Introduction
Carbon dioxide (CO2) and methane (CH4) are the two most abundant
anthropogenic greenhouse gases and play important roles in global warming
and climate change (IPCC, 2013). Despite their significance, there are still
large gaps in our understanding of both gases concerning the spatial
distribution and time dependence of their natural and anthropogenic surface
sources and sinks. To get a clear comprehension of the sources and sinks of
CO2 and CH4 requires precise continuous measurements with adequate
resolution and coverage. Currently, monitoring CO2 and CH4 is
mainly based on in situ stations. Although these measurements provide
precise results, they are limited by their spatial coverage and uneven
distributions (Bousquet et al., 2006; Marquis and Tans, 2008). Besides, most
of these stations are located in the boundary layer, and therefore sink
estimates derived from these data are directly influenced by their
sensitivity to the inversion model local vertical transport (Houweling et
al., 1999; Stephens et al., 2007). The column-averaged dry-air mole fraction
measurements (XCO2 and XCH4) are sensitive not only to the surface
but also to the free troposphere, which allows a better distinction between
transport and local emissions (Yang et al., 2007). Additionally, total
column measurements are less sensitive to vertical transport and mixing, and
are also representative of a larger spatial area. A large set of studies used
the total column or column-averaged dry molar fraction observations to
improve the quality of the surface fluxes obtained by atmospheric inverse
models where quality refers to reduced uncertainty considering random and
systematic errors (e.g. Yang et al., 2007; Keppel-Aleks et al., 2011).
Recently, the satellite missions provide us with a unique view of global
XCO2 and XCH4 distributions.
The thermal and near infrared sensor for carbon observations Fourier transform
spectrometer (TANSO-FTS) on board GOSAT was successfully launched in 2009.
It is the first space-based sensor in orbit specifically with the purpose of
measuring greenhouse gases from high-resolution spectra at SWIR wavelengths. The field
of view of GOSAT/TANSO is about 0.0158 radian, yielding footprints that are
∼ 10.5 km in diameter at nadir (Kuze et al., 2009). So far,
several algorithms have been developed to retrieve XCO2 and XCH4,
such as University of Leicester full physics retrieval algorithm OCFP and
proxy version OCPR (Boesch et al., 2011), the Bremen Optimal Estimation
DOAS (BESD) algorithm (Heymann et al., 2015), the Netherlands Institute for
Space Research/Karlsruhe Institute of Technology (SRON/KIT) full physics
retrieval algorithm SRFP and proxy version SRPR (Butz et al., 2009, 2011),
the NASA Atmospheric CO2 Observations from Space or ACOS algorithm
(O'Dell et al., 2012), and the National Institute for Environmental Studies
(NIES) algorithm (Yoshida et al., 2011, 2013) and the photon path length probability density function (PPDF) algorithm (Oshchepkov
et al., 2008). Baker et al. (2010) and Alexe et al. (2015) pointed out that
the satellite measurements of XCO2 and XCH4 help fill critical
gaps in the in situ network, reducing the uncertainty of the surface flux
estimation. As the amplitude of the annual and seasonal variations of
CO2 and CH4 column abundances are small compared to their mean
abundances in the atmosphere, the satellite products should reach a
demanding precision of 2 % or better (< 8 ppm for XCO2 and
< 34 ppb for XCH4), in order to improve the precision of inversion
models. Besides, achieving high relative accuracy (< 0.5 ppm for
XCO2 and < 10 ppb for XCH4) is even more important and
demanding than precision to obtain reliable surface fluxes via inverse
modelling (Buchwitz et al., 2012).
It is hard to obtain reliable retrieval results over ocean in the normal
nadir mode due to the low albedo in the near- and short-wave infrared
spectra. Therefore, GOSAT applies the sun glint mode over the ocean at
latitudes within 20∘ of the sub-solar latitude, in which the
surface of the ocean serves as a mirror to reflect the solar radiance to the
sensor directly, increasing the signal-to-noise ratio. Nowadays, the
ground-based FTIR Total Carbon Column Observing Network (TCCON) has become a
useful tool to validate column-averaged dry-air mole fractions of CO2
and CH4 (Wunch et al., 2010, 2011a). Although all the GOSAT greenhouse
gases retrieval algorithms have already been validated, to some degree, via
the TCCON observations (e.g. Wunch et al., 2011b; Tanaka et al., 2012;
Yoshida et al., 2013; Dils et al., 2014), only the land data have been
selected in these previous studies. Inoue et al. (2013, 2014) made ocean data of NIES SWIR L2 products validation by aircraft
measurements. To ensure that the ocean data of GOSAT can be used to achieve
a more global coverage, we compare the ocean data from different algorithms
with FTIR measurements from five TCCON sites close to the ocean and near-by
GOSAT land data. In Sect. 2, we introduce the GOSAT retrievals and TCCON
measurements. The validation method is described in Sect. 3. The results
and summary are presented in Sects. 4 and 5, respectively.
Data
GOSAT
For this paper, we have selected XCO2 and XCH4 products from the
NIES v02.21, SRON/KIT v2.3.5 and ACOS v3.5 algorithms (see Table 1) with a
good quality flag, which is provided by each algorithm according to the
spectral residual, retrieval errors and other parameters. To avoid the
uncertainty resulting from different time coverages of each product, the
selected data are limited to the April 2009 to December 2013 period.
TANSO-FTS/GOSAT retrieval algorithms.
Molecular
Algorithm
Institute
Time period
References
NIES v02.21
NIES
04/2009–05/2014
Yoshida et al. (2011, 2013)
XCO2
SRFP v2.3.5
SRON/KIT
04/2009–12/2013
Butz et al. (2011)
ACOS v3.5
NASA
04/2009–06/2014
O'Dell et al. (2012)
NIES v02.21
NIES
04/2009–05/2014
Yoshida et al. (2011, 2013)
XCH4
SRFP v2.3.5
SRON/KIT
04/2009–12/2013
Butz et al. (2011)
SRPR v2.3.5
SRON/KIT
04/2009–12/2013
Schepers et al. (2012)
There are two SRON/KIT algorithms, SRFP v2.3.5 and SRPR v2.3.5, which are
both based on the RemoTeC algorithm. Both algorithms use the products from
TANSO-CAI/GOSAT as cloud screening. SRFP is a full physics version, which
adjusts parameters of surface, atmosphere and satellite instrument to fit
the GOSAT spectra. SRFP also allows for the retrieval of a few effective
aerosol parameters simultaneously with the CO2 and CH4 total
column, such as particle amount, height distribution and microphysical
properties (Butz et al., 2009, 2011). While the proxy version (SRPR) of
XCH4 accounts for the scattering by taking the ratio of the
XCH4/ XCO2, so that most light-path modifications due to scattering
cancel out (Schepers et al., 2012). The forward model of RemoTeC is based on
the vector radiative transfer model (RTM) developed by Hasekamp and
Landgraf (2005) and the Tikhonov–Phillips method is employed in the
inversion scheme. Both SRFP and SRPR have applied post-processing and bias
correction according to the modified version of GGG2012 (corrected for the
laser sampling errors, also known as ghost issues). All data have been
downloaded from the GHG-CCI project Climate Research Date Package (CRDP, 2015)
database
(http://www.esa-ghg-cci.org/sites/default/files/documents/public/documents/GHG-CCI_DATA.html).
NIES v02.21 also applies the cloud mask from TANSO-CAI/GOSAT products with
additional cloud detection scheme only for the ocean data and retrieves
aerosol parameters and surface pressure simultaneously with CO2 and
CH4 to represent the equivalent optical path length on these
cloud-screened data (Yoshida et al., 2013). The major difference between SRFP
and NIES retrieval algorithms is the handling of the optical path length
modification that results from the scattering. In the NIES algorithm, the
state vector contains the logarithms of the mass mixing ratios of fine-mode
aerosols and coarse mode aerosols, for which the a priori values are
calculated by SPRINTARS V3.84 (Takemura et al., 2009). The forward model is
based on the fast radiative transfer model proposed by Duan et al. (2005) and
the optimal solution of the Maximum A Posteriori (MAP) method is applied as
the inversion method. NIES v02.21 only contains the raw retrieval values; all
data have been downloaded from
https://data.gosat.nies.go.jp/ (GUIG, 2015).
Similar to the SRFP and NIES algorithms, ACOS v3.5 is a full-physics
algorithm, but with a different cloud filtering, state vector, forward model
and inversion strategy (Crisp et al., 2012; O'Dell et al., 2012). ACOS uses
the information from the O2-A band to select the clear-sky footprints
(Taylor et al., 2012). The forward model is based on a fast
single-scattering model (Nakajima and Tanaka, 1988), the LIDORT scalar
multiple scattering model (Spurr et al., 2001), and a second-order-of
scattering polarization model called 2OS (Natraj and Spurr, 2007). It fits
the vertical optical depth of four scattering types together with CO2.
The modified Levenberg Marquardt method is used to minimize the cost
function. As ACOS has been developed originally to retrieve the OCO
satellite data products, only XCO2 is included in the products. Wunch
et al. (2011b) pointed out that the ACOS-GOSAT v2.9 XCO2 data have a
small global bias (< 0.5 ppm), and Nguyen et al. (2014) found that
the ACOS v3.3 XCO2 abundances tend to be larger than TCCON measurements
by about 1–1.5 ppm. Here, the data from the latest version, ACOS v3.5, are
used to compare with the “near-ocean” TCCON measurements. ACOS v3.5
products have been bias corrected using TCCON GGG2014 products.
TCCON
TCCON is a network of ground-based FTIRs targeting the provision of highly
accurate and precise column-averaged dry-air mole fractions of atmospheric
components including CO2, CH4, N2O, HF, CO, H2O and
HDO, for the validation of the corresponding satellite products, such
as SCIAMACHY, GOSAT and OCO-2. All the TCCON stations use the GGG software
to derive the gas column concentrations, as has been described in detail by
Wunch et al. (2011a). XCO2 and XCH4 are calculated from the ratio
of the retrieved columns to the simultaneously retrieved O2 column, so
as to minimize systematic errors (Yang, 2002). GGG includes its own Fourier
transformation algorithm to derive the spectra from the recorded
interferograms: it also corrects for the solar intensity variations during
the recording of the interferogram due to the occurrence of clouds or heavy
aerosol loads (Keppel-Aleks et al., 2007). Most TCCON stations have been
calibrated to WMO standards by comparison to aircraft in situ overpass
measurements, and global calibration factors for each gas (0.9898 ± 0.001(1σ) for XCO2 and 0.9765 ± 0.002(1σ) for
XCH4) are applied to the TCCON data (Wunch et al., 2010; Messerschmidt
et al., 2011; Tanaka et al., 2012; Geibel et al., 2012). To ensure
network-wide consistency, Messerschmidt et al. (2010) and Dohe et al. (2013)
discovered and minimized laser sampling errors. The latest version of GGG
(GGG2014) has a ghost correction embedded in an interferogram to spectrum conversion process (I2S)
that differs in methodology to Dohe et al. (2013), but results in similar
minimization of laser sampling errors (Wunch, et al., 2015). Thanks to all
these and ongoing efforts (Hase et al., 2013; Kiel et al., 2016), TCCON
has been extensively used to validate satellite XCO2 and XCH4
retrievals (e.g. Wunch et al., 2011b; Guerlet et al., 2013; Yoshida et al.,
2013; Dils et al., 2014; Kulawik et al., 2016).
The locations and start times of TCCON sites.
Site
Latitude
Longitude
Alt (km a.s.l)
Start time
References
Izaña
28.3 N
16.5 W
2.37
May-07
Blumenstock et al. (2014)
Ascension Island
7.9 S
14.3 W
0.01
May-12
Feist et al. (2014)
Darwin
12.4 S
130.9 E
0.03
Aug-05
Griffith et al. (2014a)
Reunion Island
20.9 S
55.5 E
0.09
Sep-11
De Mazière et al. (2014)
Wollongong
34.4 S
150.8 E
0.03
May-08
Griffith et al. (2014b)
As the TANSO-FTS/GOSAT sun glint data over the ocean are limited to latitudes
within 20∘ of the sub-solar latitude, only five low-latitude and
geographically close-to-ocean TCCON sites are selected (see Table 2, from
north to south: Izaña, Ascension Island, Darwin, Reunion Island and
Wollongong). The corresponding TCCON products used in this study are GGG2014
version. All data were downloaded from the TCCON Data Archive, hosted by the
Carbon Dioxide Information Analysis Center (CDIAC) at
ftp://tccon.ornl.gov/.
Methodology
Spatiotemporal collocation criterion
The ideal TCCON-satellite data pair should consist of measurements at the
same place during the same time. However, in order to find a sufficient
number of co-located measurements to enable a robust statistical analysis,
several spatiotemporal criteria were used in previous validations. Wunch et
al. (2011b) used the mid-tropospheric potential temperature field at 700 hPa
(T700) to define the coincidence criteria, as Keppel-Aleks et al. (2011)
pointed out that the potential temperature coordinate is a good proxy for
large-scale CO2 gradients in the Northern Hemisphere and mid-latitudes.
Guerlet et al. (2013) utilized model CO2 fields to determine
coincidences and Nguyen et al. (2014) used a modified Euclidian distance
weighted average of distance, time and mid-tropospheric temperature at 700 hPa.
Unfortunately, in the present paper, five TCCON sites are located in
the low-latitudes, where the correlation between XCO2 gradients and
potential temperature is less effective. Additionally, contrary to the
relatively large amount of measurements over land, the ocean data are quite
scarce. Even with a 500 or 1000 km radius collocation area around the
FTIR stations, the number of TCCON-satellite data pairs turns out to be
insufficient to obtain stable results.
The co-location area is finally set as ±5∘ latitude
±15∘ longitude around each TCCON site. Within this
co-location box, we do not detect any significant latitude or longitude
dependent bias for XCO2 and XCH4. Figure 1 depicts the locations
of TCCON sites and co-located XCH4 retrieval footprints from the SRPR
algorithm from April 2009 to December 2013. The blue points represent the GOSAT sun
glint data over ocean, and the green ones correspond to the normal nadir data
above land. The collocation time is set to ±2 h. That means that all
the FTIR measurements occurring within ±2 h of a single satellite
observation, meeting the spatial requirement, are averaged to acquire one
TCCON-satellite data pair. Dils et al. (2014) demonstrated that the typical
variability (1σ), of the FTIR measurements within a 4 h time window,
including random errors and real atmospheric variability, is on average 2.5 ppb
for XCH4 and 0.4 ppm for XCO2; this meets the precision
requirement of the ground-based measurements (better than 0.25 % for
XCO2 and 0.2–0.3 % for XCH4) (Wunch et al., 2011a, 2015).
Therefore, in this study, the statistical analyses are based on the
individual data pairs or daily averaged data pairs, and all data pairs are
assumed to be of equal weight.
TCCON stations and SRPR XCH4 co-located footprints from April
2009 to December 2013. The colocation box is chosen as ±5∘
latitude ±15∘ longitude around the TCCON station. The
blue footprints are sun glint data over ocean, and the green ones are data
above land.
The average of the differences between a priori-corrected and
original satellite XCO2 and XCH4 retrievals (corrected – original)
at five TCCON stations. Iza, Asc, Dar, Reu and Wol stand for Izaña,
Ascension Island, Darwin, Reunion Island and Wollongong. The blue footprints
are sun glint data over ocean and the green ones are data above land.
A priori and averaging kernel corrections
Rodgers and Connor (2003) pointed out that it is not reasonable to directly
compare the measurements made by different remote sounders due to their
different a priori profiles and averaging kernels.
To deal with the a priori issue, TCCON a priori profile is applied as the
common a priori profile to correct the satellite retrievals:
ccor=c+∑ihi(1-Aisat)(xap,iTCCON-xap,isat)hi=mi∑mi,
in which, ccor and c are the a priori-corrected and original satellite
column-averaged dry-air mole fraction; i is the vertical layer index;
Aisat is the column-averaging kernel of the satellite retrieval
algorithm of layer i; xap,iTCCON and xap,isat are the a
priori dry-air mole fraction profile of TCCON and satellite algorithm,
respectively; hi corresponds to the normalized airmass-weight function
of layer i; mi corresponds to the mass of dry air in layer i.
The prior CO2 profiles of ACOS are derived from the output of the
Laboratoire de Meteorologie Dynamique (LMDz) model, with fluxes optimized to
match surface observations. The prior CO2 and CH4 profiles of NIES
are calculated for every observed day by an offline global atmospheric
transport model developed by the NIES (Maksyutov et al., 2008). The a priori
CO2 profiles of SRON/KIT algorithms come from the forward run of the Carbon Tracker Initiative with extrapolation based on in situ measurements, while the
XCH4 a priori is derived from the TM4 model (Meirink et al., 2006).
Figure 2 shows the impact of a priori correction for different retrieval
algorithms both on ocean and land data. For each algorithm, the a priori
correction factor of ocean data is similar to that of land data. For
XCO2, the correction factor (a priori-corrected – original) ranges from
-0.6 to 0.3 ppm. SRFP has stronger and more erratic correction factors
compared to NIES and ACOS. For XCH4, the correction factor ranges from
1.0 to 5.0 ppb with quasi-constant value at these TCCON stations.
The time series plots of XCO2 and XCH4
altitude-correction factors for different GOSAT algorithms at the Izaña
site. Blue data points are sun glint data over ocean and the green ones are
data above land.
It should be noted that we apply the spline interpolation “interpolation
method” to re-grid the TCCON gas concentrations to the satellite retrieval
levels or layers. It will result in errors for Izaña station, because
the a priori of TCCON starts from 2.37 km, which could not cover the whole
vertical range of the a priori of the satellite products. Therefore, we do
the test using the same a priori of satellite retrievals below 2.37 km to do
the a priori correction “fixed method”. As the difference between the
interpolation method and fixed method is within 0.5 ppb for XCH4 and
0.05 ppm for XCO2, this error can be ignored.
We have not dealt with the impact of the difference between the averaging
kernels of TCCON and GOSAT data, because the true atmospheric variability is
unavailable. Fortunately, the TCCON stations are located at low-latitudes,
so that the solar zenith angle (during the ±2 h when GOSAT pass the
TCCON sites) remains small, and GOSAT and TCCON averaging kernels look very
similar.
Altitude correction
Different from other stations, the Izaña FTIR is located on a steep
mountain, with an altitude of 2.37 km a.s.l. If we directly compare the
GOSAT data with Izaña FTIR measurements, a large bias could be
generated. Therefore, in this section, we present an altitude-correction
method to modify the GOSAT retrievals around the Izaña site. To that
end, we calculate the ratio (α) between the column-averaged dry-air
mole fractions of the target gas G above two different altitudes or pressure
levels P1 and P2, based on the a priori profile shape, as
α=cG,akP1/cG,akP2.
In Eq. (3), the column-averaged dry-air mole fraction of the target gas
above pressure level P1 or P2, cG,ak (P1 or P2), is computed as
cG,ak(P1orP2)=VCG(P1orP2)VCair(P1orP2)=∫P1orP2PtopfGdryakdpgmairdry[1+fH2Odry(mH2O/mairdry)]∫P1orP2Ptopdpgmairdry[1+fH2Odry(mH2O/mairdry)],
with
fH2Odry=fH2O/(1-fH2O).
In Eqs. (4) and (5) fH2O and fH2Odry are the mole and dry-air
mole fractions of H2O, respectively, fGdry is the a priori
dry-air mole fraction of the target gas G; mairdryand mH2O are
the molar weights of dry air and H2O, respectively. P1 or P2 and Ptop
represent the bottom and top pressure of the column, and g is
the gravitational acceleration, which varies with altitude and latitude.
Here, “ak” stands for the averaging kernel value at pressure level p of the satellite
product: it appears in order to account for the retrieval sensitivity at
each pressure level in the correction factor α that we apply to the
satellite data (we always apply the correction factor to the satellite
product, not to the TCCON product).
To compute fH2Odry, we use the 6-hour European Centre for
Medium-Range Weather Forecasting (ECMWF) interim reanalysis specific
humidity (SH), interpolated linearly in space and time to the GOSAT
field of view, which is given as the ratio of the mass of water vapour to the
mass of moist air (Dee et al., 2011):
SH=mH2OfH2O/(mairdryfairdry+mH2OfH2O),
and thus
fH2Odry=(mairdry/mH2O)⋅SH/(1-SH).
Equation (4) can then be rewritten as
cG,ak(P1 or P2)=VCG,ak(P1 or P2)VCair(P1 or P2)=∫P1orP2PtopfGdryakdpgmairdry[1+SH/(1-SH)]∫P1 or P2Ptopdpgmairdry[1+SH/(1-SH)].
The correction factor α (in Eq. 3) is applied as follows:
P1 corresponds to the pressure level of the TCCON station and P2 corresponds
to the pressure level of the GOSAT footprint. For example, for Izaña,
the altitude of FTIR station is generally higher than that of GOSAT
footprint; therefore P1 < P2, and the a priori profile of satellite
product is used as fGdry in Eq. (8). Note that if the
altitude of the GOSAT footprint is higher than the altitude of the TCCON
station (P1 > P2), then the a priori profile of TCCON would be
used as fGdry.
XCO2 results of NIES, SRFP and ACOS algorithms at 5 TCCON
stations based on all individual satellite–TCCON data pairs. The 95 %
confidence interval of relative bias, relative scatter, R and N are defined
in Sect. 3.4. Between brackets are the results without altitude
correction. Positive/negative bias means the FTIR measurement is less/larger than the GOSAT product.
Site
Target
NIES_XCO2
SRFP_XCO2
ACOS_XCO2
95 % Bias
Scatter
R
N
95 % Bias
Scatter
R
N
95 % Bias
Scatter
R
N
Iza
Ocean
-0.24 ± 0.036
0.37
0.88
397
0.05 ± 0.052
0.38
0.92
205
0.09 ± 0.030
0.33
0.92
458
(-0.27 ± 0.038)
(0.39)
(0.88)
(0.07 ± 0.056)
(0.41)
(0.91)
(-0.13 ± 0.030)
(0.33)
(0.92)
Land
0.03 ± 0.030
0.42
0.87
740
0.06 ± 0.058
0.67
0.78
521
-0.04 ± 0.024
0.40
0.90
1061
(0.03 ± 0.030)
(0.42)
(0.88)
(0.13 ± 0.057)
(0.66)
(0.79)
(0.07 ± 0.021)
(0.34)
(0.92)
Asc
Ocean
-0.31 ± 0.035
0.39
0.91
436
-0.03 ± 0.024
0.30
0.12
98
0.03 ± 0.022
0.30
0.13
718
Land
–
–
–
–
–
–
–
–
–
–
–
–
Dar
Ocean
-0.06 ± 0.041
0.38
0.92
337
-0.01 ± 0.059
0.30
0.94
101
0.15 ± 0.025
0.31
0.95
614
Land
-0.26 ± 0.019
0.37
0.89
1519
0.02 ± 0.014
0.41
0.86
3103
-0.06 ± 0.013
0.34
0.91
2774
Reu
Ocean
-0.47 ± 0.033
0.36
0.84
467
0.03 ± 0.056
0.35
0.83
153
0.03 ± 0.019
0.27
0.87
766
Land
-0.24 ± 0.030
0.33
0.81
477
0.20 ± 0.055
0.56
0.62
402
-0.05 ± 0.025
0.30
0.82
542
Wol
Ocean
-0.49 ± 0.046
0.41
0.81
302
0.08 ± 0.058
0.38
0.92
162
-0.01 ± 0.026
0.31
0.92
520
Land
-0.08 ± 0.022
0.53
0.82
2339
0.03 ± 0.026
0.52
0.82
2513
-0.00 ± 0.014
0.40
0.88
3026
All
Ocean
-0.33 ± 0.018
0.41
0.89
1939
0.03 ± 0.026
0.35
0.92
719
0.06 ± 0.011
0.31
0.93
3076
Land
-0.13 ± 0.013
0.47
0.85
5075
0.04 ± 0.012
0.49
0.84
6539
-0.03 ± 0.008
0.37
0.90
7403
XCH4 results of NIES, SRFP and SRPR algorithms at 5 TCCON
stations based on all individual satellite–TCCON data pairs. The 95 %
confidence interval of relative bias, relative scatter, R and N are defined
in Sect. 3.4. Between brackets are the results without altitude
correction. Positive/negative bias means the FTIR measurement is less/larger than the GOSAT product.
Site
Target
NIES_XCH4
SRFP_XCH4
SRPR_XCH4
95 % Bias
Scatter
R
N
95 % Bias
Scatter
R
N
95 % Bias
Scatter
R
N
Iza
Ocean
-0.19 ± 0.074
0.62
0.62
397
-0.33 ± 0.061
0.64
0.59
180
-0.16 ± 0.056
0.72
0.51
632
(0.88 ± 0.075)
(0.63)
(0.62)
(0.89 ± 0.062)
(0.68)
(0.52)
(1.04 ± 0.055)
(0.70)
(0.48)
Land
-0.32 ± 0.054
0.64
0.72
740
0.22 ± 0.046
0.92
0.53
521
0.16 ± 0.025
0.64
0.68
2583
(0.63 ± 0.055)
(0.69)
(0.67)
(1.30 ± 0.050)
(0.87)
(0.51)
(1.10 ± 0.024)
(0.61)
(0.68)
Asc
Ocean
0.13 ± 0.063
0.73
-0.13
436
-0.09 ± 0.069
0.51
-0.06
94
-0.19 ± 0.070
0.98
-0.19
755
Land
–
–
–
–
–
–
–
–
–
–
–
–
Dar
Ocean
0.59 ± 0.069
0.65
0.62
337
0.59 ± 0.130
0.56
0.57
73
0.30 ± 0.055
0.69
0.53
600
Land
-0.38 ± 0.026
0.52
0.56
1519
0.21 ± 0.021
0.61
0.43
3103
0.04 ± 0.016
0.59
0.49
5494
Reu
Ocean
0.00 ± 0.048
0.53
0.58
467
0.42 ± 0.084
0.47
0.70
120
0.22 ± 0.045
0.62
0.39
720
Land
0.01 ± 0.046
0.51
0.41
477
0.80 ± 0.066
0.67
0.31
402
0.50 ± 0.044
0.67
0.17
907
Wol
Ocean
-0.47 ± 0.070
0.62
0.58
302
-0.03 ± 0.093
0.58
0.68
151
-0.35 ± 0.079
0.83
0.37
416
Land
-0.42 ± 0.033
0.81
0.55
2339
0.08 ± 0.032
0.81
0.56
2513
-0.06 ± 0.023
0.80
0.56
4688
All
Ocean
0.02 ± 0.032
0.71
0.87
1939
0.04 ± 0.051
0.65
0.87
618
-0.02 ± 0.028
0.81
0.80
3123
Land
-0.35 ± 0.019
0.69
0.81
5075
0.20 ± 0.018
0.74
0.76
6539
0.06 ± 0.012
0.70
0.81
13672
The corrected GOSAT retrieval product is calculated as
ccoralt_cor=αccor.
To avoid additional errors coming from the uncertainties on the gas and
water vapour profiles, we applied the altitude correction only to the GOSAT
products compared with the Izaña TCCON data. Figure 3 shows the time
series of altitude-correction factor of XCO2 and XCH4 for each
algorithm with its own a priori profile as fGdry. Since the
concentrations decrease rapidly above the tropopause, almost all the ratios
for XCH4 are below 1. Additionally, the altitude correction factor has
a seasonal variation which is caused by the seasonal variation of the
tropopause height. The XCO2 altitude-correction factors of NIES and
SRFP are near 1 due to the constant vertical profile of CO2, but the
correction factor of ACOS shows a seasonal variation. This is due to the
strong seasonal fluctuation in near-surface CO2 concentrations of the a
priori CO2 profile of the ACOS algorithm.
Time series plots of TCCON and GOSAT XCO2 measurements based
on the individual data pairs. Left, middle and right panels correspond to
NIES, SRFP and ACOS algorithms, respectively. Red points represent the FTIR
measurements; blue and green ones represent the GOSAT glint data over ocean
and the normal nadir data above land, respectively.
The scatter plots of daily median of XCO2 from FTIR
measurements and different GOSAT algorithms retrievals over 5 TCCON sites.
Only the ocean and land data co-existing within ±1 day are selected;
N is the total number of days. The error bar represents the standard
deviation of all the measurements within ±1 day. The blue and green
points present the glint mode over ocean and the normal nadir mode above
land, respectively.
Time series plots of TCCON and GOSAT XCH4 measurements based
on the individual data pairs. Left, middle and right panels correspond to
NIES, SRFP and SRPR algorithms, respectively. Red, blue and green points
represent the FTIR measurements, the GOSAT glint data over ocean and the
normal nadir data above land, respectively.
The scatter plots of daily median of XCH4 from FTIR
measurements and different GOSAT algorithms retrievals over 5 TCCON sites.
Only the ocean and land data co-existing within ±1 day are selected;
N is the total number of days. The error bar represents the standard
deviation of all the measurements within ±1 day. The blue and green
points present the glint mode over ocean and the normal nadir mode above
land, respectively.
Annual mean bias of ocean data for each TCCON stations from
different algorithms from 2009 to 2013. The error bar represents the
standard deviation. Each colour represents one TCCON site (red: Izaña;
olive green: Ascension Island; green: Darwin; light blue: Reunion Island;
navy blue: Wollongong).
Statistical parameters
After corrections of each TCCON-satellite data pair, several statistical
parameters are derived for each of the five stations. N means the total
number of co-located individual or daily averaged TCCON-satellite data
pairs; R is the Pearson's correlation coefficient between the paired data;
relative bias and scatter are defined as follows:
relative bias=mean(x)×100%,relative scatter=std(x)×100%,
with
x=(XSAT-XTCCON)/XTCCON.
In which XTCCON(SAT) stands for the TCCON or satellite
data product, respectively.
We assume that relative bias follows a Gaussian distribution; then, the 95 % confidence interval of bias is
computed as follows:
(x¯-s/n⋅t0.025(n-1),x¯+s/n⋅t0.025(n-1)),s=1n-1∑i=1n(xi-x¯)2.
Here, t represents the t distribution,
s is the sample standard deviation (relative
scatter), n is the sample size (the number of
individual TCCON-satellite data pairs).
Results
After a priori and altitude correction, the time series of GOSAT retrievals
and TCCON measurements are shown in Figs. 4 and 6 and the statistics are
listed in Tables 3 and 4, for XCO2 and XCH4, respectively. In the
figures, red points represent the FTIR measurements, blue and green ones
correspond to the GOSAT sun glint data over ocean and the normal nadir data
above land, respectively.
XCO2
For XCO2, the products of three full-physics algorithms (NIES, SRFP and
ACOS) have been compared with the TCCON FTIR measurements. In general, both
ocean and land data of all algorithms show good agreement with FTIR
measurements, capturing the seasonal and annual variations of XCO2.
There are several data gaps at each site mainly due to missing TCCON
measurements.
Table 3 summarizes the ocean and land statistical results for 5 TCCON
stations based on all individual TCCON-satellite pairs. Between the brackets
are the results without altitude correction. At each site, the relative
biases of all algorithms are within 0.6 and scatters are within 0.7 %.
Averaged over all TCCON sites (taking all the individual data), the relative
biases of ocean data and land data with 95 % confidence bands are
-0.33 ± 0.018 and -0.13 ± 0.013 % for NIES,
0.03 ± 0.026 and 0.04 ± 0.012 % for SRFP,
0.06 ± 0.011 and -0.03 ± 0.008 % for ACOS. The
correlation between GOSAT ocean and FTIR data is better than that between
GOSAT land and FTIR data, and the scatter for the GOSAT ocean data is
smaller than that for the land data. Although the altitude difference is not
so crucial for XCO2, the biases at Izaña become smaller after
altitude correction, especially for ocean data. ACOS provides the largest
data density both for land and ocean retrievals and NIES has more ocean data
but less land data than SRFP.
The sub-solar latitude changes throughout the year, consequently, the glint
ocean data around each TCCON station only exist in several specific months.
To better compare the ocean data and land data, we choose the GOSAT
soundings when both data co-exist within ±1 day. Figure 5 shows the
scatter plots of daily median of XCO2 from FTIR measurements and
different GOSAT algorithms retrievals over five TCCON stations. The error
bar represents the standard deviation of all the measurements during ±1 day. Due to the unavailability of land data, only ocean data are shown at
Ascension. It is clear that the ocean XCO2 of NIES is smaller than
the land XCO2 or FTIR measurements at Izaña, Ascension, Reunion and
Wollongong. For SRPF and ACOS, the accuracy of the ocean data is close to
that of the land data and the scatter of the ocean data is even less than
that of the land data. However, it is found that the land data of SRFP at
Izaña have a larger bias than those of NIES and ACOS. As the land data
around Izaña are located above the Saharan desert, the reason probably
is that the scattering model applied by SRFP could not account correctly for
the dust aerosol in the atmosphere, or it could be due to the fact that the
gain M bias correction of SRFP is mostly based on comparison with TCCON
stations in Australia.
XCH4
Figure 6 shows the time series of GOSAT XCH4 retrievals from NIES, SRFP
and SRPR together with TCCON FTIR measurements. At first glance, similar to
the results of XCO2, both ocean and land data of all algorithms show
good agreement with FTIR measurements. Note that it has been found that
there is a systematic underestimation of SRPR XCH4 in December 2013
(∼ 10 ppb) due to an error in the XCO2 priori for that
month (not shown). Therefore, SRPR products for that month have been
eliminated. Large variations at the Wollongong site (see Fig. 6) indicate
that there are local methane emissions nearby, which was already
demonstrated by Fraser et al. (2011). They pointed out that emissions from
coal mining are the largest source of methane above background levels at
Wollongong, accounting for 60 % of the surface concentration. As the GOSAT
retrievals from all algorithms also see these variations, the emissions
probably cover a large area.
Table 4 lists the statistical results for XCH4. Almost all the biases
for ocean and land data at all sites are within 0.5 %, and the scatters
are within 1.0 %; this means that they meet the precision threshold
quality criteria for inverse modelling (34 ppb) together with low bias (10 ppb). Although SRFP and SRPR are both derived from the RemoTeC algorithm,
the proxy version (SRPR) has a larger data density than the full physics
version (SRFP) because with the latter, a post-filter is applied that sets a
threshold on the scattering parameters (Butz et al., 2010) . Averaged over
all TCCON sites, the relative bias with 95 % confidence intervals of ocean
data is less than that of the land data for NIES (0.02 ± 0.032 %
vs. -0.35 ± 0.019 %), SRFP (0.04 ± 0.051 vs.
0.20 ± 0.018 %) and SRPR (-0.02 ± 0.028 vs.
0.06 ± 0.012 %). It is found that the XCH4 products of SRFP
have a smaller data density than the XCO2 products for ocean data,
which means that some extra filter was applied to the XCH4 retrievals.
Note that it is indispensable to do altitude correction when comparing the
GOSAT XCH4 retrievals with the FTIR measurements for Izaña. The
altitude-corrected biases between the GOSAT and FTIR are smaller than the
ones obtained without altitude correction, and show similar scatter and
higher correlation coefficient. The bias decrease for ocean data is larger
than that for land data (1.17 and 0.95 % for NIES, 1.21 and
1.08 % for SRFP, 1.20 and 0.94 % for SRPR), because the GOSAT
footprints over ocean have a lower altitude; this could also be recognized
in the time series of altitude-correction factors (see Fig. 3).
Figure 7 shows the scatter plots of XCH4 daily median of FTIR
measurements and different GOSAT retrievals over TCCON sites. As in Fig. 5,
it is found that the land data of SRFP at Izaña have large bias and
scatter. As mentioned at Sect. 4.1, this error probably results from the
dust aerosol in the air. Apart from that, the XCH4 abundances of ocean
data at Darwin are larger than the FTIR measurements, and the biases range
from 0.30 % to 0.59 % for these three algorithms. This systematic bias
may originate in the fact that almost all the ocean footprints near Darwin
site are limited to a small area (near 125∘ E, see Fig. 1),
and are a little bit further away from the FTIR location compared with the
distances at the other four sites. For the other sites, the accuracy of
ocean data of the three algorithms is close to that of the land data.
Stability
The stability here has two meanings. First, the difference of biases (mean
and standard deviation) of each algorithm between 5 TCCON sites to see
spatial distributions of the GOSAT biases. Second, the difference of biases
between each year during analysis period (2009–2013) to see temporal
behaviours of the GOSAT biases. Figure 8 shows the annual mean biases and
corresponding standard deviations of the ocean data from the different
algorithms and molecules at each TCCON station, based on individual
co-located ocean data pairs. Almost all annual mean biases are within 1 %
during the measurement period 2009–2013 and the differences between adjacent
years at are within 0.4 % for XCO2 and 0.7 % for XCH4 at
each station. The maximum differences between each station in the same year
are about 0.3 % for XCO2 and 1.2 % for XCH4. The XCO2
ocean data from ACOS seem more stable than the NIES and SRFP data; their
biases are close to zero and the standard deviations are smaller. The
XCO2 ocean data from NIES have a systematic bias (less than the FTIR
measurements), and their standard deviations are similar to those of SPFP.
The stability of XCH4 ocean data from SRFP tends to be slightly better
than that from NIES and SRPR, but the biases of all three algorithms at
Darwin are quite large compared with other sites in 2009 and 2010. In
addition, we should keep in mind that the XCH4 data from SRFP algorithm
have the lowest data density.
Summary
The XCO2 and XCH4 GOSAT sun glint mode retrievals from NIES
v02.21, SRFP v2.3.5, SRPR v2.3.5 and ACOS v3.5 algorithms were validated
with the FTIR measurements from five TCCON stations and nearby GOSAT land
data. As the GOSAT land data have already been validated with TCCON
measurements in previous studies, we mainly focused on the differences
between ocean data and nearby land data. Due to the low data density of sun
glint mode retrievals, all the GOSAT footprints located within ±5∘ latitude and ±15∘ longitude around
each TCCON site were selected. The a priori profile of TCCON is used as the
common profile to eliminate the differences between GOSAT and FTIR data due
to the use of different a priori profiles in their retrievals. An
altitude-correction method is applied to eliminate the bias due to altitude
differences between the FTIR station location and the GOSAT footprints, but
only in the comparisons made at Izaña; it is particularly important when
comparing the XCH4 data.
For XCO2, NIES, SRFP and ACOS algorithms are all full-physics methods
but with different cloud filters, forward models and inversion schemes. ACOS
provides the largest data density both for land and ocean products and NIES
has more ocean data but less land data than SRFP. Averaged over all TCCON
sites, the relative biases of ocean data and land data with 95 %
confidence intervals are -0.33 ± 0.018 and -0.13 ± 0.013 % for NIES, 0.03 ± 0.026 and 0.04 ± 0.012 %
for SRFP, 0.06 ± 0.011 and -0.03 ± 0.008 % for ACOS,
respectively. Apart from the XCO2 ocean data from NIES indicating a
slight systematic bias, other retrievals show good agreement with TCCON
measurements, among which the ACOS products have the most robust stability.
For all algorithms, the XCH4 retrievals have a worse stability and
smaller precision than the XCO2 retrievals. Although the SRPR and SRFP
are both derived from the RemoTeC algorithm, SRPR provides more data, and its
ocean data show a larger scatter. The lower density of SRFP ocean data
probably results from the application of a severe cloud and aerosol
post-filtering. Averaged over all 5 TCCON sites, the relative bias with
95 % confidence intervals of ocean data is less than that of the land
data for NIES (0.02 ± 0.032 vs. -0.35 ± 0.019 %), SRFP
(0.04 ± 0.051 vs. 0.20 ± 0.018 %) and SRPR
(-0.02 ± 0.028 vs. 0.06 ± 0.012 %) along with the numbers
refer to ocean and to land for NIES (1939 vs. 5075), SRFP (618 vs. 6539) and
SRPR (3123 vs. 13672).