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Machine learning-based aerosol characterization using OCO-2 O₂ A-band observations

Chen, Sihe and Natraj, Vijay and Zeng, Zhao-Cheng and Yung, Yuk L. (2022) Machine learning-based aerosol characterization using OCO-2 O₂ A-band observations. Journal of Quantitative Spectroscopy and Radiative Transfer, 279 . Art. No. 108049. ISSN 0022-4073. doi:10.1016/j.jqsrt.2021.108049. https://resolver.caltech.edu/CaltechAUTHORS:20220121-968317000

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Abstract

Aerosol scattering influences the retrieval of the column-averaged dry-air mole fraction of CO₂ (XCO₂) from the Orbiting Carbon Observatory-2 (OCO-2). This is especially true for surfaces with reflectance close to a critical value where there is very low sensitivity to aerosol loading. A spectral sorting approach was introduced to improve the characterization of aerosols over coastal regions. Here, we generalize this procedure to land surfaces and use a two-step neural network to retrieve aerosol parameters from OCO-2 measurements. We show that, by using a combination of radiance measurements in the continuum and inside the absorption band, both the aerosol optical depth and layer height, as well as their uncertainties, can be accurately predicted. Using the improved aerosol estimates as a priori, we demonstrate that the accuracy of the XCO₂ retrieval can be significantly improved compared to the OCO-2 Level-2 Standard product. Furthermore, using simulated observations, we obtain estimates of the error in the retrieved XCO₂. These simulations indicate that the bias-corrected OCO-2 Lite data, which is used for flux inversions, may have remaining biases due to interference of aerosol effects.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.jqsrt.2021.108049DOIArticle
ORCID:
AuthorORCID
Natraj, Vijay0000-0003-3154-9429
Zeng, Zhao-Cheng0000-0002-0008-6508
Yung, Yuk L.0000-0002-4263-2562
Alternate Title:Machine learning-based aerosol characterization using OCO-2 O2 A-band observations
Additional Information:© 2022 Published by Elsevier Ltd. Received 27 August 2021, Revised 18 November 2021, Accepted 21 December 2021, Available online 26 December 2021. A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). VN acknowledges support from the NASA Earth Science US Participating Investigator program (solicitation NNH16ZDA001N-ESUSPI). We thank D. Crisp, C. E. Miller, M. Gunson and S. Sander for stimulating discussions. Author Contributions. S. C. devised the machine learning method, conducted the data curation, performed the analysis, and wrote the manuscript. V. N. conceived the project, supervised the work, and revised the manuscript. Z. Z. developed the spectral sorting method, revised the manuscript, and provided suggestions on visualization. Y. Y. co-supervised the work and revised the manuscript. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funders:
Funding AgencyGrant Number
NASA/JPL/Caltech80NM0018D0004
NASANNH16ZDA001N-ESUSPI
Subject Keywords:OCO-2; Aerosol; Critical albedo; CALIPSO; Machine learning; O2-A Band
DOI:10.1016/j.jqsrt.2021.108049
Record Number:CaltechAUTHORS:20220121-968317000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220121-968317000
Official Citation:Sihe Chen, Vijay Natraj, Zhao-Cheng Zeng, Yuk L. Yung, Machine learning-based aerosol characterization using OCO-2 O2 A-band observations, Journal of Quantitative Spectroscopy and Radiative Transfer, Volume 279, 2022, 108049, ISSN 0022-4073, https://doi.org/10.1016/j.jqsrt.2021.108049.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113069
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:22 Jan 2022 00:14
Last Modified:22 Jan 2022 00:14

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