Abstract
Abstract. The Orbiting Carbon Observatory (OCO-2) instrument measures high-resolution spectra of the sun's radiance reflected at the earth's surface or scattered in the atmosphere. These spectra are used to estimate the column-averaged dry air mole fraction of CO2 (XCO2) and the surface pressure. The official retrieval algorithm (NASA's Atmospheric CO2 Observations from Space retrievals, ACOS) is a full-physics algorithm and has been extensively evaluated. Here we propose an alternative approach based on an artificial neural network (NN) technique. For training and evaluation, we use as reference estimates (i) the surface pressures from a numerical weather model and (ii) the XCO2 derived from an atmospheric transport simulation constrained by surface air-sample measurements of CO2. The NN is trained here using real measurements acquired in nadir mode on cloud-free scenes during even-numbered months and is then evaluated against similar observations during odd-numbered months. The evaluation indicates that the NN retrieves the surface pressure with a root-mean-square error better than 3 hPa and XCO2 with a 1σ precision of 0.8 ppm. The statistics indicate that the NN trained with a representative set of data allows excellent accuracy that is slightly better than that of the full-physics algorithm. An evaluation against reference spectrophotometer XCO2 retrievals indicates similar accuracy for the NN and ACOS estimates, with a skill that varies among the various stations. The NN–model differences show spatiotemporal structures that indicate a potential for improving our knowledge of CO2 fluxes. We finally discuss the pros and cons of using this NN approach for the processing of the data from OCO-2 or other space missions.
Highlights
During the past decades, natural fluxes have absorbed about half of the anthropogenic emissions of CO2 (Knorr, 2009), but there is large uncertainty about the spatial distribution of this sink over time and on the processes that control it
One may argue that the neural network (NN) has learned from the model and generates an estimate that is not based on the spectra but rather on some prior information
This is a strong indication that the information is derived from the spectra as the NN does not “know” the CAMS value that corresponds to the observation location
Summary
Natural fluxes have absorbed about half of the anthropogenic emissions of CO2 (Knorr, 2009), but there is large uncertainty about the spatial distribution of this sink over time and on the processes that control it. Attempts to complement this network with satellite retrievals from sensors that were not designed for this purpose were not successful (Chevallier et al, 2005), but a series of dedicated instruments were put into orbit when the Greenhouse Gases Observing Satellite (GOSAT, Yokota et al, 2009) and the second Orbiting Carbon Observatory (OCO-2 Eldering et al, 2017) were launched in 2009 and 2014, respectively These were still operational at the time of writing. A NN-based technique was already used by Chédin et al (2003) for a fast retrieval of midtropospheric mean CO2 concentrations from some meteorological satellite radiometers These authors trained their NNs on a large ensemble of radiance simulations made using a reference radiation model and assuming diverse atmospheric and surface conditions. The retrievals from the NN approach are evaluated against model estimates of surface pressure and XCO2, as well as observations from the Total Carbon Column Observing Network (TCCON, Wunch et al, 2011a).
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