Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83994
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dc.contributor.authorHill, Emma Maryen
dc.contributor.authorWang, Leien
dc.contributor.authorDavis, James L.en
dc.contributor.authorTamisiea, Mark E.en
dc.date.accessioned2016-10-04T06:07:59Zen
dc.date.accessioned2019-12-06T15:36:05Z-
dc.date.available2016-10-04T06:07:59Zen
dc.date.available2019-12-06T15:36:05Z-
dc.date.issued2016en
dc.identifier.citationWang, L., Davis, J. L., Hill, E. M., & Tamisiea, M. E. (2016). Stochastic filtering for determining gravity variations for decade-long time series of GRACE gravity. Journal of Geophysical Research: Solid Earth, 121(4), 2915-2931.en
dc.identifier.issn2169-9313en
dc.identifier.urihttps://hdl.handle.net/10356/83994-
dc.description.abstractWe present a new stochastic filter technique for statistically rigorous separation of gravity signals and correlated “stripe” noises in a series of monthly gravitational spherical harmonic coefficients (SHCs) produced by the Gravity Recovery and Climate Experiment (GRACE) satellite mission. Unlike the standard destriping process that removes the stripe contamination empirically, the stochastic approach simultaneously estimates gravity signals and correlated noises relying on covariance information that reflects both the spatial spectral features and temporal correlations among them. A major benefit of the technique is that by estimating the stripe noise in a Bayesian framework, we are able to propagate statistically rigorous covariances for the destriped GRACE SHCs, i.e. incorporating the impact of the destriping on the SHC uncertainties. The Bayesian approach yields a natural resolution for the gravity signal that reflects the correlated stripe noise, and thus achieve a kind of spatial smoothing in and of itself. No spatial Gaussian smoothing is formally required although it might be useful for some circumstances. Using the stochastic filter, we process a decade-length series of GRACE monthly gravity solutions, and compare the results with GRACE Tellus data products that are processed using the “standard” destriping procedure. The results show that the stochastic filter is able to remove the correlated stripe noise to a remarkable degree even without an explicit smoothing step. The estimates from the stochastic filter for each destriped GRACE field are suitable for Bayesian integration of GRACE with other geodetic measurements and models, and the statistically rigorous estimation of the time-varying rates and seasonal cycles in GRACE time series.en
dc.format.extent17 p.en
dc.language.isoenen
dc.relation.ispartofseriesJournal of Geophysical Research: Solid Earthen
dc.rights© 2016 American Geophysical Union (AGU). This paper was published in Journal of Geophysical Research: Solid Earth and is made available as an electronic reprint (preprint) with permission of American Geophysical Union (AGU). The published version is available at: [http://dx.doi.org/10.1002/2015JB012650]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en
dc.subjectGRACEen
dc.subjecttime-variable gravityen
dc.titleStochastic filtering for determining gravity variations for decade-long time series of GRACE gravityen
dc.typeJournal Articleen
dc.contributor.researchEarth Observatory of Singaporeen
dc.identifier.doi10.1002/2015JB012650en
dc.description.versionPublished versionen
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item.grantfulltextopen-
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