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Satellites have been providing spaceborne observations of the total column of CO2 (noted xco2) for over two decades now and, with the need for independent verification of Paris Agreement objectives, many new satellite concepts are currently planned or being studied to complement or extend the already existing instruments. Depending on whether they are targeting natural and/or anthropogenic fluxes of CO2, the design of these future concepts vary greatly. The characteristics of their shortwave infrared (SWIR) observations notably explore several orders of magnitude in spectral resolution (from λ/Δλ~400 for Carbon Mapper to λ/Δλ~25000 for MicroCarb) and include different selections of spectral bands (from one to four bands, among which the CO2-sensitive 1.6 µm and/or 2.05 µm bands). Besides, the very nature of the spaceborne measurements is also explored: for instance, the NanoCarb imaging concept proposes to measure CO2-sensitive truncated interferograms, instead of infrared spectra as other concepts, in order to significantly reduce the instrument size. This study synthetically explores the impact of three different design parameters on xco2 retrieval performance, as obtained through Optimal Estimation: (1) the spectral resolution; (2) the signal-to-noise ratio (SNR) and (3) the spectral band selection. Similar performance assessments are completed for the exactly-defined MicroCarb, Copernicus CO2 Monitoring (CO2M) and NanoCarb concepts. We show that improving SNR is more efficient than improving spectral resolution to increase xco2 precision when perturbating these parameters across two orders of magnitude, and that low-SNR and/or low spectral resolution yield xco2 with vertical sensitivities giving more weight to atmospheric layers close to the surface. The exploration of various spectral band combinations illustrates, especially for lower spectral resolutions, how including an O2- sensitive band helps to increase optical path length information, and how the 2.05 µm CO2-sensitive band contains more geophysical information than the 1.6 µm band. With very different characteristics, MicroCarb shows a CO2 information content only slightly higher than CO2M, which translates into lower xco2 random errors, by a factor ranging from 1.1 to 1.9 depending on the observational situation. The NanoCarb performance for a single pixel of its imager compares to concepts that measure spectra at low-SNR and low-spectral resolution but, as this novel concept would observe a given target several times during a single overpass, its performance improves when combining all the observations. Overall, the broad range of results obtained through this synthetic xco2 performance mapping hints at the future intercomparison challenges that the wide variety of upcoming CO2-observing concepts will pose. [ABSTRACT FROM AUTHOR]

Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space CARBon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturized GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry-Perot imaging interferometer with a 2.3 x 2.3 km² spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO2 and CH4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms, and provide their systematic and random errors, column vertical sensitivities and degrees of freedom as a function of five scattering error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5 – 1 ppm and 4 – 6 ppb, respectively, compliant with their respective 1-ppm and 6-ppb precision objectives. Finally, these Level 2 performance results are parameterized as a function of the explored scattering error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO2 and CH4 flux estimation performance studies. [ABSTRACT FROM AUTHOR]

A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission, and uses an empirically corrected absorption continuum in the O2 A-band. For airmasses below 3.0, XCO2 retrievals successfully capture the latitudinal variations of CO2, as well as its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a difference of 1.33 ± 1.29 ppm, which is similar to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products. We show that the systematic differences between 5AI and ACOS results can be fully removed by adding an average calculated - observed spectral residual correction to OCO-2 measurements, thus underlying the critical sensitivity of retrieval results to forward modelling. These comparisons show the reliability of 5AI as a Bayesian optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry-air mole fractions of greenhouse gases. [ABSTRACT FROM AUTHOR]

A better understanding of greenhouse gas surface sources and sinks is required in order to adress the global challenge of climate change. Spaceborne remote estimations of their atmospheric concentration can offer the global coverage that is necessary to improve the constraints on those fluxes, thus enabling a better monitoring of anthropogenic emissions. Consequently, striving for this goal, several satellite missions have been designed during the past decade, and the TCCON ground-based network provides an essential validation resource for their estimations. Most of these results are obtained thanks to Bayesian optimal estimation, an inverse method that is well-know for its sensitivity to the a priori choice. This work proposes to quantify the influence of a priori CO2 concentration profile covariance choice in term of total column retrieval bias. Several a priori covariance matrices will be compared and used in a Bayesian optimal estimation inversion scheme based on 4A/OP radiative transfer model and GEISA spectroscopic database, evaluated thanks to calc-obs spectral residuals computed for hundreds of observations for which atmospheric situations are known. The matrices will be taken from regular TCCON a priori profile databases, CAMS analyses or derived from a profile climatology of profiles measured by balloon-borne AirCores. The performance of the retrieval procedure will then be assessed against atmospheric profiles of CO2 acquired at several stations as well as during the 2018 Monitoring of Atmospheric composition and Greenhouse gases through multi-Instruments Campaign (MAGIC). [ABSTRACT FROM AUTHOR]