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CH4 retrieval from hyperspectral satellite measurements in short-wave infrared: sensitivity study and preliminary test with GOSAT data

  • Article
  • Atmospheric Science
  • Published:
Chinese Science Bulletin

Abstract

One of the important science requirements of monitoring atmospheric methane abundances from hyperspectral measurements is to establish a highly accurate retrieval algorithm. This paper aimed to describe a retrieval algorithm based on a series of sensitivity study with an effective and accurate forward model, which was applied to realize online calculation of absorption coefficient and backscattered solar radiance. The sensitivity study consisted of band selection, interference factors analysis, and retrieval model construction. The band selection analysis indicated that the 1.65 μm band (5,900–6,150 cm−1) associated with the 2.06 μm band (4,800–4,900 cm−1) retained more than 90 % of the information content of CH4, CO2, and temperature, and more than 85 % of that of H2O in the retrieval. Investigation of the interference factors showed that H2O, temperature, and CO2 will cause unacceptable errors if they are not revised. This also showed that revising temperature and H2O with a profile model is more efficient than with a temperature offset and a H2O scale factor model. A preliminarily test using the Greenhouse gases Observing SATellite Level 1B spectral data indicated that most of the retrieval errors were less than 1 %, which is acceptable for methane flux estimation.

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References

  1. Etheridge DM, Steele RJ, Langenfelds RL (1998) Atmospheric methane between 1000 A.D. and present: evidence of anthropogenic emissions and climatic variability. J Geophys Res 103:15979–15993

    Google Scholar 

  2. Blake DR, Rowland FS (1988) Continuing worldwide in tropospheric methane, 1978 to 1987. Science 239:1129–1131

    Article  Google Scholar 

  3. Dlugokencky EJ, Houweling S, Bruhwiler L et al (2003) Atmospheric methane levels off: temporary pause or a new steady-state? Geophys Res Lett. doi:10.1029/2003GL018126

    Google Scholar 

  4. Forster P, Ramaswamy V, Artaxo P et al (2007) Changes in atmospheric constituents and in radiative forcing. Cambridge University Press, Cambridge

    Google Scholar 

  5. Baker DF, Law RM, Gurney KR et al (2006) TransCom 3 inversion intercomparision: impact of transport model errors on the interannual variability of regional CO2 fluxes, 1988–2003. Glob Biogeochem Cycles 20:GB2012

    Article  Google Scholar 

  6. Meirink JF, Eskes HJ, Goede APH (2006) Sensitivity analysis of methane emission derived from SCIAMACHY observations through inverse modelling. Atmos Chem Phys 6:1275–1292

    Article  Google Scholar 

  7. Xiong X, Barnet C, Maddy E et al (2008) Characterization and validation of methane products from the Atmospheric Infrared Sounder (AIRS). J Geophys Res 113:G00A01

    Google Scholar 

  8. Frankenber C, Aben I, Bergamaschi P et al (2011) Global column-averaged methane mixing ratios from 2003 to 2009 as derived from SCIMACHY observations: trends and variability. J Geophys Res 116:D04302

    Google Scholar 

  9. Morino I, Uchino O, Inoue M et al (2011) Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra. Atmos Meas Tech 4:1061–1076

    Article  Google Scholar 

  10. Liu Y, Lv D, Chen H et al (2011) Advances in technologies and methods for satelliteremote sensing of atmospheric CO2. Remote Sens Technol Appl 26:247–254

    Google Scholar 

  11. Buchwitz M, Reuter M, Bovensmann H et al (2013) Carbon monitoring satellite (CarbonSat): assessment of scattering related atmospheric CO2 and CH4 retrieval errors and first results on implications for inferring city CO2 emissions. Atmos Meas Tech 6:4769–4850

    Article  Google Scholar 

  12. Liu Y, Duan M, Cai Z et al (2012) Chinese carbon dioxide satellite (TanSat) status and plans. In: American Geophysical Union 2012 fall meeting

  13. Frankenberg C, Meirink JF, Weele M et al (2005) Assessing methane emission from global space-borne observations. Science 308:1010–1014

    Article  Google Scholar 

  14. Parker R, Boesch H, Cogan A et al (2011) Methane observations from the greenhouse gases observing SATellite: comparison to ground-based TCCON data and model calculations. Geophys Res Lett 38:L15807

    Article  Google Scholar 

  15. Butz A, Hasekamp OP, Frankenber C et al (2010) CH4 retrievals from space-based solar backscatter measurements: performance evaluation against simulated aerosol and cirrus loaded scenes. J Geophys Res 115:D24302

    Article  Google Scholar 

  16. O’Dell CW, Connor B, Boesch H et al (2012) The ACOS CO2 retrieval algorithm-part 1: description and validation against synthetic observations. Atmos Meas Tech 5:99–121

    Article  Google Scholar 

  17. Yi L, Yang DX, Cai ZN (2013) A retrieval algorithm for TanSat XCO2 observation: retrieval experiments using GOSAT data. Chin Sci Bull 58:1520–1523

    Google Scholar 

  18. Boesch H, Baker D, Connor B et al (2011) Global characterization of CO2 column retrievals from shortwave-infrared satellite observations of the orbiting carbon observatory-2 mission. Remote Sens 3:270–304

    Article  Google Scholar 

  19. Butz A, Guerlet S, Hasekamp O et al (2011) Toward accurate CO2 and CH4 observation from GOSAT. Geophys Res Lett 38:L14812

    Article  Google Scholar 

  20. Yoshida Y, Kikuchi N, Morino I et al (2013) Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data. Atmos Meas Tech 6:1533–1547

    Article  Google Scholar 

  21. Rodgers CD (2000) Inverse methods for atmospheric sounding: theory and practice. World Scientific, Singapore, pp 81–100

    Book  Google Scholar 

  22. Levenberg KA (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 11:164–168

    Google Scholar 

  23. Marquardt DW (1963) An algorithm for least squares estimation of non-linear parameters. J Soc Ind Appl Math 11:431–441

    Article  Google Scholar 

  24. Cai Z, Liu Y, Yang D (2013) Analysis of XCO2 retrieval sensitivity using simulated Chinese carbon satellite (TanSat) measurements. Sci China Earth Sci (in press)

  25. Spurr JDR, Kurosu PT, Chance VK (2001) A linearized discrete ordinate radiative transfer model for atmospheric remote sensing retrieval. J Quant Spectrosc Radiat Transf 68:689–735

    Article  Google Scholar 

  26. Clough SA, Shephard MW, Mlawer EJ et al (2005) Atmospheric radiative transfer modeling: a summary of the AER codes. J Quant Spectrosc Radiat Transf 91:233–244

    Article  Google Scholar 

  27. Sofieva VF, Kyrola E (2003) Information approach to optimal selection of spectral channels. J Geophys Res 108:4513

    Article  Google Scholar 

  28. Crevoisier C, Chedin A, Scott NA (2003) AIRS channel selection for CO2 and other trace-gas retrievals. Q J R Meteorol Soc 129:2719–2740

    Article  Google Scholar 

  29. Yang D, Yi L, Cai Z (2013) Simulation study of aerosol optical properties to top of atmospheric reflected sunlight in the near infrared CO2 weak band. Atmos Ocean Sci Lett 6:60–64

    Google Scholar 

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Acknowledgments

The authors thank TCCON for providing the ground-based observations and the a priori data, the GOSAT team for providing GOSAT observational data, and the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing the meteorological data used to simulate retrieval studies. We also greatly appreciate the Atmospheric and Environmental Research (AER) for providing the LBLRTM model, the Harvard-Smithsonian Center for Astrophysics for providing the HITRAN 2008 database, and RT solutions for providing the VLIDORT model. The authors thank Dr. XiaozhenXiong for his comments and suggestions that resulted in an improved manuscript. This work was supported by the Strategic Priority Research Program—Climate Change: Carbon Budget and Relevant Issues (XDA05040200) and the National High-Tech R&D Program of China (2011AA12A104).

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Authors and Affiliations

  1. Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Jianbo Deng, Yi Liu, Dongxu Yang & Zhaonan Cai

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Jianbo Deng

Authors
  1. Jianbo Deng
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  2. Yi Liu
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  3. Dongxu Yang
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  4. Zhaonan Cai
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Corresponding author

Correspondence to Dongxu Yang.

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SPECIAL TOPIC: Greenhouse Gas Observation From Space: Theory and Application

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Deng, J., Liu, Y., Yang, D. et al. CH4 retrieval from hyperspectral satellite measurements in short-wave infrared: sensitivity study and preliminary test with GOSAT data. Chin. Sci. Bull. 59, 1499–1507 (2014). https://doi.org/10.1007/s11434-014-0245-2

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  • DOI: https://doi.org/10.1007/s11434-014-0245-2

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