Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals using TCCON data from near-ocean sites

Abstract

The thermal And near infrared sensor for carbon observations Fourier transform spectrometer (TANSO-FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) applies the normal nadir mode above the land ("land data") and sun glint mode over the ocean ("ocean data") to provide global distributions of column-averaged dry-air mole fractions of CO₂ and CH₄, or XCO₂ and XCH₄. Several algorithms have been developed to obtain highly accurate greenhouse gas concentrations from TANSO-FTS/GOSAT spectra. So far, all the retrieval algorithms have been validated with the measurements from ground-based Fourier transform spectrometers from the Total Carbon Column Observing Network (TCCON), but limited to the land data. In this paper, the ocean data of the SRPR, SRFP (the proxy and full-physics versions 2.3.5 of SRON/KIT's RemoTeC algorithm), NIES (National Institute for Environmental Studies operational algorithm version 02.21) and ACOS (NASA's Atmospheric CO₂ Observations from Space version 3.5) are compared with FTIR measurements from five TCCON sites and nearby GOSAT land data.</br></br>For XCO₂, both land and ocean data of NIES, SRFP and ACOS show good agreement with TCCON measurements. Averaged over all TCCON sites, the relative biases of ocean data and land data are -0.33 and -0.13-% for NIES, 0.03 and 0.04-% for SRFP, 0.06 and -0.03-% for ACOS, respectively. The relative scatter ranges between 0.31 and 0.49-%. For XCH₄, the relative bias of ocean data is even less than that of the land data for the NIES (0.02 vs. -0.35-%), SRFP (0.04 vs. 0.20-%) and SRPR (-0.02 vs. 0.06-%) algorithms. Compared to the results for XCO₂, the XCH₄ retrievals show larger relative scatter (0.65-0.81-%).

Atmos. Meas. Tech., 9, 14151430, 2016 www.atmos-meas-tech.net/9/1415/2016/ doi:10.5194/amt-9-1415-2016 Author(s) 2016. CC Attribution 3.0 License.

Minqiang Zhou1,2,3, Bart Dils2, Pucai Wang1, Rob Detmers4, Yukio Yoshida5, Christopher W. ODell6,

Dietrich G. Feist7, Voltaire Almario Velazco8, Matthias Schneider9, and Martine De Mazire2

1Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

2Belgian Institute for Space Aeronomy, Brussels, Belgium

3University of Chinese Academy of Sciences, Beijing, China

4SRON Netherlands Institute for Space Research, Utrecht, the Netherlands

5National Institute for Environmental Studies, Tsukuba, Japan

6Colorado State University, Fort Collins, CO, USA

7Max Planck Institute for Biogeochemistry, Jena, Germany

8Centre for Atmospheric Chemistry, University of Wollongong, Wollongong, Australia

9Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany

Correspondence to: Minqiang Zhou ([email protected])

Received: 25 September 2015 Published in Atmos. Meas. Tech. Discuss.: 26 October 2015 Revised: 26 February 2016 Accepted: 18 March 2016 Published: 1 April 2016

Abstract. The thermal And near infrared sensor for carbon observations Fourier transform spectrometer (TANSOFTS) on board the Greenhouse Gases Observing Satellite (GOSAT) applies the normal nadir mode above the land (land data) and sun glint mode over the ocean (ocean data) to provide global distributions of column-averaged dry-air mole fractions of CO2 and CH4, or XCO2 and XCH4. Several algorithms have been developed to obtain highly accurate greenhouse gas concentrations from TANSO-FTS/GOSAT spectra. So far, all the retrieval algorithms have been validated with the measurements from ground-based Fourier transform spectrometers from the Total Carbon Column Observing Network (TCCON), but limited to the land data. In this paper, the ocean data of the SRPR, SRFP (the proxy and full-physics versions 2.3.5 of SRON/KITs RemoTeC algorithm), NIES (National Institute for Environmental Studies operational algorithm version 02.21) and ACOS (NASAs Atmospheric CO2 Observations from Space version 3.5) are compared with FTIR measurements from ve TCCON sites and nearby GOSAT land data.

For XCO2, both land and ocean data of NIES, SRFP and

ACOS show good agreement with TCCON measurements.

Averaged over all TCCON sites, the relative biases of ocean data and land data are 0.33 and 0.13 % for NIES, 0.03

and 0.04 % for SRFP, 0.06 and 0.03 % for ACOS, respec

tively. The relative scatter ranges between 0.31 and 0.49 %.

For XCH4, the relative bias of ocean data is even less than that of the land data for the NIES (0.02 vs. 0.35 %), SRFP

(0.04 vs. 0.20 %) and SRPR (0.02 vs. 0.06 %) algorithms.

Compared to the results for XCO2, the XCH4 retrievals show larger relative scatter (0.650.81 %).

1 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 signicance, 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

Published by Copernicus Publications on behalf of the European Geosciences Union.

Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals using TCCON data from near-ocean sites

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 reect 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 ve 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.

2 Data

2.1 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 ag, 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.

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 TANSOCAI/GOSAT as cloud screening. SRFP is a full physics version, which adjusts parameters of surface, atmosphere and satellite instrument to t 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 modications 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 TikhonovPhillips method is employed in the inversion scheme. Both

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1416 M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals

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 inuenced 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 uxes 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 rst space-based sensor in orbit specically with the purpose of measuring greenhouse gases from high-resolution spectra at SWIR wavelengths. The eld 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 (ODell 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 ll critical gaps in the in situ network, reducing the uncertainty of the surface ux 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 uxes via inverse modelling (Buchwitz et al., 2012).

M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals 1417

Table 1. TANSO-FTS/GOSAT retrieval algorithms.

Molecular Algorithm Institute Time period References

NIES v02.21 NIES 04/200905/2014 Yoshida et al. (2011, 2013) XCO2 SRFP v2.3.5 SRON/KIT 04/200912/2013 Butz et al. (2011)

ACOS v3.5 NASA 04/200906/2014 ODell et al. (2012)

NIES v02.21 NIES 04/200905/2014 Yoshida et al. (2011, 2013) XCH4 SRFP v2.3.5 SRON/KIT 04/200912/2013 Butz et al. (2011)

SRPR v2.3.5 SRON/KIT 04/200912/2013 Schepers et al. (2012)

SRFP and SRPR have applied post-processing and bias correction according to the modied 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

Web End =http://www.esa-ghg-cci.org/sites/default/ http://www.esa-ghg-cci.org/sites/default/files/documents/public/documents/GHG-CCI_DATA.html

Web End =les/documents/public/documents/GHG-CCI_DATA.html http://www.esa-ghg-cci.org/sites/default/files/documents/public/documents/GHG-CCI_DATA.html

Web End = ).

NIES v02.21 also applies the cloud mask from TANSOCAI/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 modication that results from the scattering. In the NIES algorithm, the state vector contains the logarithms of the mass mixing ratios of ne-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/

Web End =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 ltering, state vector, forward model and inversion strategy (Crisp et al., 2012; ODell 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 ts the vertical optical depth of four scattering types together with CO2. The modied 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 mea-

surements by about 11.5 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.

2.2 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(1) for XCO2

and 0.9765 0.002(1) for XCH4) are applied to the TC

CON 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).

As the TANSO-FTS/GOSAT sun glint data over the ocean are limited to latitudes within 20 of the sub-solar latitude, only ve low-latitude and geographically close-to-ocean TC

CON sites are selected (see Table 2, from north to south:

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1418 M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals

Table 2. The locations and start times of TCCON sites.

Site Latitude Longitude Alt (km a.s.l) Start time References

Izaa 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 Grifth et al. (2014a) Reunion Island 20.9 S 55.5 E 0.09 Sep-11 De Mazire et al. (2014) Wollongong 34.4 S 150.8 E 0.03 May-08 Grifth et al. (2014b)

Izaa, 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/

Web End =ftp://tccon.ornl.gov/ .

3 Methodology

3.1 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 nd a sufcient 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 eld at 700 hPa (T700) to dene 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 elds to determine coincidences and Nguyen et al. (2014) used a modied Euclidian distance weighted average of distance, time and mid-tropospheric temperature at 700 hPa. Unfortunately, in the present paper, ve 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 insufcient to obtain stable results.

The co-location area is nally set as 5 latitude 15

longitude around each TCCON site. Within this co-location box, we do not detect any signicant 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 occur-ring 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 typ-

Figure 1. 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.

ical variability (1), 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 0.20.3 % 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.

3.2 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 proles and averaging kernels.

To deal with the a priori issue, TCCON a priori prole is applied as the common a priori prole to correct the satellite retrievals:

ccor = c +Xihi(1 Asati)(xTCCONap,i xsatap,i) (1)

hi =

mi

Pmi , (2)

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; Asati is the column-averaging kernel of the satellite retrieval algorithm of layer i; xTCCONap,i and xsatap,i are the a priori dry-air mole fraction prole of TCCON and satellite algorithm, respectively; hi corresponds to the normal-

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M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals 1419

XCO (corrorig) (ppm)

XCO (corrorig) (ppm)

Land Ocean

Land Ocean

Figure 2. The average of the differences between a priori-corrected and original satellite XCO2 and XCH4 retrievals (corrected original) at ve TCCON stations. Iza, Asc, Dar, Reu and Wol stand for Izaa, Ascension Island, Darwin, Reunion Island and Wollongong. The blue footprints are sun glint data over ocean and the green ones are data above land.

ized airmass-weight function of layer i; mi corresponds to the mass of dry air in layer i.

The prior CO2 proles of ACOS are derived from the output of the Laboratoire de Meteorologie Dynamique (LMDz) model, with uxes optimized to match surface observations.The prior CO2 and CH4 proles of NIES are calculated for every observed day by an ofine global atmospheric transport model developed by the NIES (Maksyutov et al., 2008). The a priori CO2 proles 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.

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 Izaa 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 xed method.As the difference between the interpolation method and xed 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.

3.3 Altitude correction

Different from other stations, the Izaa 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 Izaa 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 Izaa site. To that end, we calculate the ratio ( ) 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 prole shape, as

= cP1G,ak/cP2G,ak. (3)

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) =

V CG(P1orP2)

V Cair(P1orP2) (4)

[integraltext]

PtopP1 or P2

f dryGakdp

gmdryair[1+f dryH2O(mH2O/mdryair)]

=

,

[integraltext]

PtopP1 or P2

dpgmdryair[1+f dryH2O(mH2O/mdryair)]

with

f dryH2O = fH2O/(1 fH2O). (5)

In Eqs. (4) and (5) fH2O and f dryH2O are the mole and dry-air mole fractions of H2O, respectively, f dryG is the a priori dry

air mole fraction of the target gas G; mdryairand 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 that we apply to the satellite data (we always apply the correction factor to the satellite product, not to the TCCON product).

To compute f dryH2O, we use the 6-hour European Centre for Medium-Range Weather Forecasting (ECMWF) interim

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1420 M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals

Time (month/year) Time (month/year) Time (month/year)

Time (month/year) Time (month/year) Time (month/year)

Figure 3. The time series plots of XCO2 and XCH4 altitude-correction factors for different GOSAT algorithms at the Izaa site. Blue data points are sun glint data over ocean and the green ones are data above land.

reanalysis specic humidity (SH), interpolated linearly in space and time to the GOSAT eld 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/(mdryairf dryair + mH2OfH2O), (6) and thus

f dryH2O = (mdryair/mH2O) SH/(1 SH). (7)

Equation (4) can then be rewritten as

cG,ak(P1 or P2) =

V CG,ak(P1 or P2)

V Cair(P1 or P2) (8)

=

[integraltext]

PtopP1 or P2

f dryGakdp

gmdryair[1+SH/(1SH)]

[integraltext]

.

PtopP1 or P2

dp gmdryair[1+SH/(1SH)]

The correction factor (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 Izaa, the altitude of FTIR station is generally higher than that of GOSAT footprint; therefore P1 < P2, and the a priori prole of satellite product is used as f dryG 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 prole of TCCON would be used as f dryG.

The corrected GOSAT retrieval product is calculated as

calt_corcor = ccor. (9)

To avoid additional errors coming from the uncertainties on the gas and water vapour proles, we applied the altitude correction only to the GOSAT products compared with the Izaa TCCON data. Figure 3 shows the time series of altitude-correction factor of XCO2 and XCH4 for each algorithm with its own a priori prole as f dryG. 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 prole of CO2, but the correction factor of

ACOS shows a seasonal variation. This is due to the strong seasonal uctuation in near-surface CO2 concentrations of the a priori CO2 prole of the ACOS algorithm.

3.4 Statistical parameters

After corrections of each TCCON-satellite data pair, several statistical parameters are derived for each of the ve stations. N means the total number of co-located individual or daily averaged TCCON-satellite data pairs; R is the Pearsons correlation coefcient between the paired data; relative bias and scatter are dened as follows:

relative bias = mean(x) 100%, (10)

relative scatter = std(x) 100%, (11)

with

x = (XSAT XTCCON)/XTCCON. (12)

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 % condence interval of bias is computed as follows:

( x s/pn t0.025(n 1), x + s/pn t0.025(n 1)), (13)

s =

[radicaltp]

[radicalvertex]

[radicalvertex]

[radicalbt]

1n 1

n

Xi=1(xi x)2. (14)

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).

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M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals 1421

Table 3. XCO2 results of NIES, SRFP and ACOS algorithms at 5 TCCON stations based on all individual satelliteTCCON data pairs. The 95 % condence interval of relative bias, relative scatter, R and N are dened 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_XCO295 % 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

Table 4. XCH4 results of NIES, SRFP and SRPR algorithms at 5 TCCON stations based on all individual satelliteTCCON data pairs. The 95 % condence interval of relative bias, relative scatter, R and N are dened 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_XCH495 % 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

4 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 gures, 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.

4.1 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 within0.7 %. Averaged over all TCCON sites (taking all the individual data), the relative biases of ocean data and land data with 95 % condence 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 Izaa become smaller after altitude cor-

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1422 M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals

Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

Figure 4. 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.

rection, 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 specic 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 ve TCCON stations. The error bar represents the standard deviation of all the measurements during 1 day. Due to the un

availability 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 Izaa, 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 Izaa have a larger bias than those of NIES and ACOS. As the land data around Izaa 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.

4.2 XCH4

Figure 6 shows the time series of GOSAT XCH4 retrievals from NIES, SRFP and SRPR together with TCCON FTIR measurements. At rst 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 algo-

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Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

Time (month/year)

M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals 1423

Figure 5. 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.

rithms 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-

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1424 M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals

Time (month/year) Time (month/year) Time (month/year)

Time (month/year) Time (month/year) Time (month/year)

Time (month/year) Time (month/year) Time (month/year)

Time (month/year) Time (month/year) Time (month/year)

Time (month/year) Time (month/year) Time (month/year)

Figure 6. 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.

lter is applied that sets a threshold on the scattering parameters (Butz et al., 2010) . Averaged over all TCCON sites, the relative bias with 95 % condence 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 lter 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 Izaa. 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 coefcient. 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 Izaa 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.

4.3 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 (20092013) to see temporal behaviours of the GOSAT biases. Figure 8 shows the annual mean biases and corresponding standard deviations of

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M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals 1425

Figure 7. 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.

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 20092013 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

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1426 M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals

Bias (%)

Year

Bias (%)

Year

Bias (%)

Bias (%)

Year

Year

Figure 8. 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: Izaa; olive green: Ascension Island; green: Darwin; light blue: Reunion Island; navy blue: Wollongong).

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.

5 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 ve 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 lon

gitude around each TCCON site were selected. The a priori prole of TCCON is used as the common prole to eliminate the differences between GOSAT and FTIR data due to the use of different a priori proles 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 Izaa; 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 lters, 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 % condence intervals are 0.33 0.018 and

Bias (%)

Year

Bias (%)

Year

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, re

spectively. 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-ltering. Averaged over all 5 TCCON sites, the relative bias with 95 % condence 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).

Acknowledgements. This work is supported by the National Natural Science Foundation of China (41575034) and the National BasicResearch Program of China (2013CB955801) and the Belgian contribution by the ESA Climate Change Initiative-GreenhouseGases project. The TCCON measurements at Ile de La Reunion are supported by the EU FP7 project ICOS_Inwire, as well as the Belgian support to ICOS and to the AGACCII project of theScience for Sustainable Development programme. The TCCON station on Ascension Island has been funded by the Max PlanckInstitute for Biogeochemistry. The operation of the Izaa FTIR instrument has been very importantly supported by O. E. Garca and E. Seplveda, which are contracted by the MeteorologicalState Agency of Spain (AEMET). Measurements at Darwin andWollongong are supported by Australian Research Council grantsDP0879468, DP110103118 & DP140101552. Darwin TCCON is also supported by the Australian Bureau of Meteorology and NASAs Orbiting Carbon Observatory Project. TCCON data were obtained from the TCCON Data Archive, hosted by

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M. Zhou et al.: Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals 1427

the Carbon Dioxide Information Analysis Center (CDIAC) ftp://tccon.ornl.gov/

Web End =ftp://tccon.ornl.gov/ . The ACOS/GOSAT retrievals were developed and carried out at the NASA Jet Propulsion Laboratory and Colorado State University, with funding from the NASA ACOS project. The SRON/GOSAT has been supported by the ESA Climate Change Initiative-Greenhouse Gases project. The authors thank D. Wunch for useful comments to the manuscript. The authors also wish to thank the Universit de la Runion, as well as the French regional and national (INSU, CNRS) organizations, for supporting the TCCON operations in Reunion Island. Filip Desmet (used to work at BIRA-IASB) and Jean-Marc Metzger (UMS3365 of the OSU Runion) are also acknowledged for their support in the operation of the Reunion Island FTIR instrument.

Edited by: J. Notholt

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