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The Adaptable 4A Inversion (5AI): Description and first XCO2 retrievals from OCO-2 observations [Discussion paper]
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dc.contributor.authorDogniaux, Matthieues_ES
dc.contributor.authorCrevoisier, Cyriles_ES
dc.contributor.authorArmante, Raymondes_ES
dc.contributor.authorCapelle, Virginiees_ES
dc.contributor.authorDelahaye, Thibaultes_ES
dc.contributor.authorCassé, Vincentes_ES
dc.contributor.authorDe Mazière, Martinees_ES
dc.contributor.authorDeutscher, Nicholas Michaeles_ES
dc.contributor.authorFeist, Dietrich G.es_ES
dc.contributor.authorGarcía Rodríguez, Omaira Elenaes_ES
dc.contributor.authorGriffith, David W. T.es_ES
dc.contributor.authorHase, Frankes_ES
dc.contributor.authorIraci, Lauraes_ES
dc.contributor.authorKivi, Rigeles_ES
dc.contributor.authorMorino, Isamues_ES
dc.contributor.authorNotholt, Justuses_ES
dc.contributor.authorPollard, David F.es_ES
dc.contributor.authorRoehl, Coleen M.es_ES
dc.contributor.authorShiomi, Keies_ES
dc.contributor.authorStrong, Kimberlyes_ES
dc.contributor.authorTe, Yaoes_ES
dc.contributor.authorVelazco, Voltaire A.es_ES
dc.contributor.authorWarneke, Thorstenes_ES
dc.date.accessioned2020-11-13T09:39:43Z-
dc.date.available2020-11-13T09:39:43Z-
dc.date.issued2020-
dc.identifier.citationAtmospheric Measurement Techniques Discussions. 2020es_ES
dc.identifier.issn1867-8610-
dc.identifier.urihttp://hdl.handle.net/20.500.11765/12579-
dc.description.abstractA 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.es_ES
dc.description.sponsorshipThis work has received funding from CNES and CNRS.es_ES
dc.language.isoenges_ES
dc.publisherEuropean Geosciences Uniones_ES
dc.rightsLicencia CC: Reconocimiento CC BYes_ES
dc.subjectGreenhouse gaseses_ES
dc.subjectClimate changees_ES
dc.subjectRemote sensinges_ES
dc.subjectCarbon dioxidees_ES
dc.titleThe Adaptable 4A Inversion (5AI): Description and first XCO2 retrievals from OCO-2 observations [Discussion paper]es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://dx.doi.org/10.5194/amt-2020-403es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
Colecciones: Artículos científicos 2019-2022


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