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Uncertainty quantification for remote sensing data: Sensitivity to a priori conditions in optimal estimation retrieval algorithms

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dc.contributor.author Teixeira, Joaquim
dc.contributor.author Braverman, Amy
dc.contributor.author Hobbs, Jon
dc.contributor.author Gunson, Michael
dc.date.accessioned 2020-10-14T00:08:58Z
dc.date.available 2020-10-14T00:08:58Z
dc.date.issued 2018-07-28
dc.identifier.citation 2018 Joint Statistical Meetings (JSM 2018), Vancouver, Canada, July 28-August 2, 2018 en_US
dc.identifier.clearanceno 18-3871
dc.identifier.uri http://hdl.handle.net/2014/50278
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2018 en_US
dc.title Uncertainty quantification for remote sensing data: Sensitivity to a priori conditions in optimal estimation retrieval algorithms en_US
dc.type Presentation en_US


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