On the potential of a neural network-based approach for estimating XCO2 from OCO-2 measurements

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 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 days, respectively. The information about the date mainly comes from the weak CO2 band, that 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 behaviour 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 are significantly 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.

On the Theory of the Divergence Method for Quantifying Source Emissions From Satellite Observations

“…For example, the integrated mass enhancement (IME) method relates the total integrated plume mass estimated from a satellite image directly to the source emission rate, after calibrating the relation between IME and effective wind for simulated cases in a range of meteorological settings (Varon et al., 2018). A machine learning approach can be similarly used to obtain such a direct inversion (e.g., Bréon et al., 2021; Dumont Le Brazidec et al., 2023).…”