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Nicholas M Deutscher - One of the best experts on this subject based on the ideXlab platform.

  • estimates of european uptake of co2 inferred from gosat xco2 retrievals sensitivity to measurement bias inside and outside europe
    Atmospheric Chemistry and Physics, 2016
    Co-Authors: Liang Feng, Nicholas M Deutscher, Dietrich G Feist, Isamu Morino, Robert Parker, Paul I Palmer, Rigel Kivi, Ralf Sussmann
    Abstract:

    Abstract. Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.

  • does gosat capture the true seasonal cycle of carbon dioxide
    Atmospheric Chemistry and Physics, 2015
    Co-Authors: Nicholas M Deutscher, Frederic Chevallier, Hartmut Boesch, Hannakaisa Lindqvist, Christopher W Odell, Sourish Basu, Liang Feng
    Abstract:

    Abstract. The seasonal cycle accounts for a dominant mode of total column CO2 (XCO2) annual variability and is connected to CO2 uptake and release; it thus represents an important quantity to test the accuracy of the measurements from space. We quantitatively evaluate the XCO2 seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO2 Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO2, as well as the regional growth rates in XCO2 through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO2 between ACOS and TCCON, but ACOS generally infers a date of maximum XCO2 2–3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO2. In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO2 depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO2 doubles from western USA to East Asia at 45–50° N, which is only partially shown by the models. In general, we find that model-to-model differences can be larger than GOSAT-to-model differences. These results suggest that GOSAT/ACOS retrievals of the XCO2 seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.

  • consistent satellite xco 2 retrievals from sciamachy and gosat using the besd algorithm
    Atmospheric Measurement Techniques, 2015
    Co-Authors: J Heymann, Heinrich Bovensmann, M Buchwitz, J P Burrows, Akihiko Kuze, Hiroshi Suto, M Reuter, Michael Hilker, Oliver Schneising, Nicholas M Deutscher
    Abstract:

    Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

  • atmospheric carbon dioxide retrieved from the greenhouse gases observing satellite gosat comparison with ground based tccon observations and geos chem model calculations
    Journal of Geophysical Research, 2012
    Co-Authors: Austin Cogan, Nicholas M Deutscher, Hartmut Boesch, Liang Feng, Robert Parker, Paul I Palmer, Jf Blavier, Ronald Macatangay, Justus Notholt, Coleen M Roehl
    Abstract:

    We retrieved column-averaged dry air mole fractions of atmospheric carbon dioxide (X_CO_2) from backscattered short-wave infrared (SWIR) sunlight measured by the Japanese Greenhouse gases Observing SATellite (GOSAT). Over two years of X_CO_2 retrieved from GOSAT is compared with X_CO_2 inferred from collocated SWIR measurements by seven ground-based Total Carbon Column Observing Network (TCCON) stations. The average difference between GOSAT and TCCON X_CO_2 for individual TCCON sites ranges from −0.87 ppm to 0.77 ppm with a mean value of 0.1 ppm and standard deviation of 0.56 ppm. We find an average bias between all GOSAT and TCCON X_CO_2 retrievals of −0.20 ppm with a standard deviation of 2.26 ppm and a correlation coefficient of 0.75. One year of XCO2 was retrieved from GOSAT globally, which was compared to global 3-D GEOS-Chem chemistry transport model calculations. We find that the latitudinal gradient, seasonal cycles, and spatial variability of GOSAT and GEOS-Chem agree well in general with a correlation coefficient of 0.61. Regional differences between GEOS-Chem model calculations and GOSAT observations are typically less than 1 ppm except for the Sahara and central Asia where a mean difference between 2 to 3 ppm is observed, indicating regional biases in the GOSAT X_CO_2 retrievals unobserved by the current TCCON network. Using a bias correction scheme based on linear regression these regional biases are significantly reduced, approaching the required accuracy for surface flux inversions.

  • methane observations from the greenhouse gases observing satellite comparison to ground based tccon data and model calculations
    Geophysical Research Letters, 2011
    Co-Authors: Robert Parker, Nicholas M Deutscher, Hartmut Boesch, Liang Feng, Austin Cogan, Paul I Palmer, Annemarie Fraser, J Messerschmidt, David W T Griffith
    Abstract:

    We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (X_(CH4)) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT X_(CH4) retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the X_(CH4) retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT X_(CH4) retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed X_(CH4) are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis.

Akihiko Kuze - One of the best experts on this subject based on the ideXlab platform.

  • consistent satellite xco 2 retrievals from sciamachy and gosat using the besd algorithm
    Atmospheric Measurement Techniques, 2015
    Co-Authors: J Heymann, Heinrich Bovensmann, M Buchwitz, J P Burrows, Akihiko Kuze, Hiroshi Suto, M Reuter, Michael Hilker, Oliver Schneising, Nicholas M Deutscher
    Abstract:

    Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

  • oco gosat preflight cross calibration experiment
    IEEE Transactions on Geoscience and Remote Sensing, 2010
    Co-Authors: Fumihiro Sakuma, Carol J Bruegge, David M Rider, D Brown, S Geier, S Kawakami, Akihiko Kuze
    Abstract:

    The Orbiting Carbon Observatory (OCO) by NASA's Jet Propulsion Laboratory (JPL) and Greenhouse gases Observing SATellite (GOSAT) by JAXA were built to provide independent measures of the global distributions of carbon dioxide (CO2) from space. GOSAT achieved a successful orbit on January 23, 2009, and OCO failed its launch attempt on February 24, 2009. Both sensors detect absorptions at the 0.76-?m oxygen band and at the weak and strong CO2 bands at 1.6 and 2.0 ?m, respectively. In order to establish the uncertainties and biases between the respective data products, the OCO and GOSAT teams have planned a number of cross-comparison studies. The first of these, discussed here, is the validation of the prelaunch absolute radiometric calibrations, specified at ±5%. The cross-comparison campaign to validate this OCO approach was performed at NASA's JPL in April 2008. In this paper, the OCO reference detectors and three GOSAT radiometers viewed the OCO sphere and radiometric standard. The overall agreements between the OCO calibration and GOSAT measurement of the OCO integrating sphere were 1.5% at 0.76 ?m, 2.7% ± 1.1% at 1.6 ?m, and 0.2% ± 4.1% at 2.0 ?m. To validate the GOSAT preflight calibration, the cross-calibration experiment continued at JAXA's Tsukuba Space Center in December 2008, where the same radiometers measured the two GOSAT spheres. Agreements are better than 1.8% at 0.76 ?m, 1.6% at 1.6 ?m, and 1.4% at 2 ?m. These studies give confirmation that the flight instruments have been calibrated to within their uncertainty requirements.

  • thermal and near infrared sensor for carbon observation fourier transform spectrometer on the greenhouse gases observing satellite for greenhouse gases monitoring
    Applied Optics, 2009
    Co-Authors: Akihiko Kuze, Hiroshi Suto, Masakatsu Nakajima, Takashi Hamazaki
    Abstract:

    The Greenhouse Gases Observing Satellite (GOSAT) monitors carbon dioxide (CO2) and methane (CH4) globally from space using two instruments. The Thermal and Near Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) detects gas absorption spectra of the solar short wave infrared (SWIR) reflected on the Earth's surface as well as of the thermal infrared radiated from the ground and the atmosphere. TANSO-FTS is capable of detecting three narrow bands (0.76, 1.6, and 2.0 μm) and a wide band (5.5-14.3 μm) with 0.2 cm−1 spectral resolution (interval). The TANSO Cloud and Aerosol Imager (TANSO-CAI) is an ultraviolet (UV), visible, near infrared, and SWIR radiometer designed to detect cloud and aerosol interference and to provide the data for their correction. GOSAT is placed in a sun-synchronous orbit 666 km at 13:00 local time, with an inclination angle of 98 °. A brief overview of the GOSAT project, scientific requirements, instrument designs, hardware performance, on-orbit operation, and data processing is provided.

  • the instrumentation and the bbm test results of thermal and near infrared sensor for carbon observation tanso on gosat
    Proceedings of SPIE, 2006
    Co-Authors: Akihiko Kuze, Tomoyuki Urabe, Hiroshi Suto, Yutaka Kaneko, Takashi Hamazaki
    Abstract:

    The Greenhouse gases Observing SATellite (GOSAT) is a satellite to monitor the carbon dioxide (CO2) and the methane (CH4) globally from orbit. GOSAT will be placed in a 666 km sun-synchronous orbit of 13:00 local time, with an inclination angle of 98 deg. Two instruments are accommodated on GOSAT. Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) detects the Short wave infrared (SWIR) reflected on the earth's surface as well as the thermal infrared (TIR) radiated from the ground and the atmosphere. TANSO-FTS is capable of detecting wide spectral coverage, specifically, three narrow bands (0.76, 1.6, and 2 micron) and a wide band (5.5-14.3 micron) with 0.2 cm-1 spectral resolution. TANSO Cloud and Aerosol Imager (TANSO-CAI) is a radiometer of ultraviolet (UV), visible, and SWIR to correct cloud and aerosol interference. The paper presents the instrument design of TANSO-FTS/CAI, and test results using Bread Board Model (BBM) are presented.

Isamu Morino - One of the best experts on this subject based on the ideXlab platform.

  • estimates of european uptake of co2 inferred from gosat xco2 retrievals sensitivity to measurement bias inside and outside europe
    Atmospheric Chemistry and Physics, 2016
    Co-Authors: Liang Feng, Nicholas M Deutscher, Dietrich G Feist, Isamu Morino, Robert Parker, Paul I Palmer, Rigel Kivi, Ralf Sussmann
    Abstract:

    Abstract. Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.

  • validation of xch 4 derived from swir spectra of gosat tanso fts with aircraft measurement data
    Atmospheric Measurement Techniques, 2014
    Co-Authors: Makoto Inoue, Isamu Morino, Osamu Uchino, Yuki Miyamoto, Tazu Saeki, Yukio Yoshida, Tatsuya Yokota, Colm Sweeney, P P Tans
    Abstract:

    Abstract. Column-averaged dry-air mole fractions of methane (XCH4), retrieved from Greenhouse gases Observing SATellite (GOSAT) short-wavelength infrared (SWIR) spectra, were validated by using aircraft measurement data from the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. In the calculation of XCH4 from aircraft measurements (aircraft-based XCH4), other satellite data were used for the CH4 profiles above the tropopause. We proposed a data-screening scheme for aircraft-based XCH4 for reliable validation of GOSAT XCH4. Further, we examined the impact of GOSAT SWIR column averaging kernels (CAK) on the aircraft-based XCH4 calculation and found that the difference between aircraft-based XCH4 with and without the application of the GOSAT CAK was less than ±9 ppb at maximum, with an average difference of −0.5 ppb. We compared GOSAT XCH4 Ver. 02.00 data retrieved within ±2° or ±5° latitude–longitude boxes centered at each aircraft measurement site with aircraft-based XCH4 measured on a GOSAT overpass day. In general, GOSAT XCH4 was in good agreement with aircraft-based XCH4. However, over land, the GOSAT data showed a positive bias of 1.5 ppb (2.0 ppb) with a standard deviation of 14.9 ppb (16.0 ppb) within the ±2° (±5°) boxes, and over ocean, the average bias was 4.1 ppb (6.5 ppb) with a standard deviation of 9.4 ppb (8.8 ppb) within the ±2° (±5°) boxes. In addition, we obtained similar results when we used an aircraft-based XCH4 time series obtained by curve fitting with temporal interpolation for comparison with GOSAT data.

  • validation of xco 2 derived from swir spectra of gosat tanso fts with aircraft measurement data
    Atmospheric Chemistry and Physics, 2013
    Co-Authors: Makoto Inoue, Isamu Morino, Osamu Uchino, Yuki Miyamoto, Yukio Yoshida, Tatsuya Yokota, Toshinobu Machida, Yousuke Sawa, H Matsueda, Colm Sweeney
    Abstract:

    Abstract. Column-averaged dry air mole fractions of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were validated with aircraft measurements by the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. To calculate XCO2 based on aircraft measurements (aircraft-based XCO2), tower measurements and model outputs were used for additional information near the surface and above the tropopause, respectively. Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAKs) and the shape of a priori profiles on the aircraft-based XCO2 calculation. The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than ±0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. We compared GOSAT data retrieved within ±2 or ±5° latitude/longitude boxes centered at each aircraft measurement site to aircraft-based data measured on a GOSAT overpass day. The results indicated that GOSAT XCO2 over land regions agreed with aircraft-based XCO2, except that the former is biased by −0.68 ppm (−0.99 ppm) with a standard deviation of 2.56 ppm (2.51 ppm), whereas the averages of the differences between the GOSAT XCO2 over ocean and the aircraft-based XCO2 were −1.82 ppm (−2.27 ppm) with a standard deviation of 1.04 ppm (1.79 ppm) for ±2° (±5°) boxes.

  • influence of aerosols and thin cirrus clouds on the gosat observed co 2 a case study over tsukuba
    Atmospheric Chemistry and Physics, 2011
    Co-Authors: Osamu Uchino, Isamu Morino, Yukio Yoshida, Nobuyuki Kikuchi, Tetsu Sakai, Tomohiro Nagai, Atsushi Shimizu, Takashi Shibata, Akihiro Yamazaki, Akihiro Uchiyama
    Abstract:

    Abstract. Lidar observations of vertical profiles of aerosols and thin cirrus clouds were made at Tsukuba (36.05° N, 140.12° E), Japan, to investigate the influence of aerosols and thin cirrus clouds on the column-averaged dry-air mole fraction of carbon dioxide (XCO2) retrieved from observation data of the Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer, measured in the Short-Wavelength InfraRed band (TANSO-FTS SWIR), onboard the Greenhouse gases Observing SATellite (GOSAT). The lidar system measured the backscattering ratio, depolarization ratio, and/or the wavelength exponent of atmospheric particles. The lidar observations and ground-based high-resolution FTS measurements at the Tsukuba Total Carbon Column Observing Network (Tsukuba TCCON) site were recorded simultaneously during passages of GOSAT over Tsukuba. GOSAT SWIR XCO2 data (Version 01.xx) released in August 2010 were compared with the lidar and Tsukuba TCCON data. High-altitude aerosols and thin cirrus clouds had a large impact on the GOSAT SWIR XCO2 results. By taking into account the observed aerosol/cirrus vertical profiles and using a more adequate solar irradiance database in the GOSAT SWIR retrieval, the difference between the GOSAT SWIR XCO2 data and the Tsukuba TCCON data was reduced. The 3-band retrieval approach where the aerosol and cirrus profiles were retrieved gave us the best results and the retrieved XCO2 data followed the seasonal cycle of ~8 ppm observed at Tsukuba TCCON site.

Tatsuya Yokota - One of the best experts on this subject based on the ideXlab platform.

  • validation of xch 4 derived from swir spectra of gosat tanso fts with aircraft measurement data
    Atmospheric Measurement Techniques, 2014
    Co-Authors: Makoto Inoue, Isamu Morino, Osamu Uchino, Yuki Miyamoto, Tazu Saeki, Yukio Yoshida, Tatsuya Yokota, Colm Sweeney, P P Tans
    Abstract:

    Abstract. Column-averaged dry-air mole fractions of methane (XCH4), retrieved from Greenhouse gases Observing SATellite (GOSAT) short-wavelength infrared (SWIR) spectra, were validated by using aircraft measurement data from the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. In the calculation of XCH4 from aircraft measurements (aircraft-based XCH4), other satellite data were used for the CH4 profiles above the tropopause. We proposed a data-screening scheme for aircraft-based XCH4 for reliable validation of GOSAT XCH4. Further, we examined the impact of GOSAT SWIR column averaging kernels (CAK) on the aircraft-based XCH4 calculation and found that the difference between aircraft-based XCH4 with and without the application of the GOSAT CAK was less than ±9 ppb at maximum, with an average difference of −0.5 ppb. We compared GOSAT XCH4 Ver. 02.00 data retrieved within ±2° or ±5° latitude–longitude boxes centered at each aircraft measurement site with aircraft-based XCH4 measured on a GOSAT overpass day. In general, GOSAT XCH4 was in good agreement with aircraft-based XCH4. However, over land, the GOSAT data showed a positive bias of 1.5 ppb (2.0 ppb) with a standard deviation of 14.9 ppb (16.0 ppb) within the ±2° (±5°) boxes, and over ocean, the average bias was 4.1 ppb (6.5 ppb) with a standard deviation of 9.4 ppb (8.8 ppb) within the ±2° (±5°) boxes. In addition, we obtained similar results when we used an aircraft-based XCH4 time series obtained by curve fitting with temporal interpolation for comparison with GOSAT data.

  • validation of xco 2 derived from swir spectra of gosat tanso fts with aircraft measurement data
    Atmospheric Chemistry and Physics, 2013
    Co-Authors: Makoto Inoue, Isamu Morino, Osamu Uchino, Yuki Miyamoto, Yukio Yoshida, Tatsuya Yokota, Toshinobu Machida, Yousuke Sawa, H Matsueda, Colm Sweeney
    Abstract:

    Abstract. Column-averaged dry air mole fractions of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were validated with aircraft measurements by the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. To calculate XCO2 based on aircraft measurements (aircraft-based XCO2), tower measurements and model outputs were used for additional information near the surface and above the tropopause, respectively. Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAKs) and the shape of a priori profiles on the aircraft-based XCO2 calculation. The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than ±0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. We compared GOSAT data retrieved within ±2 or ±5° latitude/longitude boxes centered at each aircraft measurement site to aircraft-based data measured on a GOSAT overpass day. The results indicated that GOSAT XCO2 over land regions agreed with aircraft-based XCO2, except that the former is biased by −0.68 ppm (−0.99 ppm) with a standard deviation of 2.56 ppm (2.51 ppm), whereas the averages of the differences between the GOSAT XCO2 over ocean and the aircraft-based XCO2 were −1.82 ppm (−2.27 ppm) with a standard deviation of 1.04 ppm (1.79 ppm) for ±2° (±5°) boxes.

  • gosat higher level product status 1 5 year after the launch
    Remote Sensing, 2010
    Co-Authors: Hiroshi Watanabe, Akira Yuki, Kenji Hayashi, Fumie Kawazoe, N Kikuchi, Fumiho Takahashi, Tsuneo Matsunaga, Tatsuya Yokota
    Abstract:

    After the 1.5 year operation of GOSAT (Greenhouse gases Observing SATellite), NIES GOSAT DHF (GOSAT Data Handling Facility of National Institute for Environmental Studies) has been producing CAI Level 1B and 1B+, FTS/CAI Level 2, and FTS/CAI Level 3 products, receiving FTS Level 1A/1B and CAI Level 1A data from JAXA (FTS: Fourier Transform Spectrometer; CAI: Cloud and Aerosol Imager; JAXA: Japan Aerospace Exploration Agency). In addition to the higher level data processing, GOSAT DHF has the following additional roles: 1) Data archive and distribution, and 2) Observational request collection from users and their submission to JAXA. After calibration and preliminary validation, the processed data are distributed to RA users at the first stage (RA users: researchers engaging in Research Announcement). The processed data were validated and then they were distributed to General Users (GU) in February 2010. All the distribution is carried out through the GOSAT User Interface Gateway (GUIG). As of August 2010, the total number of user registration exceeds 900, and a large number of products are distributed both to the RA users and GU. In August 2010, improvement of the algorithm for FTS L2 SWIR products was conducted. The validation for this FTS L2 product of this new version still indicates that the FTS SWIR L2 CO 2 and CH 4 data still show slightly lower values than reference data used for validation. FTS TIR data processing is on the way.

Liang Feng - One of the best experts on this subject based on the ideXlab platform.

  • estimates of european uptake of co2 inferred from gosat xco2 retrievals sensitivity to measurement bias inside and outside europe
    Atmospheric Chemistry and Physics, 2016
    Co-Authors: Liang Feng, Nicholas M Deutscher, Dietrich G Feist, Isamu Morino, Robert Parker, Paul I Palmer, Rigel Kivi, Ralf Sussmann
    Abstract:

    Abstract. Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.

  • does gosat capture the true seasonal cycle of carbon dioxide
    Atmospheric Chemistry and Physics, 2015
    Co-Authors: Nicholas M Deutscher, Frederic Chevallier, Hartmut Boesch, Hannakaisa Lindqvist, Christopher W Odell, Sourish Basu, Liang Feng
    Abstract:

    Abstract. The seasonal cycle accounts for a dominant mode of total column CO2 (XCO2) annual variability and is connected to CO2 uptake and release; it thus represents an important quantity to test the accuracy of the measurements from space. We quantitatively evaluate the XCO2 seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO2 Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO2, as well as the regional growth rates in XCO2 through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO2 between ACOS and TCCON, but ACOS generally infers a date of maximum XCO2 2–3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO2. In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO2 depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO2 doubles from western USA to East Asia at 45–50° N, which is only partially shown by the models. In general, we find that model-to-model differences can be larger than GOSAT-to-model differences. These results suggest that GOSAT/ACOS retrievals of the XCO2 seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.

  • atmospheric carbon dioxide retrieved from the greenhouse gases observing satellite gosat comparison with ground based tccon observations and geos chem model calculations
    Journal of Geophysical Research, 2012
    Co-Authors: Austin Cogan, Nicholas M Deutscher, Hartmut Boesch, Liang Feng, Robert Parker, Paul I Palmer, Jf Blavier, Ronald Macatangay, Justus Notholt, Coleen M Roehl
    Abstract:

    We retrieved column-averaged dry air mole fractions of atmospheric carbon dioxide (X_CO_2) from backscattered short-wave infrared (SWIR) sunlight measured by the Japanese Greenhouse gases Observing SATellite (GOSAT). Over two years of X_CO_2 retrieved from GOSAT is compared with X_CO_2 inferred from collocated SWIR measurements by seven ground-based Total Carbon Column Observing Network (TCCON) stations. The average difference between GOSAT and TCCON X_CO_2 for individual TCCON sites ranges from −0.87 ppm to 0.77 ppm with a mean value of 0.1 ppm and standard deviation of 0.56 ppm. We find an average bias between all GOSAT and TCCON X_CO_2 retrievals of −0.20 ppm with a standard deviation of 2.26 ppm and a correlation coefficient of 0.75. One year of XCO2 was retrieved from GOSAT globally, which was compared to global 3-D GEOS-Chem chemistry transport model calculations. We find that the latitudinal gradient, seasonal cycles, and spatial variability of GOSAT and GEOS-Chem agree well in general with a correlation coefficient of 0.61. Regional differences between GEOS-Chem model calculations and GOSAT observations are typically less than 1 ppm except for the Sahara and central Asia where a mean difference between 2 to 3 ppm is observed, indicating regional biases in the GOSAT X_CO_2 retrievals unobserved by the current TCCON network. Using a bias correction scheme based on linear regression these regional biases are significantly reduced, approaching the required accuracy for surface flux inversions.

  • methane observations from the greenhouse gases observing satellite comparison to ground based tccon data and model calculations
    Geophysical Research Letters, 2011
    Co-Authors: Robert Parker, Nicholas M Deutscher, Hartmut Boesch, Liang Feng, Austin Cogan, Paul I Palmer, Annemarie Fraser, J Messerschmidt, David W T Griffith
    Abstract:

    We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (X_(CH4)) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT X_(CH4) retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the X_(CH4) retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT X_(CH4) retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed X_(CH4) are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis.