The MetOp/Infrared Atmospheric Sounding Interferometer (IASI) instruments have provided data for operational meteorology and
document atmospheric composition since 2007. IASI ozone (O3) data have been used
extensively to characterize the seasonal and interannual variabilities and the evolution
of tropospheric O3 at the global scale. SOftware for a Fast Retrieval of IASI Data (SOFRID) is a fast retrieval algorithm that
provides IASI O3 profiles for the whole IASI period.
Until now, SOFRID O3 retrievals (v1.5 and v1.6) were performed with a
single a priori profile, which resulted in important biases and probably a
too-low variability. For the first time, we have implemented a comprehensive
dynamical a priori profile for spaceborne O3 retrievals which takes the pixel location, time
and tropopause height into account for SOFRID-O3 v3.5
retrievals. In the
present study, we validate SOFRID-O3 v1.6 and v3.5 with electrochemical concentration cell (ECC) ozonesonde profiles
from the global World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database for the 2008–2017 period. Our validation is
based on a thorough statistical analysis using Taylor diagrams. Furthermore, we
compare our retrievals with ozonesonde profiles both smoothed by the IASI averaging kernels and raw.
This methodology is essential to
evaluate the inherent usefulness of the retrievals to assess O3 variability and trends. The use of
a dynamical a priori profile largely improves the retrievals concerning two main
aspects: (i) it corrects high biases for low-tropospheric O3 regions such as
the Southern
Hemisphere, and (ii) it increases the retrieved O3 variability, leading to a
better agreement with ozonesonde data. Concerning upper troposphere–lower stratosphere (UTLS) and stratospheric O3,
the improvements are less important and the biases are very similar for both
versions. The SOFRID tropospheric ozone columns (TOCs)
display no significant drifts (<2.5 %) for the Northern Hemisphere
and significant negative ones (9.5 % for v1.6 and 4.3 % for v3.5) for the Southern Hemisphere. We
have compared our validation results to those of the Fast Optimal Retrievals on Layers for IASI (FORLI)
retrieval software from the
literature for smoothed ozonesonde data only. This comparison highlights three
main differences:
(i) FORLI retrievals contain more theoretical information about tropospheric O3
than SOFRID;
(ii) root mean square differences (RMSDs) are smaller and correlation
coefficients are higher for SOFRID than for FORLI; (iii) in the Northern
Hemisphere, the 2010 jump detected in FORLI TOCs is not
present in SOFRID.
Introduction
Ozone (O3) in the stratosphere protects life from solar UV radiation.
Close to the surface, O3 is an oxidative pollutant harmful for human
health through irritation of respiratory tracts and for
vegetation through deposition on leaves that leads to the reduction of plant
growth . Tropospheric O3 is
also a powerful greenhouse
gas whose increase during the 20th century has significantly contributed to
global warming . The radiative forcing of O3 is
particularly important in the tropical upper troposphere–lower stratosphere
(UTLS) .
It is therefore
important to document the evolution of
O3 in these different layers independently. There is clear evidence from
satellite databases that upper stratospheric O3 has increased since 1997
following
the ban of chlorofluorocarbons (CFCs) by the Montreal Protocol . Nevertheless, the
total column O3 has been stable since 1998. According to , this contradiction is due
to
the fact that lower stratospheric O3 is declining and compensates both
stratospheric and tropospheric O3 increase. Based on Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) tropospheric
ozone columns (TOCs), they state that TOC is globally increasing. OMI/MLS
data for the 2005–2016 period are indeed documenting global positive TOC trends
with particularly large increases over Asia .
Based on 10 years of retrievals with the Fast Optimal Retrievals on Layers for Infrared Atmospheric Sounding Interferometer (IASI) O3 (FORLI-O3) software, document a
decrease in tropospheric O3 levels in the Northern Hemisphere (NH). Another
IASI tropospheric O3 product (Karlsruhe Optimized and Precise Radiative transfer Algorithm Fit; KOPRAFIT-O3) displays a TOC decrease
over continental China . In their exhaustive
work on TOC evolution, clearly highlight the contradiction
between global increase (OMI/MLS and other UV–vis products) on the one hand and
global decrease (IASI) on the other hand. They also show that the
different satellite products agree on a TOC increase over Asia.
Among the two global IASI TOC datasets used in
, FORLI-O3 indicates a significant global decrease and O3
retrievals with the SOftware for a Fast Retrieval of IASI Data (SOFRID) indicate a
slightly weaker and less significant one. Two versions of FORLI-O3
have
been validated by (v20141022) and
(v20151001).
They both document a jump in the O3 retrievals in 2010 but
this does
not
hinder the fact that TOCs are decreasing according to . It has
to be noted that both validation studies compare IASI retrievals to ozonesonde
profiles smoothed by the retrieval averaging kernels. Such a comparison enables
the detection of abnormal biases, variability or drifts in the retrievals but
does
not document the ability of FORLI-O3 to reproduce real O3 levels and
variabilities.
SOFRID-O3 has only been validated at the
beginning of the IASI period on a very short time period and
on a longer time period together with FORLI-O3 and KOPRAFIT-O3
. Furthermore, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) L2 atmospheric temperature products
retrieved from IASI and used for FORLI (v20141022 and v20151001) and for
SOFRID-O3 v1.5
retrievals are not stable in
time . Therefore, we have reprocessed the whole IASI database
using ECMWF operational analyses for temperature and humidity to produce
SOFRID-O3 v1.6.
SOFRID-O3 has been shown to overestimate low tropospheric ozone
over the Southern Hemisphere (SH) .
have hypothesized that this overestimation was due to the use
of a
single a priori profile biased towards NH
midlatitudes O3. In order to verify this hypothesis and
to improve our O3 retrievals, we have developed a new version of SOFRID-O3
(v3.5), with a dynamical a priori profile based on a global O3
climatology
.
The aim of the present paper is to validate both of the latest SOFRID-O3 products
(v1.6 and v3.5) for the
whole IASI period (2008–2017) in order to infer their ability to reproduce
tropospheric O3 levels and variability on seasonal to decadal timescales. The validation is based on O3 profiles from
ozonesondes retrieved from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database.
In Sect. , we describe the characteristics and differences
of SOFRID-O3 v1.6 and v3.5 retrievals.
Section is dedicated to the description of the validation
methodology based on
comparisons between smoothed and raw ozonesonde data, and we provide our
validation results in Sect. . Based on ,
we also
compare our results to FORLI-O3 (Sect. ) before concluding the paper in Sect. .
IASI SOFRID-O3 retrievals
IASI is a spaceborne thermal infrared nadir spectrometer. IASI has a moderate
spectral resolution combined with a high signal-to-noise ratio and a 12 km
footprint at nadir . Thanks to its large across-track
scanning (∼2200 km), IASI revisits each scene twice daily around 09:30 LT
solar time in the
morning and in the evening. Three IASI instruments have been launched on the
MetOp meteorological platforms (MetOp-A in 2006, MetOp-B in 2012 and MetOp-C in 2018). Here, we present results based on O3 retrievals from 10 years of MetOp-A/IASI data. We will present results based on the morning overpass data
only as they are known to provide more information than nighttime data.
Furthermore, it facilitates the comparison to other validation studies
also based on morning data.
The SOFRID software first described in
is based on the RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) operational radiative transfer code
combined with the 1D-Var software
, both developed within the
framework of EUMETSAT Numerical Weather Prediction Satellite Applications
Facility (NWP-SAF). The O3
profiles are retrieved from the 980–1100 cm-1 spectral window encompassing
the 9.6 µm O3 absorption band. Only cloud-free or weakly contaminated
pixels are processed. Pixels with Advanced Very High Resolution Radiometer (AVHRR)-derived fractional cloud
cover larger than 25 % are excluded. We also use a test based on brightness
temperatures at 11 and 12 µm when
AVHRR cloud cover is not available as described in . The two
SOFRID-O3 versions that are validated
and compared in the present paper have significant differences that are
described below.
Single a priori profile: v1.6
SOFRID-O3 v1.6 is almost similar to v1.5 described in . It is
based on RTTOV v9.3 . In RTTOV, the optical depths are
expressed
as a linear combination of profile-dependent
predictors that are functions of temperature, absorber amount, pressure and
viewing angle. In RTTOV v9.3, the regression coefficients are derived from
computations with the line-by-line radiative
transfer model v11.6 (LBLRTM; ) on 43 atmospheric levels using
the HITRAN2004 spectroscopic database .
The single difference is that v1.6 uses temperature and humidity profiles from
ECMWF operational analyses for the RTTOV
simulations and v1.5 was using IASI L2 products delivered by EUMETSAT. The
change has been operated for availability problems and mostly because the
EUMETSAT L2 products are
not homogeneous over the whole 2008–2017 period, which could
result in retrieval inconsistencies . We use 6-hourly ECMWF
analyses which
are provided on 91 (137) vertical levels until (after) 24 June 2013
from the ground up to
0.02 hPa on a 0.25∘×0.25∘ horizontal grid. The ECMWF temperature and humidity profiles are interpolated to the time and location of the target IASI pixel
with a 3-D linear interpolation scheme.
O3 concentrations are retrieved on the 43 RTTOV levels with the NWP-SAF 1D-Var algorithm based on the optimal estimation method (OEM) . The OEM is a Bayesian method where the incomplete information provided by the measurement is complemented by a priori information which is
supposed to represent the best knowledge of the state vector at the moment of the measurement. In our case, the state vector is the O3 profile. For both v1.5 and v1.6, we use a single O3 a priori
profile which is based on 2 years (2008–2009) of WOUDC and Measurement of Ozone on Airbus In-Service Aircraft – In-Service Aircraft for a Global Observing System (MOZAIC-IAGOS)
profiles completed to the top of the RTTOV v9.3 model (0.1 hPa) by MLS-averaged profiles
(see for details).
Dynamical a priori profile: v3.5
As v1.6, SOFRID-O3 v3.5 uses interpolated temperature and humidity profiles
from ECMWF analyses. It is based on the more recent RTTOV (v11.1)
, where regression
coefficients are derived from LBLRTM
v12.2 computations on 101 vertical levels with the HITRAN2008 spectroscopic
database . The second and more
important one is that it uses
dynamical a priori profiles from TpO3, the O3 profile tropopause-based
climatology of
. This climatology is based on ozone profiles resulting from
merging ozonesonde data in the troposphere and SAGE II v6.2 data
in the stratosphere.
The ozonesonde profiles (36 000) extracted from the Binary Database of Profiles
(BDBP) come from 136 stations for the period 1980 to 2006 .
For each merged ozonesonde–SAGE II profile, the tropopause was computed
according to the World Meteorological Organization
(WMO) definition of the lapse-rate tropopause .
For each month, the ozone profiles are gathered according to 10∘
latitude bins and
1 km tropopause intervals, and the corresponding averaged profiles
together with their 1σ variabilities are computed and provided. Variable
a priori profiles have already been used for satellite sensor retrievals. For
instance, Tropospheric Emission Spectrometer (TES) O3 retrievals used monthly mean profiles from the Model for OZone and Related chemical Tracers (MOZART) chemistry–transport model (CTM) averaged over a 10∘ latitude ×60∘ longitude grid
. OMI O3 a priori profiles are
based on a monthly and latitude-dependent ozone profile climatology
derived from
ozonesonde and satellite data . Nevertheless, the use of
an a
priori profile simply based on the geographical location of the satellite pixel does
not allow taking the atmospheric dynamics into account. For instance, at a
midlatitude location, the O3 profile can be typical of midlatitudes on one day and polar (low tropopause) or tropical (high tropopause) a few days later
depending on the global atmospheric dynamics (position of the polar or
subtropical jets, anticyclones). The use of a tropopause-dependent climatology
allows us to take the atmospheric
dynamics into account and provides a more accurate a priori O3
profile. This technique was once used for O3 total column retrievals from Fourier-transform infrared spectroscopy (FTIR) spectra at the Jungfraujoch station . It was shown that
the retrieved O3 columns were largely improved when the tropopause was taken into account
in the choice of the a priori profile. In a first attempt to take the
tropopause into account for satellite retrievals, have
implemented two a priori profiles in the KOPRAFIT-O3 retrieval algorithm to
basically discriminate the tropics (tropopause higher than 14 km) from other
latitudes. have slightly improved the approach with a set of three
a priori profiles for high latitudes (tropopause lower than 10 km),
midlatitudes (tropopause between 10 and 14 km) and the tropics (tropopause
higher than 14 km). have tested a set of profiles for
retrievals on a synthetic database.
In SOFRID-O3 v3.5, we compute the tropopause
using the WMO lapse-rate
definition from the ECMWF interpolated temperature profiles. The a priori
profile is then picked up from the TpO3 climatology according to month,
latitude and tropopause height.
Information content and retrieval error
A remote sensing instrument is not equally sensitive to the different
atmospheric
layers. Its vertical sensitivity depends on its instrumental characteristics
and
on local parameters.
In the case of a thermal infrared nadir sounder such as IASI, surface
parameters such
as surface emissivity, surface temperature, thermal contrast between the
surface and the
first atmospheric layer are key parameters to determine the vertical
sensitivity, especially in the lower troposphere .
The
vertical
sensitivity of a remote sensing instrument is characterized by the so-called
averaging
kernel (AK) matrix. For each retrieval layer, the retrieved quantity is the
result of the convolution of the whole real profile by the corresponding
averaging kernel (row of the AK matrix) plus a contribution from the a priori
profile (xa) and a noise (ϵ)
contribution (see Eq. ).
x^=Ax+(I-A)xa+G(ϵ)
In an ideal case, the AK matrix (A) would be the identity matrix
(I)
and real (x) and retrieved (x^) profiles would be
identical within the noise level (ϵ)
contribution. G is the gain matrix that represents the sensitivity
of the retrieval to the measurement. In a real case, the AKs are bell-shaped
functions which peak at an
altitude that could be different from the nominal altitude and whose width
gives an indication of the retrieval vertical resolution.
The degree of freedom for signal (DFS) of a retrieval describing the number of independent
pieces of information provided by the measurement is the trace of
the AK matrix . We have divided the atmosphere in five layers
which are described in Table . The troposphere-2 layer has been selected for comparison
with , who did not compute a tropopause-based TOC
for their validation (see Sect. ). The DFS corresponding to
these different layers is displayed in Fig. for v1.6 and v3.5 averaged over the validation dataset. The total
DFS ranges from 2.4 to 3.3 for v3.5 and is
about 0.2 lower for v1.6.
The DFS values for the troposphere (WMO lapse rate), UTLS
and stratosphere are almost identical for both versions. The tropospheric DFS is the lowest (0.3–0.5) at high latitudes where surface temperature, thermal contrast
and tropopause height
are the lowest and the highest in the tropics (about 1.5) where surface
temperature and tropopause height are the highest. At midlatitudes, the
tropospheric DFS is about 0.6.
Therefore, except in the tropics, SOFRID retrievals provide less than one
independent piece of information in the troposphere. In the UTLS (stratosphere), the DFS values range from 0.7 to 1 (from 0.9 to 1.5), which means
that SOFRID provides around one independent piece of information in these
layers.
DFS and (–) retrieval errors in Dobson units (DU) for SOFRID-O3
v1.6 (red) and v3.5 (light blue) retrievals for (a) total column, (b) troposphere, (c) UTLS (300–150 hPa) and (d) stratosphere (150–25 hPa).
TOC distributions in DU for (a) July 2017 v1.6,
(b) July 2017 v3.5, (c) December 2017 v1.6 and (d) December 2017 v3.5.
The retrieval error is the sum of the measurement and smoothing errors
. Uncertainties in auxiliary parameters (temperature and
humidity
profiles, surface properties, etc.) are also responsible for errors.
and have shown that in the case of O3 and CO retrievals
from thermal infrared satellite sensors, the dominant source of errors was the
smoothing error. The retrieval errors for SOFRID-O3 v1.6 and v3.5 are displayed
in Fig. . Here, v1.6 displays slightly larger errors than v3.5 but has the
same behavior. For the total and stratospheric columns, the errors decrease
from high latitudes (9–12 DU) to the tropics (6–8 DU). The behavior of UTLS errors is similar with lower values (4 to 6 DU). For the TOC, errors are larger in the tropics (5 DU) than at middle and high latitudes (4 DU).
This is due to the fact that the tropopause height is
higher in the tropics, resulting in a larger a priori variability. The impact of the increased
variability exceeds the one of the increased information content, resulting
in a larger smoothing error.
Global distributions of tropospheric ozone columns
The global distributions of TOC from SOFRID v1.6 and v3.5 for July and December 2017 are displayed in Fig. . The global TOC structures are
similar for both versions. They both clearly show the highest TOC over
the NH midlatitudes in summer with a large export region over the northern
Pacific
off the Chinese coast and the summertime TOC maximum over the Eastern
Mediterranean already documented with the Global Ozone Monitoring Experiment-2 (GOME-2) sensor . The
tropical Wave-one pattern with the highest TOC over
the
tropical Atlantic and the lowest one over the South Pacific Convergence Zone
(SPCZ) is also noticeable for both versions. have shown that
the tropical Atlantic maximum was mostly a result of African and South American lightning NOx (LiNOx) emissions. High TOCs
are also detected during austral summer over southern Africa and the southern
Indian Ocean towards Australia. According to , these high TOCs are mostly caused by LiNOx emissions from central Africa with a yearly maximum in May.
The clearest difference between both versions is that v3.5 produces
lower TOC than v1.6 in the low tropospheric O3 regions. This is clear over the
Intertropical Convergence Zone (ITCZ) and the SPCZ, over the SH for both
seasons and over the NH midlatitudes in winter.
We will show in the validation
part of the paper that this is an
important improvement of the SOFRID-O3 retrievals.
The agreement is better in regions of high TOC such as NH midlatitudes in summer or the tropical Atlantic.
Maps of WOUDC stations with ECC O3 sonde data during the 2008–2017 period. Colors and sizes of the markers indicate the number of valid sondes at each station.
The use of a dynamical a priori
profile is responsible for visible stripes along the 10∘ latitude bands. These stripes
are generally indicating a discontinuity of 2.5 to 5 DU between two adjacent
latitude bands with different a priori profiles. They are clearly
caused by the impact of the a priori profile on the retrieval which is taken into
account in the retrieval error (see Eq. ). The latitudinal
discontinuities are therefore
consistent with our retrieval errors (4–5 DU) from Fig. .
Such stripes may appear as a problem for the use of SOFRID v3.5 data
for model validation. They are a minor problem for two main reasons. First, as is demonstrated in Sect. , the use of a dynamical a priori profile largely
improves the retrieved O3 profiles. Second, when model profiles are compared to SOFRID retrievals, the impact of the a priori profile is taken into account by using Eq. () such as in .
Validation methodologyOzonesonde data
Ozonesonde data come from the WOUDC database (https://www.woudc.org/, last access: 29 september 2020). For
consistency purposes,
we have chosen to use data from electrochemical concentration cell (ECC) sondes only. For the 10 years (the IASI
period; 2008–2017), valid comparisons were effective for about 12 000
ozonesonde profiles among the 16 000 downloaded.
A map with the number of sondes used for the validation at each station over the 2008–2017 period is displayed
in Fig. . Most (∼7000)
of the validation sondes were launched in the NH midlatitudes, with 15 stations providing more than one profile per month
on average (more than 120 profiles for 10 years) mostly in western Europe and
North America. For all other 30∘ latitude bands, the number of
validation
profiles ranges from 800 to 1200, with only three to four stations providing more than
120 profiles. The balloons that carry the ozonesondes often explode
below 40 km. In order to
complete the ozonesonde profiles in the upper stratosphere and mesosphere, we
have used MLS data averaged over 10 d on a 10∘×10∘ grid (see for details).
Taylor diagrams for (a, b) tropospheric columns and (c, d) lower tropospheric columns. (a, c) Raw sonde data, (b, d) smoothed
sonde data. Red circles indicate v1.6; blue crosses indicate v3.5.
Coincidence criteria
The spatiotemporal coincidence criteria are ±1∘ latitude,
±1∘ longitude and ±12 h. They are similar to those
used in , (50km±10h),
(100 km, ±6 h), (110 km, ±7 h). As we compare sondes with IASI
morning data only, and since most of the sonde launches are performed in the
morning, using 6 or 12 h coincidence does not introduce significant
differences. We have computed statistics for nine latitude bands which are the
whole globe, the two hemispheres and six 30∘ wide latitude bands. For each band, the monthly mean is
computed if there are at least four coincident profiles within this latitude band. We first keep pixels for which convergence is achieved. Convergence is based on the value of the retrieval cost function output from the 1D-Var analysis (Jcost) which has to be
positive, the value of its normalized gradient and the evolution of Jcost between the two last
iterations . We have also set an upper
limit (1.0) for Jcost in order to eliminate pixels with poor-quality fits. Thirdly, only pixels with a total DFS>2.0 are selected. Using these criteria, we have kept about 9.0×105 pixels
out of 1.1×106.
Comparison with raw and smoothed data
To compare remote-sensed to in situ or modeled profiles, it is important to
apply Eq. () to the in situ or simulated profile
. This procedure allows us
to check the quality of the retrieval taking its degraded vertical resolution
and sensitivity into account.
Nevertheless, in a validation objective, it is also necessary to
compare the retrieved profiles to raw (not smoothed by the AKs) in situ
profiles in order to perform a fully informative validation. This is of
particular importance when the satellite data are used for issues such as the
ozone seasonal to interannual variabilities
or to document the long-term tropospheric ozone tendencies
. Indeed, the application of Eq. ()
implies the mixing of information between the different layers. Therefore, the variabilities and the
drifts computed from raw and smoothed sonde data may be different and need to
be documented.
Raw ozone sonde data have been compared to IASI
retrievals in few
studies at the beginning of the IASI period but have been
disregarded in more recent validation work . The
importance
of raw data validation regarding seasonal and interannual variabilities and
trends analyses will be highlighted in detail in Sect. .
Taylor diagram
In order to validate remote sensing with reference in situ observations, we need to determine how well they are able to reproduce the same behavior. There are four statistical indicators that have to be computed: (i) the absolute difference or bias which documents the accuracy, (ii) the root mean square of the
differences (RMSDs) which tell whether the bias is significant or not, (iii) the coefficient of correlation (R) which documents the consistency and phase of the variabilities of both
datasets and (iv) the ratio of the standard deviations of both datasets which documents the goodness of the amplitude of the retrieval variability. In the case of IASI O3, the first three indicators are frequently
computed , but the last one is rarely
compared , which makes most validation exercises incomplete.
Based on the relationship between correlation coefficients, RMSDs and variances of the reference (validating) and test (validated)
datasets, has developed the Taylor diagram initially for climate model evaluation. It displays all of these parameters (except the biases) in a more convenient and
synthetic way than tables with numbers. Each experiment or observation to be
validated correspond to a point placed within a quarter circle. The reference
is located in the middle of the
x axis (see Figs. , ). The correlation
coefficient between the reference and test dataset is given by the azimuthal
position of the point. The RMSD is proportional to the distance between the
test and the reference point. Finally, the radial distance from the origin is
proportional to the variance of the experiment. We have normalized both RMSDs
and standard deviations by the standard deviation of the reference to display the results from multiple experiments on a single diagram (see for details).
Taylor diagrams for (a, b) UTLS columns and (c, d) stratospheric columns. (a, c) Raw sonde data, (b, d) smoothed sonde
data. Red circles indicate v1.6; blue crosses indicate v3.5.
Validation resultsGeneral statistics for tropospheric, UTLS and stratospheric partial columns
For the different latitude bands, the statistics from the comparisons between
ozonesondes and SOFRID data are
presented in Table for the biases and corresponding RMSDs.
Taylor diagrams are displayed in
Fig. for
the TOC and lower tropospheric columns and
in Fig. for the
UTLS and stratospheric columns.
Biases (%) between sondes and SOFRID retrievals with corresponding RMSDs (%).
Values between brackets correspond to smoothed sonde data. Significant biases
(bias > RMSD) are in bold text.
Concerning the troposphere, the comparison between SOFRID and raw sonde clearly
shows the improvement from v1.6 to v3.5 (Fig. a). Here,
v3.5 displays a larger variability in better agreement with the raw sonde data with a ratio between SOFRID and sonde variances ranging from 0.62 to 1.01. For v1.6, this ratio ranges from 0.15 to 0.45. The RMSDs of the SOFRID versus raw
sonde data are lower and the correlation coefficients larger for v3.5
than for v1.6. Tropospheric biases are smaller than 10 % with the noticeable
exception of midlatitudes and high latitudes of the SH for v1.6 and raw
sonde data with significant biases of 29 % and 55 %, respectively (Table ).
This problem of SOFRID v1.6 retrievals in the SH had already been diagnosed by and by . The use of a dynamical a priori profile in v3.5 allows us to reduce these large biases to almost zero.
As expected, when the sonde profiles are smoothed with SOFRID AKs (Fig. b and d), the agreement between sonde data and SOFRID
retrievals
is better. The retrieval variabilities are closer to the sonde variabilities,
the RMSDs are smaller, and the correlation coefficients are
higher. It is also noticeable that differences between both retrieval versions
are less important and that the improvement of v3.5 relative to v1.6 is less
evident. Furthermore, the large v1.6 biases in the SH troposphere at midlatitudes and
high latitudes are reduced below 10 % when the impact of the a priori profile is
taken into account with Eq. (), hiding the problem.
The lower tropospheric retrieved columns agree less with raw sonde data with
degraded correlation coefficients and larger RMSDs (Fig. c) compared to the TOCs. For raw sonde data comparisons,
the lower tropospheric variability is better for v3.5 than for v1.6. When the
sondes
are smoothed, the statistics are much better and similar to the TOC results
(Fig. d). The added value of lower tropospheric columns
relative to TOCs is therefore not obvious for SOFRID-O3.
In the UTLS, both v1.6 and v3.5 are in good agreement with raw sonde data
(Fig. a) and the differences between both versions are
much lower than for the tropospheric columns. Correlation coefficients range
from 0.67 to 0.93, and the ratios between retrieved
and raw sonde variances range from 0.5 to 1.0 at midlatitudes and high latitudes. For the northern and southern
tropical latitudes, the correlations coefficients range from 0.6 to 0.75, and the variance ratios are between 1.6 and 2.1, highlighting a too-high variability retrieved in
the tropical UTLS. In the UTLS, biases are positive (5 % to 18 %) at high latitudes and midlatitudes, and negative (-3 % to -21 %) at tropical latitudes and not significant because of large RMSDs.
Taylor diagrams for (a) tropospheric columns and (b) UTLS columns for
comparisons with raw sonde data. Red circles indicate v3.5; blue crosses indicate v3.3.
In the stratosphere, the agreement
between raw sonde data and SOFRID retrievals is very good for the two versions
as well as in all latitude bands, with correlation coefficients in the 0.75–0.98 range and variance ratios in the 0.56–0.96 range, except in the tropical bands where the retrieved variances are much lower than the ozonesonde variances (Fig. c). Stratospheric columns from v3.5 are in slightly better agreement (higher R2, lower RMSDs) with ozonesonde data than v1.6. Large positive biases (10 %–14 %) are found at tropical latitudes for
both v1.6 and v3.5 (Table ).
Both in the UTLS and the stratosphere, the agreement is only slightly improved (larger correlation coefficients and lower RMSDs) when the sonde
profiles are smoothed by the AKs (Fig. b and d).
Smoothing of the sonde profiles does not significantly modify the UTLS and stratospheric biases. In particular, the
tropical UTLS large negative biases are still present when the AKs are applied to the sonde data. The small differences between v1.6 and v3.5 on the one hand and between raw and smoothed sonde data on the other hand highlight the larger sensitivity of IASI to the UTLS and the stratosphere than to the
troposphere as already discussed in and for
SOFRID v1.5.
Impact of the intraseasonal tropopause dependence of the a priori profile on SOFRID improvements
The climatology is tropopause dependent in two
different ways. First, it implicitly documents the seasonal and latitudinal
relationship between the tropopause and the O3 profiles with the
classification of the O3 profiles by month and 10∘ latitude band. Second, it
explicitly documents the intraseasonal tropopause O3 profiles' relationship
with various tropopause-dependent profiles for each month and latitude band.
In order to determine the impact of the intraseasonal tropopause dependence on the SOFRID retrievals, we have performed retrievals using the profile with the highest occurrence for each month and each 10∘ latitude band as a
priori information. The version with a single monthly a priori profile in each 10∘ latitude band is v3.3.
The comparisons between v3.5 and v3.3 are presented in Taylor diagrams for the tropospheric and the UTLS columns in Fig. . In the stratosphere, the changes (not shown) are negligible. Concerning the TOC, the improvements are
negligible except in the 60–30∘ N and 60–90∘ S bands where v3.5 better reproduces
the variability of the TOC. In the UTLS, v3.5 gives slightly better results in
terms of correlation coefficients and variability relative to v3.3 in all the
latitude bands except in the 60–90∘ S one. Nevertheless, the
improvement from
v3.3 to v3.5 is minor compared to the overall improvement from v1.6 to v3.5
(Figs. and ). We can therefore conclude
that the seasonal (monthly) and latitude dependence of the a priori O3 profile is responsible for most of SOFRID improvements.
Vertical profiles
After comparing partial columns, it is interesting to look at
complete profiles to get better insight about the discrepancies between IASI retrievals and sonde data.
The annual average profiles for v1.6 and v3.5 are
displayed in Figs. and , respectively, for the
different latitude bands.
Profile comparisons between sonde and SOFRID-O3 v1.6 a priori profiles (left panels) (solid black lines), IASI (dashed black lines), smoothed
(SmRS, solid blue lines) and raw (RS, dashed blue lines) sonde vertical
profiles, biases (right panels) (solid lines)
and RMSD (dashed lines) between IASI and raw (black lines) and smoothed (blue
lines) sondes for the NH (left panels) and SH (right panels).
Same as Fig. for SOFRID-O3 v3.5.
In the NH, v1.6 and v3.5 show similar behavior with a large upper
tropospheric positive bias at midlatitudes and high latitudes and a large oscillation from a negative
bias at 250 hPa to a large positive bias at 100 hPa in the tropics. These
profile features are responsible for the positive (negative) biases for
the midlatitudes and high latitudes (tropics) UTLS columns and for the positive
biases for the tropical stratospheric columns (see Table ). In the SH, the large tropospheric positive biases of SOFRID relative to raw
sondes (below 300 hPa in the high latitudes and midlatitudes and below 500 hPa in the tropics) present in v1.6 almost
disappear in v3.5. The improvement of SOFRID accuracy in the SH extratropical troposphere is the clearest advantage of using dynamical a priori profiles. In the SH tropics, the TOC difference between v1.6 and v3.5 is
not so clear (see Table ) because the positive bias in the
lower troposphere is compensated by a larger negative bias in the upper
troposphere in v1.6. As already discussed from column comparisons, it is also
noticeable from profile comparisons (Figs. and
) that the agreement between SOFRID retrievals and smoothed
sonde profiles is better than with raw sondes. An important exception are the
large UTLS oscillations in both the NH and SH tropics and for both v1.6 and
v3.5. Therefore, unlike what was expected, this important discrepancy between retrievals and sonde data does not result from the use of a single a priori profile too far from the real profile. The differences between v3.5 and v1.6 are largely reduced when sondes are
smoothed. For instance, the large tropospheric biases for v1.6 in the SH
disappear when the smoothing is applied to the sonde profiles.
For all latitude bands, RMSD profiles display the largest values around the
tropopause (below 60 % in the extratropics and up to 100 % in the NH tropics),
as is expected because it is the altitude range with the largest relative
variability. RMSDs between retrievals and smoothed data are
generally much lower than with raw data. This is also expected since the
smoothing error is the largest source of error in IASI retrievals (see
). RMSDs with smoothed sondes in the troposphere are somewhat larger for v3.5 than v1.6 especially in the SH. This is an indication of the increased sensitivity and decreased smoothing of v3.5. This is also evident in the Taylor diagrams which show that tropospheric variabilities are larger and in better agreement with sonde data (raw and smoothed) for v3.5 (see Fig. ).
Time series of tropospheric columns
As tropospheric O3 trend assessment is a major issue and one of the main topic of the TOAR (Tropospheric Ozone Assessment Report)/International Global Atmospheric Chemistry (IGAC)
international initiative , we focus in this section on TOC
time series. Time series are also interesting to bring insight about the general statistics discussed in the previous sections and to identify possible drifts of the data.
The time series of IASI and sonde monthly TOCs are presented in
Fig. () for v1.6 and in Fig. () for v3.5 for the Northern (Southern) Hemisphere. We present both raw and smoothed sonde data to highlight the impact of smoothing upon the agreement between IASI and sondes. This impact is particularly obvious for SOFRID v1.6 at midlatitudes. At northern midlatitudes, the bias between SOFRID v1.6 and raw sonde TOCs
displays large seasonal variations from -5 % to -10 % in summer and 10 % to 20 % in
winter, resulting in a negligible 2%±15% average bias (Table ).
When sonde data are smoothed by IASI AKs, the sonde
variability is largely reduced. Bias varies from 5 % in winter to -5 % in summer.
Time series of SOFRID-O3 v1.6 TOCs (DU) in the Northern Hemisphere for (a) 90–60∘ N, (b) 60–30∘ N, (c) 30–0∘ N and (d) 90–0∘ N.
Blue lines indicate IASI retrievals,
red lines indicate raw sonde data, and green lines indicate smoothed sonde data.
Differences (%) between IASI and sonde data for (e) 90–60∘ N, (f) 60–30∘ N, (g) 30–0∘ N and (h) 90–0∘ N.
Red lines indicate differences with raw sonde data and green lines indicate differences with smoothed sonde data.
Same as Fig. for SOFRID-O3 v1.6 in the Southern
Hemisphere.
For southern midlatitudes, as already highlighted by
and , SOFRID TOCs are significantly biased high (29%±22 %) relative to raw sonde data (Table ). This was explained by the fact that the single a
priori profile used in v1.6 is biased towards northern midlatitude O3.
When the sonde data are smoothed by IASI AKs, the agreement is much better and the bias becomes non-significant (5%±9 %) as a result of taking the a priori contribution
into account (Eq. ). The largest significant bias
(56%±25 %)
is found in the SH high latitudes for v1.6 TOCs (Table ) with
large seasonal variations from 20 % in winter to 120 % in summer. The large bias variabilities at midlatitudes and especially high latitudes of the SH result from the very low seasonal variability of the retrieved columns (see Fig. a).
Same as Fig. for SOFRID-O3 v3.5 in the Northern
Hemisphere.
Same as Fig. for SOFRID-O3 v3.5 in the Southern
Hemisphere.
For v3.5, the use of a dynamical a priori profile
clearly improves the retrievals at midlatitudes. At northern midlatitudes,
the seasonal bias variation is reduced to -10 %–0 % and the average bias remains small (-6%±14 %). When
smoothing is applied, the seasonal variability almost disappears and the bias
is only -3%±9 %. At southern midlatitudes, the agreement is very good and very
similar for raw and smoothed sonde data, with no real seasonal signature
detectable and an average bias close to 0 %.
At tropical latitudes, the situation is quite different. First, the seasonal
variability is not so notable and regular, and the difference between raw and
smoothed sondes is lower than at midlatitudes. Furthermore, the behavior of
v1.6 and v3.5 is close even though v3.5 is in better agreement with sonde
data (see Sect. ). In the southern tropics, there is a
noticeable variation of bias between 2011 and 2014, with large negative biases of
-10 % and -15 % for 2008–2010 and 2015–2017 with biases of 0 and -5 % for v1.6 and v3.5, respectively. As such a bias variation is not detected for other latitude
bands, we assume that it may be linked to a gap in sonde data for the 2011–2014 period. A closer look at SH tropics' ECC sonde data shows that only two stations (Réunion and Nairobi) provide data regularly (30–50 profiles per year) over the period. For the Pago Pago Pacific station, data are available only from 2014 to 2016 and starting from 2012 for Irene in South Africa. For the Natal Atlantic station, more
than 25 profiles are available during the 2008–2010 and 2014–2017 periods and almost none during the 2011–2013 period.
One issue that was raised in TOAR was the different trends
computed from different satellite products. UV–visible satellite sensors
produce positive tropospheric O3 burden trends in both hemispheres, while trends from IASI products are negative. It has to be noted that in
, negative O3
burden trends from SOFRID v1.5 in the Northern Hemisphere and Southern
Hemisphere, and for the whole Earth
are, respectively, one-fourth, one-half and one-third smaller than FORLI's. The drifts computed
from the SOFRID–sonde differences are displayed in Figs. to .
At high northern latitudes, for both v1.6 and v3.5, the drifts are large (>9 % decade-1 and >5 % decade-1 for raw and smoothed data, respectively) and significant at the 95 % level.
For midlatitudes and tropical latitudes, drifts are between
0.9 % decade-1 and -3.4 % decade-1 but are not significant. The NH midlatitude
drift with raw sonde data is reduced from -3.1 with v1.6 to
-0.4 % decade-1 with v3.5. For the whole NH,
the drifts are not significant and decreases from -2.2 with v1.6 to
0.7 % decade-1 with v3.5 for raw sonde data. They are <1.5%decade-1±0.8 % decade-1 and hardly significant (p>0.10) for smoothed sonde data.
In the SH tropics, drifts are ∼-5 % decade-1 and
∼-3 % decade-1 for raw and smoothed sonde data, respectively, and only
significant for v3.5 compared to raw data. These drifts are
linked to the large negative biases of the 2011–2014 period resulting from
missing data (see above). For v1.6, a large but
non-significant drift (-8 %) also occurs at high latitudes, which is largely reduced for v3.5. For the whole SH, we found a significant negative drift (relative to raw sonde data) of
-9.5 % decade-1± 4.7 % decade-1 for v1.6 which is reduced to -4.3 % decade-1± 1.4 % decade-1 and becomes non-significant for v3.5.
Comparison with IASI-FORLI
Two versions of IASI O3 retrievals with the FORLI software have been
validated by and (B18). Part of their validation results are based on the same data as the present study, namely ECC ozone sondes from the WOUDC database between 2008 and 2014 for and 2008 and 2016
for B18. As they document the latest FORLI version (v20151001) on a
longer time period, we will focus on our comparison with B18. They have used a
comparable number (11 600) of ozone sonde
profiles as in the present study, and their comparison methodology is
close to the one we have used (spatiotemporal coincidence criteria
set to 100 km and ±6 h). We have collected the coefficients of determination (R2), the biases, the RMSDs, the DFS of the retrievals and the slopes of the linear fit between
the smoothed sondes and retrievals from B18.
There are some limitations to
the comparison between the validation of our SOFRID retrievals and the FORLI validation from B18. We are comparing our data with literature results which do not provide the same information as we do. For instance, B18 do not document the sonde and IASI variabilities, and it is therefore not possible to draw their data in Taylor diagrams. B18 have also limited their comparisons to smoothed sonde data. Another limitation is that FORLI and SOFRID use their own quality flags to filter the data.
In order to document the impact of the pixel selection on SOFRID validation, we have performed the
comparison with sonde data using modified
quality flags. The cloud filtering threshold is the clearest source of
difference between the pixel selection of both algorithms. We have therefore
lowered the upper limit of the AVHRR cloud fraction cover to 13 %, which is the threshold used by B18, resulting in a loss of 5 % of
the treated pixels. The Jcost threshold has been decreased from 1.0 to 0.15
with a 6 % decrease of the selected retrieved profiles. Finally, the DFS lower value has been set to 1.75, increasing the number of selected retrievals by 2 %. These threshold modifications resulted in negligible changes of the general statistics (bias, RMSD, R) for the three atmospheric layers (troposphere, UTLS and stratosphere) and the different latitude
bands that are presented in this section. These statistics, based on large
amounts of data, are therefore not hindered by pixel selection differences.
In Fig. , we have drawn DFS from SOFRID v1.6 and v3.5
and from FORLI for the layers selected by B18 (1013–300, 300–150 and 150–25 hPa). Figure displays the coefficients of determination
(R2) and the slopes (b) from linear relationships fitted
between IASI retrievals and smoothed sonde data. Biases and RMSDs are shown for the three retrievals in Fig. . Finally, Fig. documents the drifts between sondes and SOFRID retrievals for the whole NH
for the surface–300 hPa layer to be comparable to B18.
DFS of IASI SOFRID-O3 v1.6 (red),
SOFRID-O3 v3.5 (light blue) and FORLI-O3 (green) retrievals in the different
latitude bands for (a) 1013–300 hPa, (b) 300–150 hPa and (c) 150–25 hPa. FORLI data are taken from .
b indicates slopes of the linear regression (positive values), and (–) R2 indicates
coefficients of determination (negative values) between IASI retrievals and sonde
data. Red indicates SOFRID-O3 v1.6, light blue indicates SOFRID-O3 v3.5, and green indicates FORLI-O3
(from ).
Biases and ±RMSDs (bars) of the differences between IASI
retrievals and sonde
data. Red indicates SOFRID-O3 v1.6, light blue indicates SOFRID-O3 v3.5, and green indicates FORLI-O3
(from ).
Time series of SOFRID-O3 (a) v1.6 and (c) v3.5 surface–300 hPa columns
for the Northern Hemisphere (0–90∘ N). Blue lines indicate IASI retrievals,
red lines indicate raw sonde data, and green lines indicate smoothed sonde data.
Differences between IASI and sonde data for (b) v1.6 and (d) v3.5. Red lines indicate raw sonde data and
green lines indicate smoothed sonde data.
In the three atmospheric layers, the information content is larger with FORLI
than with SOFRID v1.6 and v3.5 (Fig. ). This is particularly
visible for
the midlatitudes and tropics in the troposphere with DFS of 0.8 to 0.9 for
FORLI and DFS of only 0.4 to 0.6 for SOFRID. This probably results from
the retrieval noise level which is lower for FORLI than for SOFRID . At high
latitudes, the DFS values are low and closer for both algorithms, and the increase from high to midlatitudes
is therefore much larger for FORLI than for SOFRID. As both algorithms use a
single retrieval noise and a priori covariance matrix and similar surface and
atmospheric temperatures, the reason for such a difference is unclear. In the UTLS and stratosphere, the same increases of DFS from high latitudes to the tropics are visible for the three products. The difference in information content between retrievals is less
pronounced in the UTLS and in the stratosphere than in the troposphere.
The RMSDs (see Fig. ) are generally larger for FORLI than for
SOFRID. In the troposphere, RMSDs reach 18 % for FORLI and
are below 10 % for both SOFRID v1.6 and v3.5. In the UTLS, RMSDs are larger than in the other layers due to the lower absolute columns. For
SOFRID, UTLS RMSDs are in the range of 10 %–30 % and 20 %–45 % for FORLI. For both
SOFRID and FORLI, the highest RMSDs are in the tropics where the 150–300 hPa columns are the lowest. In the stratosphere, FORLI's RMSDs are also
systematically larger than SOFRID's. The differences are the largest at high
latitudes with FORLI RMSDs 3 to 4 times larger than SOFRID's.
The R2 differences (Fig. ) are partly related to the RMSD
differences. Generally, SOFRID has larger R2 than FORLI. As for the RMSDs, the differences
between both algorithms are the largest at high latitudes (especially in the
Southern Hemisphere where R2<0.4 for FORLI products) in the three layers. In the troposphere, the coefficients of determination are comparable for both algorithms in the tropical bands, and SOFRID v3.5 gives higher R2 than SOFRID v1.6. The differences between retrieval versions are generally lower and can even be reversed in the UTLS and in the stratosphere.
The slopes of the linear fits between retrievals and sonde data provide
complementary information to the R2 coefficients. A slope smaller than 1
indicates that the retrieved variability is too low compared to the reference
data, and conversely, a slope larger than 1 indicates an overestimation of the variability. In the troposphere, SOFRID v1.6 and v3.5 and FORLI have similar slopes except in the 60–90∘ S band where FORLI has a significantly lower slope
than SOFRID (Fig. ).
In the troposphere, FORLI products present systematic negative biases from 7 % to 20 % except in the polar regions. Concerning SOFRID, the tropospheric biases
are within ±6 % (comparable to TOC biases in Table ). The
results are largely different when the raw sonde data
are considered with very large biases in the Southern Hemisphere with SOFRID
v1.6, as discussed in Sect. . In the UTLS, SOFRID and
FORLI biases are significantly positive except in the tropics, and more
specifically in the SH tropics, where SOFRID columns are negatively biased by
∼20 %, as discussed in Sect. (Table ). In the stratosphere, both SOFRID and FORLI products
are positively biased. The largest differences between both retrieval
algorithms are found in the extratropical southern latitudes with FORLI biases larger than SOFRID. In the 60–90∘ S latitude band, FORLI biases reach about 40 %
against about 5 % for SOFRID.
From the perspective of a better quantification of tropospheric O3 evolution
and of the TOAR results , it is also important to compare the
drifts between sonde and retrievals. B18 present
and discuss the drift between FORLI and sonde data for different layers in
the whole NH.
The SOFRID NH tropospheric drifts discussed in Sect. are
smaller and opposite in sign to
the significant -8.6%decade-1±3.4% decade-1 drift between FORLI and smoothed sonde data in
the NH troposphere presented in B18.
As B18 computed a surface–300 hPa column instead of a
tropospheric column, we have computed the drifts based on the same layer (see
Fig. ).
Drifts for surface–300 hPa columns are slightly (0.1 % to 0.4 %) smaller than for TOCs and are not significant in both cases. The
comparison of the NH drift with B18 is therefore not dependent on the
tropospheric layer definition. For v1.6 and v3.5, compared to raw and smoothed sonde data, the surface–300 hPa column drifts range from -2.0 % decade-1 to 1.3 % decade-1 (see Fig. ), values which are much smaller than in
B18. Nevertheless, the NH tropospheric drift from FORLI is attributed to an
abrupt change or jump detected in 2010 . Indeed, the
drift strongly decreases after the jump and it becomes even
non-significant for most of the stations over the periods before or after the
jump, separately . The discontinuity is suspected to result
from updates in level-2 temperature data from EUMETSAT used as inputs into
FORLI . The absence of a jump and the small drift in SOFRID v1.6 (Fig. h) and v3.5 (Fig. h) NH
tropospheric data are therefore probably linked to the use of temperature
profiles from ECMWF analyses instead of EUMETSAT L2 products.
Conclusions
This study aimed at assessing the quality of two different versions of
SOFRID-O3 at the global scale and over the 10-year IASI period using
ozonesondes from the WOUDC. SOFRID-O3 v1.6 retrievals are
based on a single a priori profile like most other global IASI O3 retrievals . In v3.5, the a priori profile is dynamically selected from an O3 profile climatology based on latitude, season and the tropopause height. Other satellite O3 retrievals use a priori profiles from climatologies but they are chosen based on
geographical and temporal criteria only .
use three different a priori profiles picked up according to three
broad tropopause height classes to represent high, middle and tropical latitudes. To our knowledge, it is the first time that the tropopause height is used in such a comprehensive way for the choice of the a priori
profile for spaceborne O3 retrievals.
The general statistics (Taylor diagrams) of the comparisons between ozonesonde and SOFRID have highlighted the large improvements brought by v3.5 especially in the troposphere. The use of a tropopause-based a priori profile generally reduces the RMSDs and increases the correlation
coefficients and the amplitude of the retrieved variability.
The high TOC biases of v1.6
relative to low O3 are also corrected with v3.5. This is of
particular importance in the SH extratropics where the very large biases almost disappear. In the NH, lower TOCs are retrieved in winter, leading to a better seasonal cycle. A sensitivity test demonstrated that these SOFRID
improvements are dominated by the seasonal and latitude dependence of the a
priori profile.
In the UTLS and
stratosphere, the improvements are less important. In particular, both versions
are impacted by positive biases for the UTLS (18 % at NH midlatitudes) and
stratospheric (<7 %) columns at
extratropical latitudes that were already discussed in . In the tropics, large profile oscillations around the
tropopause result in negative biases in the UTLS (21 % in the SH) and positive biases (<14 %) in the
stratospheric columns.
Concerning the TOC drifts, we have shown that there were no significant
differences between v1.6 and v3.5. There are no significant drifts except at
high northern latitudes (increase of 9 % decade-1–13 % decade-1) and at southern tropical latitudes (decrease of 4 % decade-1–5 % decade-1). For southern tropics, the apparent decrease is probably linked to a sampling weakness at different stations which makes the time series inhomogeneous.
Our study has also demonstrated the importance of making comparisons with both raw and smoothed in situ data. Comparisons only with smoothed data could lead to the conclusion that the satellite data are better than they really are. For instance, the high bias for low TOC with v1.6 is almost completely corrected when smoothing is applied. The real improvement of v3.5 relative to v1.6 is only sizable when we compare SOFRID retrievals with raw sonde data.
Finally, we have compared our validation results to the latest (v20151001)
FORLI-O3 retrieval validation. The comparison had to be limited because
the variability of FORLI-O3 retrievals and ozonesonde data was not provided in , which prevented us to draw Taylor diagrams.
Furthermore, in , the FORLI-O3 data are compared to
smoothed sonde data only. FORLI produces larger RMSDs than SOFRID especially in the
stratosphere at high latitudes. The coefficients of determination (R2) are
consequently lower for FORLI columns than for SOFRID. Tropospheric biases are
significantly larger for FORLI (7 %–20 %) than for SOFRID (<6 %). Finally, no
significant tropospheric O3 drift is detected for both versions of
SOFRID-O3 in the NH. The difference with FORLI which is impacted by a
significant TOC jump in
2010 is likely linked to the use of different
temperature profiles for the radiative transfer calculations (ECMWF analyses
for SOFRID and EUMETSAT L2 for FORLI).
Data availability
The SOFRID-O3 data are freely available on the IASI-SOFRID website (http://thredds.sedoo.fr/iasi-sofrid-o3-co/, last access: 29 September 2020; ).
IASI L1c data have been downloaded from the Ether French atmospheric database
(http://ether.ipsl.jussieu.fr, last access: 29 September 2020; Ether, ). The research with IASI was conducted with some
financial support from the Centre national d'études spatiales (CNES) (TOSCA–IASI project). The ozonesonde data used in this study were provided by the WOUDC (10.14287/10000008, WOUDC database, 2020).
Author contributions
BB performed the validation of SOFRID-O3 data and wrote the paper. EE initiated and contributed to the
development of SOFRID-O3 v3.5. ELF was in charge of the SOFRID retrieval operations.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank those responsible for the
WOUDC measurements and archives for making the
ozonesonde data available.
Financial support
This research has been supported by the “Centre National d'Etudes Spatiales” (CNES) within the
framework of the IASI project.
Review statement
This paper was edited by Mark Weber and reviewed by two anonymous referees.
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