 data. Because the occultation retrieval from cool and weak
stars is also difficult due to low signal to noise ratio and
often results in mediocre nighttime occultation profiles see the data
disclaimer,
we screened out these “bad star” profiles from the comparison.
NDACC stations that were used for different latitude zones.
MLS on board the Aura satellite, launched in July 2004, uses
thermal infrared emission to measure the atmosphere between
∼0 and 90 km. MLS measures globally during the day and
night, and the accuracy of the MLS ozone profiles is estimated to
be better than 5 % in the stratosphere
. In this comparison we used the MLS
Level 2 version 3.3 data . MLS ozone
profiles are mixing ratios as a function of pressure while the
GBL profiles are number densities as a function of altitude.
Thus, we converted the GBL densities to mixing ratios and
pressures using the ECMWF model analysis data (below 1 hPa)
and the MSIS-90 climatology (above 1 hPa), which are included
in the GOMOS Level 1 data.
It is convenient to use the neutral density data from
the Level 1 product, especially as the ECMWF data below
1 hPa (∼50km) are generally
very accurate (errors of less than 1 %).
The MSIS-90 climatology, merged with the ECMWF data, is less
accurate though. Nevertheless, we estimate that the accuracy of the
GOMOS Level 1 neutral air product between 50 and 60 km is
still better than approximately 5 %.
OSIRIS is a UV/visible spectrograph, including a near-infrared
imager, on board the Odin satellite. OSIRIS measures the
atmosphere using the limb scatter technique, measuring scattered
sunlight and scanning Earth's limb between
∼ 10 and 100 km.
In this work, we have used two different OSIRIS ozone profile products.
The University of Saskatchewan's OSIRIS ozone product is retrieved
from OSIRIS data using the SaskMART Saskatchewan multiplicative algebraic reconstruction
technique;. The product has been the target in several
validation studies e.g.,. In this study we use the
version 5.07 of these data. The Finnish Meteorological Institute's (FMI) OSIRIS ozone
product is retrieved using the modified onion peeling method , which is
similar to the method used in this work. The present version is 3.2. The two available OSIRIS
ozone products agree with each other within a couple of percents.
Comparison method
For each co-located profile pair we calculated the difference as
Δ=XGBL-XrefXref×100(%),
where XGBL is a GBL profile and Xref is
a reference (NDACC, GOMOS night, MLS or OSIRIS) profile. The
coincidence criteria for each instrument are shown in
Table . To estimate the bias against GOMOS night,
MLS and OSIRIS, we calculated the median of the individual
relative differences using 10∘ latitude zones between
70∘ S and 70∘ N. With the NDACC soundings we
used only six latitude zones (see Table ) because
the latitude coverage of the NDACC sounding stations is much
sparser than of the polar orbiting satellites especially in the
Southern Hemisphere. We also calculated the bias against GOMOS
night occultations as a function of solar zenith angle.
Coincidence criteria for the GBL and correlative measurements.
In the median calculation, we used coincidences from all
overlapping years. Possible year-to-year differences due to e.g., aging
of the instruments were found insignificant (a few percents
with no clear pattern) compared to the other sources of bias.
In addition, the vertical resolutions of the four different
satellite ozone products are similar (2–3 km for GBL,
GOMOS night, and OSIRIS, and ∼3km for MLS).
Therefore, these profiles were compared without any vertical
smoothing. The vertical resolution of GBL is determined
by the field of view of GOMOS and the movement of the
satellite during the measurement. These lead to an ∼2km
theoretical resolution in the GBL product, which is further lowered
to ∼2–3 km due to the retrieval method, according to our estimate.
In the GOMOS occultation retrieval,
the resolution is fixed to 2–3 km (depending on the altitude)
using the Tikhonov regularization and target resolution technique .
The vertical resolutions of the OSIRIS and MLS ozone products are very
close to the GOMOS resolutions and the marginal resolution differences
do not seem to cause any notable issues in the comparisons.
The NDACC ozonesonde profiles, which have significantly better
vertical resolution, were smoothed to the
approximately same resolution with the satellite products using a Gaussian filter.
Interquartile ranges (shaded areas) and medians (solid lines) of the
individual relative differences of GBL ozone profiles against NDACC
ozonesondes for different latitude zones. The numbers are the amount of
co-located points at each altitude.
Some of the GBL profiles include outliers especially at the
lowermost retrieved altitudes. This measurement is often
corrupted because GOMOS has lost the tracking of the star. Before
comparisons, we screened the GBL data with the following criteria:
always remove the lowest retrieved point;
remove points with the reduced χ2>10;
remove points below 35 km where the relative error
O3(error)O3(density)>0.1,
where O3(error) is the error estimate of the
retrieved density.
On average, the χ2 screening removes ∼0.3 points per profile and
the error screening removes ∼1.3 of the remaining points per profile.
This kind of screening significantly improves the agreement in
the 20–25 km range against all studied reference data.
Results
Figure shows the result against NDACC
ozonesondes for the six latitude zones. Shown are interquartile
ranges and medians of the differences. The numbers represent the
numbers of co-located pairs at each altitude. In these
comparisons the median difference is always less than 10 %
except latitudes 60–90∘ S where the negative bias is more than
-20 % at 19 km.
Median relative differences of GBL ozone profiles against GOMOS
nighttime occultations for different solar zenith angles.
Figure shows the comparison against GOMOS
night occultations for different solar zenith angles. There is
a clear positive bias at around 50 km when the solar
zenith angle is 75∘ or larger.
With smaller solar zenith
angles the differences are rather similar. The large solar
zenith angle observations account for 16 % of the GBL data
and the GOMOS measurement geometry is such that these
observations appear mostly at mid and high latitudes. We suspect
that the 50 km bias is linked to stray light and its
removal. GOMOS daytime data suffer from serious stray light
contamination and in particular the upper altitudes are sensitive
to the accuracy of the removal method. We also note
that the solar zenith angle seems to be the only parameter that
clearly correlates with the 50 km bias. Some good
individual profiles can be found even for high solar zenith angles
but there is no clear pattern, or too few profiles to draw
conclusions.
Because the majority of the GBL measurements with a large solar
zenith angle are distinctly biased at 50 km, in the remaining
comparisons we only used the GBL data with the solar zenith angle
smaller than 75∘.
Figure shows the difference in 10∘
latitude bins against GOMOS night, MLS, and OSIRIS
measurements. The most distinctive feature is the 10–15 %
negative bias at around 40 km, which is present in all
comparisons. A few percent of this difference can be explained
by the diurnal variation of ozone. The GOMOS day measurements
are made around 10:00 LT, which is in the minimum of
the diurnal curve . In addition, the MLS
afternoon measurements are made around 14:00 LT, which is in the
quite recently discovered afternoon maximum of ozone
. We estimate
that the diurnal variation explains about 3 % of the
40 km bias against MLS and around 1–2 % against
GOMOS nighttime and OSIRIS. Another notable structure is the
negative bias at southern mid/high latitudes, which is several
10 % at 19 km. Large biases exist at tropic/sub-tropic regions below 21 km but the deviations are large
too. Remaining differences are below 10 % (and often
<5 %).
Median relative differences of GBL ozone profiles against GOMOS
nighttime occultations (upper left), MLS (upper right), OSIRIS University of
Saskatoon version (lower left), and OSIRIS FMI version (lower right).
Discussion and summary
We have processed and released a GOMOS bright limb ozone data
set. This data set roughly doubles the number of useful GOMOS
ozone profiles. In general, the bias in the GBL ozone is less
than 10 %, and often less than 5 %, but there is a clear
10–13 % negative bias at 40 km. The reason for this
bias is uncertain but the most likely cause is the stray light
contamination in the 300–320 nm band already noted by
. Above the 45 km tangent height,
we use UV wavelengths to retrieve ozone and below 40 km
we use visible wavelengths. The retrieval method is sensitive to
the transition from UV to visible and it is easily disturbed by
stray light. As explained above, we scale the amount of stray
light to get a smooth ozone profile in the 40 km
transition. This ad hoc approach works reasonably well in
practice but better ways to switch between the different
wavelength domains should be investigated in future.
The diurnal variation explains about 1–3 % of the observed
40 km bias as the GOMOS day measurements are made during
the minimum of the diurnal cycle. At the 50 km
altitude we have up to 50 % positive bias depending on the
solar zenith angle. This bias cannot be explained by natural
variation; it indicates some problem in the retrieval, most
likely related to the stray light and its correction. Until this
issue is solved, it is recommended to only use GBL measurements
with a solar zenith angle of less than 75∘ when using
data from the altitudes above 45 km.
Login information for accessing the GBL data.
Serverftp.fmi.fiLogingomosGBLPasswordkOs20mos!
In this work, we showed the accuracy of the GBL data as a
function of latitude, altitude,
and solar zenith angle (Figs. –).
These are the most important
variables affecting the overall quality of the profiles.
Beside these variables, we have also studied
the effect of season, scattering angle, azimuth angle, albedo, and time, but they
do not seem to correlate substantially with the bias. The quality of
the GBL data could be summarized as follows. The accuracy of the GBL
data is better than 10 % between 20 and 35 km.
There is a negative bias at 35–45 km that has a consistent shape with all
studied observation conditions. Because of the regular shape, this
bias is straightforward to correct if the data is used, for example,
in time series studies. Above 45 km, the data is valid with
the solar zenith angles of less that 75∘ when the accuracy is
approximately 15 % or better.
The GBL Level 2 files are available from the FMI's FTP server and
the login information is shown in Table .
The file format is HDF5 (one file for each profile) and the total size of
the whole data set is about 22 GB.
Acknowledgements
This study was funded by the European Space Agency through the SPIN project
(http://esa-spin.org) and by the Academy of Finland through the MIDAT
project. The authors wish to thank the three referees for reviewing the
manuscript and for suggesting relevant corrections. The authors also
appreciate the MLS and OSIRIS teams for sharing their data. Special thanks go to
Viktoria Sofieva for valuable suggestions. The (ozone sounding) data used in
this publication were obtained as part of the Network for the Detection of
Atmospheric Composition Change (NDACC) and are publicly available (see
http://www.ndacc.org).
Edited by: C. von Savigny
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