Consistent and accurate long-term data sets of global atmospheric
concentrations of carbon dioxide (CO

Space-based observations of carbon dioxide (CO

The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY
(SCIAMACHY) on-board the European Space Agency's (ESA) Environmental Satellite
(ENVISAT)

The Thermal And Near infrared Sensor for carbon Observations Fourier
Transform Spectrometer (TANSO-FTS) on-board the Greenhouse gases Observing
SATellite (GOSAT)

Several retrieval algorithms have been developed to evaluate the satellite
observations for SCIAMACHY

Carbon cycle and climate-related research requires consistent and accurate
long-term global CO

This paper is structured as follows: in Sects. 2 and 3, relevant aspects of
the SCIAMACHY and TANSO-FTS instruments are discussed. Section 4 gives
a short overview of the SCIAMACHY BESD retrieval algorithm whereas in Sect. 5
the recently developed GOSAT BESD XCO

The satellite instrument SCIAMACHY

The SCIAMACHY instrument was a passive remote sensing moderate-resolution
imaging spectrometer and measured sunlight transmitted, reflected and
scattered by the Earth's atmosphere or surface in the ultraviolet, visible
and near-infrared wavelength regions in eight spectral channels (214–1750,
1940–2040, 2265–2380

For the work presented in this study the nadir mode observations in channel 4
(755–775

GOSAT was the first satellite mission dedicated to measuring atmospheric
XCO

GOSAT carries two satellite instruments, the TANSO-FTS and the Cloud and
Aerosol Imager (TANSO-CAI). The TANSO-FTS is a double pendulum
interferometer. It measures two orthogonal polarisation directions of
reflected or scattered sunlight in three bands (bands 1, 2, 3) in the SWIR
between 4800 and 13 200

The TANSO-CAI instrument is a high spatial resolution imager detecting clouds and optically thick aerosol layers within the TANSO-FTS field of view. The TANSO-CAI data products are not used for the BESD algorithm.

The BESD retrieval algorithm has been developed at the University of Bremen
to retrieve XCO

The algorithm is a core algorithm within ESA's CCI

The BESD algorithm retrieves several independent parameters from the
O

Atmospheric scattering is considered by fitting three scattering-related
parameters. A thin ice cloud layer consisting of fractal ice crystals with
50 µm effective radius and a thickness of 0.5

Coefficients for empirical noise for GOSAT high (H) and medium (M) gain observations over land.

The forward model is the radiative transfer model SCIATRAN

Although BESD has been designed to minimise scattering-related retrieval errors, clouds are still an important potential error source and strict cloud filtering is necessary. BESD filters clouds by using cloud information based on measurements of the Medium Resolution Imaging Spectrometer (MERIS).

The post-processing of the retrieved data includes strict quality filtering
and an empirical bias correction. This is needed due to the demanding
accuracy requirements on the satellite retrievals. The implemented bias
correction for SCIAMACHY BESD is described in the BESD ATBD

The GOSAT BESD algorithm is based on the SCIAMACHY BESD algorithm which has
been modified to also retrieve XCO

GOSAT BESD uses GOSAT Level 1B data (L1B) version 161160. These data have
been obtained from the GOSAT User Interface Gateway
(

The a priori information includes profiles of temperature, pressure and
humidity obtained from ECMWF data and height information from a digital
elevation model (DEM). The used DEM (obtained from

The GOSAT XCO

The state vector of GOSAT BESD consists of 38 elements instead of 26 for
SCIAMACHY BESD. The state vector elements, their a priori values and
uncertainties are listed in Table

Here,

A temperature shift, the column-averaged mole fraction of water vapour and
the surface pressure are fitted as for SCIAMACHY BESD and also the CO

Contributions from plant fluorescence and the impact of a non-linearity
response of the incident radiation to the intensity in the mostly affected
band 1 can be reduced by fitting a wavenumber independent offset (also called
zero-level offset)

The fit parameters defining atmospheric scattering are the same as for
SCIAMACHY BESD, namely CWP, CTH and APS. The defined thin cloud layer
consists of fractal ice particles with an effective radius of
100

The much higher spectral resolution of GOSAT is the reason why the radiative
transfer model SCIATRAN cannot run in the implemented computational efficient
correlated-k mode used for SCIAMACHY BESD. However, in order to accelerate
the radiative transfer calculations for GOSAT BESD retrievals, tabulated
cross sections

In Fig.

State vector elements of the GOSAT BESD retrieval algorithm.

Observed (black) and fitted (red) intensity (radiance) and its
residuum (blue) over a typical scene in Germany, near Berlin
(52.42

Parameters and thresholds as used for the quality filtering. A scene
is considered to be of “good” quality if e.g. the albedo difference
between the fitted and a priori albedo in band 2 (albedo difference, weak
CO

Even thin clouds are a main error source for satellite XCO

Used TCCON sites, their location, altitude (above sea level) and used observation period.

The high demands on the satellite retrievals require strict quality filtering
not only for clouds. In order to minimise biases and to reduce the scatter of
the data, GOSAT BESD uses filter thresholds for selected parameters. The used
parameters and their filter thresholds have been selected by evaluating GOSAT
XCO

Systematic errors have been additionally reduced by using a global bias
correction scheme

To reduce the systematic errors in the GOSAT BESD XCO

Two-dimensional histograms of non-bias-corrected (left) and standard
(bias-corrected, right) GOSAT BESD–TCCON XCO

The coefficients found by multivariate linear regression are

The quality of the satellite XCO

TCCON stations used for validation.

The Total Carbon Column Observing Network (TCCON)

The first part of this study is the validation of the GOSAT BESD (available
for January 2010–December 2013) and SCIAMACHY BESD XCO

The comparison between different CO

All TCCON measurements 2 hours before or after the satellite measurement
and all satellite data within a

Four values have been obtained from the comparisons of the data sets at the TCCON sites: (i) the number of collocated data points, (ii) the mean difference between the data sets (can be interpreted as a regional bias), (iii) the standard deviation of the difference (is an estimate of the precision when compared with TCCON) and (iv) the linear correlation coefficient between the data sets.

SCIAMACHY BESD (black), GOSAT BESD (green) and TCCON (red) XCO

Scatter plots of individual satellite vs. TCCON XCO

Figure

In Fig.

Results of the comparison between GOSAT BESD and TCCON XCO

As Table

In more detail, the comparison results between GOSAT BESD XCO

Table

In order to summarise the results, we calculate the mean standard deviation of the difference (can be interpreted as an upper limit for the single measurement precision) and the standard deviation of the station biases, which we interpret as the station-to-station bias deviation (short: station-to-station bias). For the sake of completeness, we also calculate the mean of the station biases (mean offset) and the mean correlation coefficient. However, the mean offset is less relevant as it can be easily adjusted. In order to determine robust values, we have excluded TCCON stations with less than 30 measurements in one of the comparisons, i.e. Tsukuba, JPL, Saga, Izaña and Lauder are not considered.

The full data set analysis (GOSAT: January 2010–December 2013; SCIAMACHY:
August 2002–March 2012) shows for the standard GOSAT BESD data set a mean
offset of

The SCIAMACHY BESD data have a mean offset of

As Fig.

Results of the comparison of daily averages of (standard) GOSAT,
SCIAMACHY and TCCON XCO

For the comparison of the validation results of GOSAT BESD and SCIAMACHY
BESD, we have used the time period 2010 to 2011 where both data sets overlap.
Both data sets have a negative station bias e.g. at Bremen
(

Overall, the analysis results for the time period 2010–2011 are similar to
the results obtained for the full data set analysis. In both comparisons, the
mean offset is negative (

Results of the comparison of daily means of GOSAT BESD, SCIAMACHY BESD and
TCCON XCO

Global maps of XCO

As Fig.

A more detailed comparison is shown in Table

The differences between the satellite data are likely due to non-perfect
collocations (observed air masses are not identical) and potentially due to
a non-perfect BESD retrieval algorithm. However, the similar scatter of the
difference between the data sets compared to the difference to TCCON, the high
correlation coefficient of the station biases and the smaller/similar
standard deviation of the mean differences of the data sets compared to the
station-to-station bias indicate a high degree of consistency between the
SCIAMACHY and GOSAT XCO

In addition to the comparisons with TCCON, we have also compared the BESD
data sets with the model results of CarbonTracker. For this purpose, we have
used data of 4 months in 2011: we selected April–May when the
atmospheric CO

CarbonTracker is NOAA's modelling and assimilation system and has been
developed to estimate global CO

Figure

The results for August–September 2011 are shown in
Fig.

The remaining differences between GOSAT and SCIAMACHY BESD are likely due to the non-perfect spatial and temporal collocations and a non-perfect BESD algorithm. However, the smaller/similar differences of the BESD data sets as compared to CarbonTracker are another indication for the high degree of consistency between GOSAT and SCIAMACHY BESD.

As consistent long-term data sets of XCO

The quality of the BESD data products was estimated by a validation study
using TCCON observations. This comparison showed that the GOSAT BESD XCO

In order to evaluate the consistency of the satellite data products, we compared the data products with the TCCON data for the same time period and performed a direct comparison of the satellite data.

The comparison of the validation results for the years 2010–2011, when the
observation periods of SCIAMACHY and GOSAT overlap, showed for both data sets
a small mean offset (

The GOSAT BESD and SCIAMACHY BESD XCO

In a direct comparison of the satellite data, we analysed daily averages of
GOSAT and SCIAMACHY BESD XCO

We have also compared global monthly maps and latitudinal averages of the
satellite data sets with CarbonTracker XCO

The remaining differences found between GOSAT and SCIAMACHY are likely not only due to
non-perfect collocation (i.e. the observed air masses can be not identical)
but likely also to a non-perfect BESD retrieval algorithm. However, the
similar scatter of the difference between the data sets compared to the
difference to TCCON and CarbonTracker and the smaller/similar
station-to-station variation of the differences of the data sets compared to
the difference to TCCON indicate a high degree of consistency between the
SCIAMACHY and GOSAT XCO

Our overarching goal is to generate a satellite-derived XCO

We thank JAXA, NIES and ESA for providing us with the GOSAT L1B and L2 IDS
data. We are also grateful to Jonathan de Ferranti for the development of the
digital elevation model, which we used for our evaluations. We thank TCCON
for providing FTS XCO