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dc.contributor.advisorHakim, Gregory Jen_US
dc.contributor.authorHryniw, Nataliaen_US
dc.date.accessioned2014-10-13T16:58:12Z
dc.date.available2014-10-13T16:58:12Z
dc.date.submitted2014en_US
dc.identifier.otherHryniw_washington_0250O_13155.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/26157
dc.descriptionThesis (Master's)--University of Washington, 2014en_US
dc.description.abstractObservations are crucial for weather and climate, not only for daily forecasts and logistical purposes, for but maintaining representative records and for tuning atmospheric models. Here scalar theory for optimal network design is expanded in a multivariate framework, to allow for optimal station siting for full field optimization. Ensemble sensitivity theory is expanded to produce the covariance trace approach, which optimizes for the trace of the covariance matrix. Relative entropy is also used for multivariate optimization as an information theory approach for finding optimal locations. Antarctic surface temperature data is used as a testbed for these methods. Both methods produce different results which are tied to the fundamental physical parameters of the Antarctic temperature field.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subject.otherMeteorologyen_US
dc.subject.otheratmospheric sciencesen_US
dc.titleScalar and Multivariate Approaches for Optimal Network Design in Antarcticaen_US
dc.typeThesisen_US
dc.embargo.termsOpen Accessen_US


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