Preprints
https://doi.org/10.5194/amt-2021-84
https://doi.org/10.5194/amt-2021-84

  19 Mar 2021

19 Mar 2021

Review status: this preprint is currently under review for the journal AMT.

GFIT3: A full physics retrieval algorithm for remote sensing of greenhouse gases in the presence of aerosols

Zhao-Cheng Zeng1,2, Vijay Natraj3, Feng Xu4, Sihe Chen2, Fang-Ying Gong2, Thomas J. Pongetti3, Keeyoon Sung3, Geoffrey Toon3, Stanley P. Sander3, and Yuk L. Yung2,3 Zhao-Cheng Zeng et al.
  • 1Joint Institute for Regional Earth System Science & Engineering (JIFRESSE), University of California, Los Angeles, USA
  • 2Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, USA
  • 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA
  • 4School of Meteorology, The University of Oklahoma, Oklahoma, USA

Abstract. Remote sensing of greenhouse gases (GHGs) in cities, where high GHG emissions are typically associated with heavy aerosol loading, is challenging due to retrieval uncertainties caused by imperfect characterization of scattering by aerosols. We investigate this problem by developing GFIT3, a full physics algorithm to retrieve GHGs (CO2 and CH4) by accounting for aerosol scattering effects in polluted urban atmospheres. In particular, the algorithm includes coarse (including sea salt and dust) and fine (including organic carbon, black carbon, and sulfate) mode aerosols in the radiative transfer model. The performance of GFIT3 is assessed using high spectral resolution observations over the Los Angeles (LA) megacity made by the California Laboratory for Atmospheric Remote Sensing–Fourier Transform Spectrometer (CLARS–FTS). CLARS–FTS is located on Mt. Wilson, California, at 1.67 km a.s.l. overlooking the LA basin, and makes observations of reflected sunlight in the near-infrared spectral range. The first set of evaluations are performed by conducting retrieval experiments using synthetic spectra. We find that errors in the retrievals of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) due to uncertainties in the aerosol optical properties and atmospheric a priori profiles are less than 1 % on average. This indicates that atmospheric scattering does not induce a large bias in the retrievals when the aerosols are properly characterised. The methodology is then further evaluated by comparing GHG retrievals using GFIT3 with those obtained from the CLARS-GFIT algorithm (used for currently operational CLARS retrievals) that does not account for aerosol scattering. We find a significant correlation between retrieval bias and aerosol optical depth (AOD). Comparison of GFIT3 AOD retrievals with collocated ground-based observations from AERONET shows that the developed algorithm produces very accurate results, with biases in AOD estimates of about 0.02. Finally, we assess the uncertainty in the widely used tracer-tracer ratio method to obtain CH4 emissions based on CO2 emissions, and find that using the CH4 / CO2 ratio effectively cancels out biases due to aerosol scattering. Overall, this study of applying GFIT3 to CLARS-FTS observations improves our understanding of the impact of aerosol scattering on the remote sensing of GHGs in polluted urban atmospheric environments. GHG retrievals from CLARS-FTS are potentially complementary to existing ground-based and space-borne observations to monitor anthropogenic GHG fluxes in megacities.

Zhao-Cheng Zeng et al.

Status: open (until 31 May 2021)

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Zhao-Cheng Zeng et al.

Zhao-Cheng Zeng et al.

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Short summary
Large carbon source regions such as megacities are also typically associated with heavy aerosol loading, which introduces uncertainties in the retrieval of greenhouse gases from reflected and scattered sunlight measurements. In this study, we developed a full physics algorithm to retrieve greenhouse gases in the presence of aerosols and demonstrated its performance by retrieving CO2 and CH4 columns from remote sensing measurements in the Los Angeles megacity.