Scalar and Multivariate Approaches for Optimal Network Design in Antarctica
Thesis (Master's)--University of Washington, 2014 Observations 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...
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ftunivwashington:oai:digital.lib.washington.edu:1773/26584 2023-05-15T14:13:28+02:00 Scalar and Multivariate Approaches for Optimal Network Design in Antarctica Hryniw, Natalia Hakim, Gregory J 2014 application/pdf http://hdl.handle.net/1773/26584 en_US eng Hryniw_washington_0250O_13155.pdf http://hdl.handle.net/1773/26584 Copyright is held by the individual authors. Meteorology atmospheric sciences Thesis 2014 ftunivwashington 2023-03-12T18:51:45Z Thesis (Master's)--University of Washington, 2014 Observations 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. Thesis Antarc* Antarctic Antarctica University of Washington, Seattle: ResearchWorks Antarctic The Antarctic |
institution |
Open Polar |
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University of Washington, Seattle: ResearchWorks |
op_collection_id |
ftunivwashington |
language |
English |
topic |
Meteorology atmospheric sciences |
spellingShingle |
Meteorology atmospheric sciences Hryniw, Natalia Scalar and Multivariate Approaches for Optimal Network Design in Antarctica |
topic_facet |
Meteorology atmospheric sciences |
description |
Thesis (Master's)--University of Washington, 2014 Observations 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. |
author2 |
Hakim, Gregory J |
format |
Thesis |
author |
Hryniw, Natalia |
author_facet |
Hryniw, Natalia |
author_sort |
Hryniw, Natalia |
title |
Scalar and Multivariate Approaches for Optimal Network Design in Antarctica |
title_short |
Scalar and Multivariate Approaches for Optimal Network Design in Antarctica |
title_full |
Scalar and Multivariate Approaches for Optimal Network Design in Antarctica |
title_fullStr |
Scalar and Multivariate Approaches for Optimal Network Design in Antarctica |
title_full_unstemmed |
Scalar and Multivariate Approaches for Optimal Network Design in Antarctica |
title_sort |
scalar and multivariate approaches for optimal network design in antarctica |
publishDate |
2014 |
url |
http://hdl.handle.net/1773/26584 |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic Antarctica |
genre_facet |
Antarc* Antarctic Antarctica |
op_relation |
Hryniw_washington_0250O_13155.pdf http://hdl.handle.net/1773/26584 |
op_rights |
Copyright is held by the individual authors. |
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1766285937334026240 |