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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 9, 3491-3512, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
01 Aug 2016
Bias corrections of GOSAT SWIR XCO2 and XCH4 with TCCON data and their evaluation using aircraft measurement data
Makoto Inoue1,a, Isamu Morino1, Osamu Uchino1, Takahiro Nakatsuru1, Yukio Yoshida1, Tatsuya Yokota1, Debra Wunch2,b, Paul O. Wennberg2, Coleen M. Roehl2, David W. T. Griffith3, Voltaire A. Velazco3, Nicholas M. Deutscher3,4, Thorsten Warneke4, Justus Notholt4, John Robinson5, Vanessa Sherlock5,c, Frank Hase6, Thomas Blumenstock6, Markus Rettinger7, Ralf Sussmann7, Esko Kyrö8, Rigel Kivi8, Kei Shiomi9, Shuji Kawakami9, Martine De Mazière10, Sabrina G. Arnold11, Dietrich G. Feist11, Erica A. Barrow12, James Barney12, Manvendra Dubey13, Matthias Schneider6, Laura T. Iraci14, James R. Podolske14, Patrick W. Hillyard14,15, Toshinobu Machida1, Yousuke Sawa16, Kazuhiro Tsuboi16, Hidekazu Matsueda16, Colm Sweeney17, Pieter P. Tans17, Arlyn E. Andrews17, Sebastien C. Biraud18, Yukio Fukuyama19, Jasna V. Pittman20, Eric A. Kort21,2,d, and Tomoaki Tanaka1,9,e 1National Institute for Environmental Studies (NIES), Tsukuba, Japan
2California Institute for Technology, Pasadena, CA, USA
3Centre for Atmospheric Chemistry, University of Wollongong, New South Wales, Australia
4Institute of Environmental Physics, University of Bremen, Bremen, Germany
5National Institute of Water and Atmospheric Research, Lauder, New Zealand
6IMK-ASF, Karlsruhe Institute of Technology, Karlsruhe, Germany
7IMK-IFU, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
8Arctic Research Centre, Finnish Meteorological Institute (FMI), Sodankylä, Finland
9Japan Aerospace Exploration Agency (JAXA), Tsukuba, Japan
10Belgian Institute for Space Aeronomy (IASB-BIRA), Brussels, Belgium
11Max Planck Institute for Biogeochemistry (MPI-BGC), Jena, Germany
12Ivy Tech Community College of Indiana, Indianapolis, IN, USA
13Los Alamos National Laboratory, Los Alamos, NM, USA
14NASA Ames Research Center, Moffett Field, CA, USA
15Bay Area Environmental Research Institute, Petaluma, CA, USA
16Meteorological Research Institute (MRI), Tsukuba, Japan
17National Oceanic and Atmospheric Administration (NOAA), Boulder, CO, USA
18Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, USA
19Japan Meteorological Agency, Tokyo, Japan
20Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
21Jet Propulsion Laboratory, Pasadena, CA, USA
anow at: Department of Biological Environment, Akita Prefectural University, Akita, Japan
bnow at: Department of Physics, University of Toronto, Toronto, Canada
cnow at: Laboratoire de Météorologie Dynamique, Palaiseau, France
dnow at: Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA
enow at: NASA Ames Research Center, Moffett Field, CA, USA
Abstract. We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) 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 XCO2 and XCH4 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 XCO2/XCH4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

Citation: Inoue, M., Morino, I., Uchino, O., Nakatsuru, T., Yoshida, Y., Yokota, T., Wunch, D., Wennberg, P. O., Roehl, C. M., Griffith, D. W. T., Velazco, V. A., Deutscher, N. M., Warneke, T., Notholt, J., Robinson, J., Sherlock, V., Hase, F., Blumenstock, T., Rettinger, M., Sussmann, R., Kyrö, E., Kivi, R., Shiomi, K., Kawakami, S., De Mazière, M., Arnold, S. G., Feist, D. G., Barrow, E. A., Barney, J., Dubey, M., Schneider, M., Iraci, L. T., Podolske, J. R., Hillyard, P. W., Machida, T., Sawa, Y., Tsuboi, K., Matsueda, H., Sweeney, C., Tans, P. P., Andrews, A. E., Biraud, S. C., Fukuyama, Y., Pittman, J. V., Kort, E. A., and Tanaka, T.: Bias corrections of GOSAT SWIR XCO2 and XCH4 with TCCON data and their evaluation using aircraft measurement data, Atmos. Meas. Tech., 9, 3491-3512,, 2016.
Publications Copernicus
Short summary
In this study, we correct the biases of GOSAT XCO2 and XCH4 using TCCON data. To evaluate the effectiveness of our correction method, uncorrected/corrected GOSAT data are compared to independent XCO2 and XCH4 data derived from aircraft measurements. Consequently, we suggest that this method is effective for reducing the biases of the GOSAT data. We consider that our work provides GOSAT data users with valuable information and contributes to the further development of studies on greenhouse gases.
In this study, we correct the biases of GOSAT XCO2 and XCH4 using TCCON data. To evaluate the...