On the potential of a neural-network-based approach for estimating XCO2 from OCO-2 measurements
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Published:2022-09-15
Issue:18
Volume:15
Page:5219-5234
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Bréon François-MarieORCID, David Leslie, Chatelanaz Pierre, Chevallier FrédéricORCID
Abstract
Abstract. In David et al. (2021), we introduced a neural network (NN) approach for estimating the column-averaged dry-air mole fraction of CO2 (XCO2) and the surface pressure from the reflected solar spectra
acquired by the OCO-2 instrument. The results indicated great potential for
the technique as the comparison against both model estimates and independent TCCON measurements showed an accuracy and precision similar to or better than that of the operational ACOS (NASA's Atmospheric CO2 Observations from Space retrievals – ACOS) algorithm. Yet, subsequent analysis showed that the neural network estimate often mimics the training dataset and is unable to retrieve small-scale features such as CO2 plumes from industrial sites. Importantly, we found that, with the same inputs as those used to estimate XCO2 and surface pressure, the NN technique is able to estimate latitude and date with unexpected skill, i.e., with an error whose standard deviation is only 7∘ and 61 d, respectively. The information about the date mainly comes from the weak CO2 band, which is influenced
by the well-mixed and increasing concentrations of CO2 in the
stratosphere. The availability of such information in the measured spectrum
may therefore allow the NN to exploit it rather than the direct CO2
imprint in the spectrum to estimate XCO2. Thus, our first version of the NN
performed well mostly because the XCO2 fields used for the training were
remarkably accurate, but it did not bring any added value. Further to this analysis, we designed a second version of the NN, excluding
the weak CO2 band from the input. This new version has a different
behavior as it does retrieve XCO2 enhancements downwind of emission hotspots, i.e., a feature that is not in the training dataset. The comparison against the reference Total Carbon Column Observing Network (TCCON) and the surface-air-sample-driven inversion of the Copernicus Atmosphere Monitoring Service (CAMS) remains very good, as in the first version of the NN. In addition, the difference with the CAMS model (also called innovation in a data assimilation context) for NASA Atmospheric CO2 Observations from Space
(ACOS) and the NN estimates is correlated. These results confirm the potential of the NN approach for an operational
processing of satellite observations aiming at the monitoring of CO2
concentrations and fluxes. The true information content of the neural
network product remains to be properly evaluated, in particular regarding
the respective input of the measured spectrum and the training dataset.
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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