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A statistical filtering approach for Gravity Recovery and Climate Experiment (GRACE) gravity data

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A statistical filtering approach for Gravity Recovery and Climate Experiment (GRACE) gravity data

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dc.contributor.author Davis, James L.
dc.contributor.author Tamisiea, Mark E.
dc.contributor.author Elosegui, Pedro
dc.contributor.author Mitrovica, J. X.
dc.contributor.author Hill, Emma M.
dc.date.accessioned 2012-06-21T01:28:27Z
dc.date.available 2012-06-21T01:28:27Z
dc.date.copyright 2008
dc.date.issued 2012-06-21
dc.identifier.citation Davis, J. L., Tamisiea, M. E., Elósegui, P., Mitrovica, J. X., & Hill, E. M. (2008). A statistical filtering approach for Gravity Recovery and Climate Experiment (GRACE) gravity data. Journal of Geophysical Research, 113.
dc.identifier.uri http://hdl.handle.net/10220/8227
dc.description.abstract We describe and analyze a statistical filtering approach for Gravity Recovery and Climate Experiment (GRACE) data that uses a parameterized model for the temporal evolution of the GRACE coefficients. After least squares adjustment, a statistical test is performed to assess the significance of the estimated parameters. If the test is passed, the parameters are used by the filter in the reconstruction of the field; otherwise, they are rejected. The test is performed, and the filter is formed, separately for annual components of the model and the trend. This new approach is distinct from Gaussian smoothing since it uses the data themselves to test for specific components of the time-varying gravity field. The statistical filter appears inherently to remove most of the “stripes” present in the GRACE fields, although destriping the fields prior to filtering seems to help the trend recovery. We demonstrate that the statistical filter produces reasonable maps for the annual components and trend. We furthermore assess the statistical filter for the annual components using ground-based GPS data in South America by assuming that the annual component of the gravity signal is associated only with groundwater storage. The undestriped, statistically filtered field has a χ2 value relative to the GPS data consistent with the best result from smoothing. In the space domain, the statistical filters are qualitatively similar to Gaussian smoothing. Unlike Gaussian smoothing, however, the statistical filter has significant sidelobes, including large negative sidelobes on the north-south axis, potentially revealing information on the errors, and the correlations among the errors, for the GRACE coefficients.
dc.format.extent 14 p.
dc.language.iso en
dc.relation.ispartofseries Journal of geophysical research
dc.rights © 2008 American Geophysical Union.This paper was published in Journal of Geophysical Research and is made available as an electronic reprint (preprint) with permission of American Geophysical Union. The paper can be found at the following official URL: [http://dx.doi.org/10.1029/2007JB005043]. 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.
dc.subject DRNTU::Science::Geology
dc.title A statistical filtering approach for Gravity Recovery and Climate Experiment (GRACE) gravity data
dc.type Journal Article
dc.identifier.doi http://dx.doi.org/10.1029/2007JB005043
dc.description.version Published version

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