Computationally efficient methods for large-scale atmospheric inverse modeling

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dc.contributor.authorCho, Taewonen
dc.contributor.authorChung, Julianneen
dc.contributor.authorMiller, Scot M.en
dc.contributor.authorSaibaba, Arvind K.en
dc.date.accessioned2022-10-27T16:50:03Zen
dc.date.available2022-10-27T16:50:03Zen
dc.date.issued2022-07-20en
dc.description.abstractAtmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges for standard inverse modeling frameworks that were often originally designed with much smaller data sets in mind. In this article, we discuss computationally efficient methods for large-scale atmospheric inverse modeling and focus on addressing some of the main computational and practical challenges. We develop generalized hybrid projection methods, which are iterative methods for solving large-scale inverse problems, and specifically we focus on the case of estimating surface fluxes. These algorithms confer several advantages. They are efficient, in part because they converge quickly, they exploit efficient matrix-vector multiplications, and they do not require inversion of any matrices. These methods are also robust because they can accurately reconstruct surface fluxes, they are automatic since regularization or covariance matrix parameters and stopping criteria can be determined as part of the iterative algorithm, and they are flexible because they can be paired with many different types of atmospheric models. We demonstrate the benefits of generalized hybrid methods with a case study from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. We then address the more challenging problem of solving the inverse model when the mean of the surface fluxes is not known a priori; we do so by reformulating the problem, thereby extending the applicability of hybrid projection methods to include hierarchical priors. We further show that by exploiting mathematical relations provided by the generalized hybrid method, we can efficiently calculate an approximate posterior variance, thereby providing uncertainty information.en
dc.description.notesThis work was partially supported by the National Science Foundation ATD program under grant nos. DMS-2026841, DMS-2026830, and DMS-2026835 and by the National Aeronautics and Space Administration under grant no. 80NSSC18K0976. This material was also based upon work partially supported by the National Science Foundation under grant no. DMS-1638521 to the Statistical and Applied Mathematical Sciences Institute. The OCO-2 CarbonTracker-Lagrange footprint library was produced by NOAA/GML and AER Inc with support from NASA Carbon Monitoring System project Andrews (CMS 2014) Regional Inverse Modeling in North and South America for the NASA Carbon Monitoring System. We especially thank Arlyn Andrews, Michael Trudeau, and Marikate Mountain for generating and assisting with the footprint library. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.en
dc.description.sponsorshipNational Science Foundation ATD program [DMS-2026841, DMS-2026830, DMS-2026835]; National Aeronautics and Space Administration [80NSSC18K0976]; National Science Foundation [DMS-1638521]; NASA Carbon Monitoring System project Andrews (CMS 2014) Regional Inverse Modeling in North and South Americaen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.5194/gmd-15-5547-2022en
dc.identifier.eissn1991-9603en
dc.identifier.issn1991-959Xen
dc.identifier.issue14en
dc.identifier.urihttp://hdl.handle.net/10919/112300en
dc.identifier.volume15en
dc.language.isoenen
dc.publisherCopernicusen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectvariational data assimilationen
dc.subjectcarbon-dioxideen
dc.subjectco2en
dc.subjectoco-2en
dc.subjectregularizationen
dc.subjectretrievalsen
dc.subjectsatelliteen
dc.subjectexampleen
dc.subjecthybriden
dc.subjectcycleen
dc.titleComputationally efficient methods for large-scale atmospheric inverse modelingen
dc.title.serialGeoscientific Model Developmenten
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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