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dc.contributor.authorRigby, Matthew
dc.contributor.authorGanesan, Anita Lakshmi
dc.contributor.authorPrinn, Ronald G.
dc.date.accessioned2014-04-15T20:50:14Z
dc.date.available2014-04-15T20:50:14Z
dc.date.issued2011-04
dc.date.submitted2011-02
dc.identifier.issn0148-0227
dc.identifier.urihttp://hdl.handle.net/1721.1/86177
dc.description.abstractA growth-based Bayesian inverse method is presented for deriving emissions of atmospheric trace species from temporally sparse measurements of their mole fractions. This work is motivated by many recent studies that have deduced emissions using archived air samples with measurement intervals of the order of a year or longer in the early part of the record. Several techniques have been used to make this underdetermined problem invertible. These include the incorporation of prior emissions estimates, the smoothing of observations or derived emissions, the approximation of emissions time series by polynomials, or the application of regularization schemes. However, these methods often suffer from limitations, such as the unavailability of independent, unbiased priors, the emergence of unrealistic emissions fluctuations due to measurement outliers, or the subjective choice of measurement or emissions smoothing time scales. This paper presents an alternative solution that reduces the influence of potentially biased priors or measurement outliers by constraining the emissions growth rate around some growth estimate, in conjunction with the model-measurement mismatch.en_US
dc.description.sponsorshipUnited States. National Aeronautics and Space Administration (NASA Upper Atmospheric Research Program, grant NNX07AE89G )en_US
dc.language.isoen_US
dc.publisherAmerican Geophysical Unionen_US
dc.relation.isversionofhttp://dx.doi.org/10.1029/2010jd015401en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceOther repositoryen_US
dc.titleDeriving emissions time series from sparse atmospheric mole fractionsen_US
dc.typeArticleen_US
dc.identifier.citationRigby, M., A. L. Ganesan, and R. G. Prinn. “Deriving Emissions Time Series from Sparse Atmospheric Mole Fractions.” Journal of Geophysical Research 116, no. D8 (2011).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Center for Global Change Scienceen_US
dc.contributor.mitauthorRigby, Matthewen_US
dc.contributor.mitauthorGanesan, Anita Lakshmien_US
dc.contributor.mitauthorPrinn, Ronald G.en_US
dc.relation.journalJournal of Geophysical Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsRigby, M.; Ganesan, A. L.; Prinn, R. G.en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-5925-3801
mit.licensePUBLISHER_POLICYen_US


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