Consistent satellite XCO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; retrievals from SCIAMACHY and GOSAT using the BESD algorithm

Heymann, J.; Reuter, M.; Hilker, M.; Buchwitz, Michael; Schneising, Oliver; Bovensmann, H.; Burrows, J. P.; Kuze, Akihiko; Shimada, Hiroshi; Deutscher, Nicholas M.; Dubey, Manvendra K.; Griffith, David; Hase, Frank; Kawakami, S.; Kivi, Rigel; Morino, Isamu; Petri, Christof; Roehl, Coleen M.; Schneider, Mat­thias; Sherlock, V.; Sussmann, Ralf; Velazco, Voltaire A.; Warneke, Thorsten; Wunch, Debra

doi:10.5194/amt-8-2961-2015

Cited by 50 publications

(41 citation statements)

References 52 publications

(77 reference statements)

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“…Nguyen et al (2014) found that XCO 2 data from GOSAT retrievals compared to groundbased TCCON XCO 2 measurements using three co-location methodologies displayed a positive bias in the range of 0.39 to 4.07 ppm, with standard deviations of 0.39 to 2.37 ppm, and correlation coefficient from 0 to 0.90. In recent studies of Heymann et al (2015), Ohyama et al (2015) and Kulawik et al. (2016), the comparison results also demonstrate the consistency between GOSAT XCO 2 and TCCON XCO 2 .…”

Section: Comparison With Satellite Datamentioning

confidence: 59%

Investigating the performance of a greenhouse gas observatory in Hefei, China

et al. 2017

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Abstract. A ground-based high-resolution Fourier transform spectrometer (FTS) station has been established in Hefei, China to remotely measure CO 2 , CO and other greenhouse gases based on near-infrared solar absorption spectra. Total column measurements of atmospheric CO 2 and CO were successfully obtained from July 2014 to April 2016. The spectra collected with an InSb detector in the first year were compared with those collected by an InGaAs detector from July 2015, demonstrating that InGaAs spectra have better signal-to-noise ratios and rms of spectral fitting residuals relative to InSb spectra. Consequently, the measurement precision of the retrieved XCO 2 and XCO for InGaAs spectra is superior to InSb spectra, with about 0.04 and 0.09 % for XCO 2 , and 1.07 and 2.00 % for XCO within clear-sky days respectively. Daily and monthly averages of columnaveraged dry air mole fraction of CO 2 show a clear seasonal cycle, while the daily and monthly averages of XCO displayed no seasonal variation. Also, we analysed the relationship of the anomalies of XCO and XCO 2 , found that the correlations are only observable for individual days, and the data under different prevailing wind conditions during the observations displayed weak correlation. The observations based on the high-resolution FTS were also compared with the temporally coinciding measurements taken with a low-resolution solar FTS instrument, the EM27/SUN. Ratioing the daily averaged XCO 2 of EM27 and FTS gives an overall calibration factor of 0.996 ± 0.001. We also compared ground-based observations from the Tsukuba TCCON station with our observations, the results showing that the variation in phase and seasonal amplitude of XCO 2 are similar to our results, but the variation of XCO in Tsukuba is quite different from our data in Hefei. To further evaluate our retrieved data, we made use of satellite measurements. The direct comparison of our observations with the Greenhouse Gases Observing Satellite (GOSAT) data shows good agreement of daily median XCO 2 , with a bias of −0.52 ppm and standard deviation of 1.63 ppm. The correlation coefficient (R 2 ) is 0.79 for daily median XCO 2 between our FTS and GOSAT observations. Daily median Orbiting Carbon Observatory 2 (OCO-2) data produce a positive bias of 0.81 ppm and standard deviation of 1.73 ppm relative to our ground-based data. Our daily median XCO 2 also show strong correlation with OCO-2 data, with correlation coefficient (R 2 ) of 0.83. Although there were a limited number of data during the observations due to instrument downtime and adverse weather, the results confirm the suitability of the observatory for ground-based long-term measurements of greenhouse gases with high precision and

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Section: Comparison With Satellite Datamentioning

confidence: 59%

Investigating the performance of a greenhouse gas observatory in Hefei, China

et al. 2017

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“…The TCCON (Total Carbon Column Observing Network; Wunch et al, 2011) is one of the established references for the validation of greenhouse gases measuring space sensors (Reuter et al, 2011;Wunch et al, 2011;Butz et al, 2011;Heymann et al, 2015;Inoue et al, 2016) and has also been used for e.g. model studies of greenhouse gases sources (Messerschmidt et al, 2013) and observation of local sources (Lindenmaier et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Addition of a channel for XCO observations to a portable FTIR spectrometer for greenhouse gas measurements

et al. 2016

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Abstract. The portable FTIR (Fourier transform infrared) spectrometer EM27/SUN, dedicated to the precise and accurate observation of column-averaged abundances of methane and carbon dioxide, has been equipped with a second detector channel, which allows the detection of additional species, especially carbon monoxide. This allows an improved characterisation of observed carbon dioxide enhancements and makes the extended spectrometer especially suitable as a validation tool of ESA's Sentinel 5 Precursor mission, as it now covers the same spectral region as used by the infrared channel of the TROPOMI (TROPOspheric Monitoring Instrument) sensor. The extension presented here does not rely on a dichroic, but instead a fraction of the solar beam is decoupled near the aperture stop of the spectrometer using a small plane mirror. This approach allows maintaining the camera-controlled solar tracker set-up, which is referenced to the field stop in front of the primary detector. Moreover, the upgrade of existing instruments can be performed without alterating the optical set-up of the primary channel and resulting changes of the instrumental characteristics of the original instrument.

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“…In this study, we used the GGG2014 datasets to validate our retrievals with g-b FTS. As reported previously [22,49,50], the TCCON data used in this study represent the mean values measured at each TCCON site within˘1 h of the GOSAT overpass time (around 13:00 local time). The XCO 2 values retrieved from our algorithm were selected from boxed latitude/longitude regions within retrieved from our at the Saga and Tsukuba stations, respectively.…”

Section: Comparison Of Ground-based Fts Data and Xco 2 Retrievalsmentioning

confidence: 99%

Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing

Jung

Kim

et al. 2016

Remote Sensing

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Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO 2 ) using Shortwave Infrared (SWIR) channels, referred to as the Yonsei CArbon Retrieval (YCAR) algorithm. The performance of the YCAR algorithm is here examined using simulated radiance spectra, with simulations conducted using different Aerosol Optical Depths (AODs), Solar Zenith Angles (SZAs) and aerosol types over various surface types. To characterize the XCO 2 retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO 2 retrieval errors and averaging kernels. The a posteriori XCO 2 retrieval errors generally increase with increasing SZA. However, errors were found to be small (<1.3 ppm) over vegetation surfaces. Column averaging kernels are generally close to unity near the surface and decrease with increasing altitude. For dust aerosol with an AOD of 0.3, the retrieval loses its sensitivity near the surface due to the influence of atmospheric scattering, with the peak of column averaging kernels at~800 hPa. In addition, we performed a sensitivity analysis of the principal state vector elements with respect to XCO 2 retrievals. The reference tests with the inherent error of the algorithm showed that overall XCO 2 retrievals work reasonably well. The XCO 2 retrieval errors with respect to state vector elements are shown to be <0.3 ppm. Information on aerosol optical properties is the most important factor affecting the XCO 2 retrieval algorithm. Incorrect information on the aerosol type can lead to significant errors in XCO 2 retrievals of up to 2.5 ppm. The XCO 2 retrievals using the Thermal and Near-infrared Sensor for carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) L1B spectra were biased by 2.78˘1.46 ppm and 1.06˘0.85 ppm at the Saga and Tsukuba sites, respectively. This study provides important information regarding estimations of the effects of aerosol properties on the CO 2 retrieval algorithm. An understanding of these effects can contribute to improvements in the accuracy of XCO 2 retrievals, especially combined with an aerosol retrieval algorithm.

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Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

Cited by 50 publications

References 52 publications

Investigating the performance of a greenhouse gas observatory in Hefei, China

Investigating the performance of a greenhouse gas observatory in Hefei, China

Addition of a channel for XCO observations to a portable FTIR spectrometer for greenhouse gas measurements

Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing

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