skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Consistent satellite XCO 2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm

Abstract

Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO 2) 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 XCO 2, the column-averaged dry-air mole fraction of CO 2. BESD has been initially developed for SCIAMACHY XCO 2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO 2 product. GOSAT BESD XCO 2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO 2 from GOSAT and present detailed comparisons with ground-based observations of XCO 2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparisonmore » results between all three XCO 2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO 2. 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° × 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 XCO 2 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 using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.« less

Authors:
 [1];  [1];  [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2];  [2];  [3];  [4];  [5];  [6];  [2];  [7];  [8];  [1];  [9];  [6];  [10] more »;  [11];  [5];  [1];  [9] « less
  1. University of Bremen, Bremen (Germany)
  2. Japan Aerospace Exploration Agency (JAXA), Tsukuba (Japan)
  3. University of Bremen, Bremen (Germany); Univ. of Wollongong, Wollongong (Australia)
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  5. University of Wollongong, Wollongong(Australia)
  6. IMK-ASF, Karlsruhe Institute of Technology (KIT), Karlsruhe (Germany)
  7. Finnish Meteorological Institute, Sodankylä (Finland)
  8. National Institute for Environmental Studies (NIES), Tsukuba (Japan)
  9. California Institute of Technology, Pasadena, CA (United States)
  10. National Institute of Water and Atmospheric Research, Wellington (New Zealand); Lab. de Meteorologie Dynamique, Palaiseau (France)
  11. IMK-IFU, Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen (Germany)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1208894
Grant/Contract Number:
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Atmospheric Measurement Techniques Discussions (Online)
Additional Journal Information:
Journal Name: Atmospheric Measurement Techniques Discussions (Online); Journal Volume: 8; Journal Issue: 2; Journal ID: ISSN 1867-8610
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Heymann, J., Reuter, M., Hilker, M., Buchwitz, M., Schneising, O., Bovensmann, H., Burrows, J. P., Kuze, A., Suto, H., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kawakami, S., Kivi, R., Morino, I., Petri, C., Roehl, C., Schneider, M., Sherlock, V., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D. Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm. United States: N. p., 2015. Web. doi:10.5194/amtd-8-1787-2015.
Heymann, J., Reuter, M., Hilker, M., Buchwitz, M., Schneising, O., Bovensmann, H., Burrows, J. P., Kuze, A., Suto, H., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kawakami, S., Kivi, R., Morino, I., Petri, C., Roehl, C., Schneider, M., Sherlock, V., Sussmann, R., Velazco, V. A., Warneke, T., & Wunch, D. Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm. United States. doi:10.5194/amtd-8-1787-2015.
Heymann, J., Reuter, M., Hilker, M., Buchwitz, M., Schneising, O., Bovensmann, H., Burrows, J. P., Kuze, A., Suto, H., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kawakami, S., Kivi, R., Morino, I., Petri, C., Roehl, C., Schneider, M., Sherlock, V., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D. Fri . "Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm". United States. doi:10.5194/amtd-8-1787-2015. https://www.osti.gov/servlets/purl/1208894.
@article{osti_1208894,
title = {Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm},
author = {Heymann, J. and Reuter, M. and Hilker, M. and Buchwitz, M. and Schneising, O. and Bovensmann, H. and Burrows, J. P. and Kuze, A. and Suto, H. and Deutscher, N. M. and Dubey, M. K. and Griffith, D. W. T. and Hase, F. and Kawakami, S. and Kivi, R. and Morino, I. and Petri, C. and Roehl, C. and Schneider, M. and Sherlock, V. and Sussmann, R. and Velazco, V. A. and Warneke, T. and Wunch, D.},
abstractNote = {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 (IFS). 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 the SCIAMACHY and the 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° × 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 using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.},
doi = {10.5194/amtd-8-1787-2015},
journal = {Atmospheric Measurement Techniques Discussions (Online)},
number = 2,
volume = 8,
place = {United States},
year = {Fri Feb 13 00:00:00 EST 2015},
month = {Fri Feb 13 00:00:00 EST 2015}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share:
  • We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO 2 (XCO 2) and CH 4 (XCH 4) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO 2more » and XCH 4 data derived from aircraft measurements taken for 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 Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO 2/XCH 4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.« less
  • China’s twelfth Five-Year Plan included pollution control measures with a goal of reducing national emissions of nitrogen oxides (NO x) by 10% by 2015 compared with 2010. Multiple linear regression analysis was used on 11-year time series of all nitrogen dioxide (NO 2) pixels from the Ozone Monitoring Instrument (OMI) over 18 NO 2 hotspots in China. The regression analysis accounted for variations in meteorology, pixel resolution, seasonal effects, weekday variability and year-to-year variability. The NO 2 trends suggested that there was an increase in NO 2 columns in most areas from 2005 to around 2011 which was followed bymore » a strong decrease continuing through 2015. The satellite results were in good agreement with the annual official NO x emission inventories which were available up until 2014. We show the value of evaluating trends in emission inventories using satellite retrievals. It further shows that recent control strategies were effective in reducing emissions and that recent economic transformations in China may be having an effect on NO 2 columns. The satellite information for 2015 suggests that emissions have continued to decrease since the latest inventories available and have surpassed the goals of the twelfth Five-Year Plan.« less
  • The substances CO{sub 2} and C{sub 2}H{sub 6} are key components of natural gas. Measurements of the molar heat capacity at constant volume (C{sub v}) for [xCO{sub 2} + (1 {minus} x)C{sub 2}H{sub 6}], x = 0.25, 0.49, 0.74, were conducted. Temperatures ranged from about 220 to 340 K, and pressures were as high as 35 MPa. Measurements were conducted on samples in compressed gas and liquid states. The primary sources of uncertainty are the estimated temperature rise and the estimated quantity of substance in the calorimeter. Overall, the uncertainty ({+-}2{sigma}) of the C{sub v} values is estimated to bemore » less than {+-}2.0% for vapor and {+-}0.5% for liquid.« less
  • We present two new products from near-infrared Greenhouse Gases Observing Satellite (GOSAT) observations: lowermost tropospheric (LMT, from 0 to 2.5 km) and upper tropospheric–stratospheric ( U, above 2.5 km) carbon dioxide partial column mixing ratios. We compare these new products to aircraft profiles and remote surface flask measurements and find that the seasonal and year-to-year variations in the new partial column mixing ratios significantly improve upon the Atmospheric CO 2 Observations from Space (ACOS) and GOSAT (ACOS-GOSAT) initial guess and/or a priori, with distinct patterns in the LMT and U seasonal cycles that match validation data. For land monthly averages,more » we find errors of 1.9, 0.7, and 0.8 ppm for retrieved GOSAT LMT, U, and XCO 2; for ocean monthly averages, we find errors of 0.7, 0.5, and 0.5 ppm for retrieved GOSAT LMT, U, and XCO 2. In the southern hemispheric biomass burning season, the new partial columns show similar patterns to MODIS fire maps and MOPITT multispectral CO for both vertical levels, despite a flat ACOS-GOSAT prior, and a CO–CO 2 emission factor comparable to published values. The difference of LMT and U, useful for evaluation of model transport error, has also been validated with a monthly average error of 0.8 (1.4) ppm for ocean (land). LMT is more locally influenced than U, meaning that local fluxes can now be better separated from CO 2 transported from far away.« less
  • Bruker™ EM27/SUN instruments are commercial mobile solar-viewing near-IR spectrometers. They show promise for expanding the global density of atmospheric column measurements of greenhouse gases and are being marketed for such applications. They have been shown to measure the same variations of atmospheric gases within a day as the high-resolution spectrometers of the Total Carbon Column Observing Network (TCCON). However, there is little known about the long-term precision and uncertainty budgets of EM27/SUN measurements. In this study, which includes a comparison of 186 measurement days spanning 11 months, we note that atmospheric variations of X gas within a single day aremore » well captured by these low-resolution instruments, but over several months, the measurements drift noticeably. We present comparisons between EM27/SUN instruments and the TCCON using GGG as the retrieval algorithm. In addition, we perform several tests to evaluate the robustness of the performance and determine the largest sources of errors from these spectrometers. We include comparisons of X CO2, X CH4, X CO, and X N2O. Specifically we note EM27/SUN biases for January 2015 of 0.03, 0.75, –0.12, and 2.43 % for X CO2, X CH4, X CO, and X N2O respectively, with 1 σ running precisions of 0.08 and 0.06 % for X CO2 and X CH4 from measurements in Pasadena. We also identify significant error caused by nonlinear sensitivity when using an extended spectral range detector used to measure CO and N 2O.« less
    Cited by 4