Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131
Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global and limited area domains, discretized for applications of numerical models of the relevant fluid equations. Often, limited area models are constructed to interpre...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.333.430 2023-05-15T17:06:09+02:00 Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131 Christopher K. Wikle Ralph F. Milliff The Pennsylvania State University CiteSeerX Archives 2003 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.333.430 http://www.stat.missouri.edu/~wikle/BCfinalMWR154.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.333.430 http://www.stat.missouri.edu/~wikle/BCfinalMWR154.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.stat.missouri.edu/~wikle/BCfinalMWR154.pdf text 2003 ftciteseerx 2016-09-11T00:05:56Z Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global and limited area domains, discretized for applications of numerical models of the relevant fluid equations. Often, limited area models are constructed to interpret intensive datasets collected over a specific region, from a variety of observational platforms. These data are noisy and they typically do not span the domain of interest uniformly in space and time. Traditional numerical procedures cannot easily account for these uncertainties. A hierarchical Bayesian modeling framework is developed for solving boundary value problems in such settings. By allowing the boundary process to be stochastic, and conditioning the interior process on this boundary, one can account for the uncertainties in the boundary process in a reasonable fashion. In the presence of data and all its uncertainties, this idea can be related through Bayes ’ Theorem to produce distributions of the interior process given the observational data. The method is illustrated with an example of obtaining atmospheric streamfunction fields in the Labrador Sea region, given scatterometer-derived observations of the surface wind field. 1 1 Text Labrador Sea Unknown |
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Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global and limited area domains, discretized for applications of numerical models of the relevant fluid equations. Often, limited area models are constructed to interpret intensive datasets collected over a specific region, from a variety of observational platforms. These data are noisy and they typically do not span the domain of interest uniformly in space and time. Traditional numerical procedures cannot easily account for these uncertainties. A hierarchical Bayesian modeling framework is developed for solving boundary value problems in such settings. By allowing the boundary process to be stochastic, and conditioning the interior process on this boundary, one can account for the uncertainties in the boundary process in a reasonable fashion. In the presence of data and all its uncertainties, this idea can be related through Bayes ’ Theorem to produce distributions of the interior process given the observational data. The method is illustrated with an example of obtaining atmospheric streamfunction fields in the Labrador Sea region, given scatterometer-derived observations of the surface wind field. 1 1 |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Christopher K. Wikle Ralph F. Milliff |
spellingShingle |
Christopher K. Wikle Ralph F. Milliff Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131 |
author_facet |
Christopher K. Wikle Ralph F. Milliff |
author_sort |
Christopher K. Wikle |
title |
Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131 |
title_short |
Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131 |
title_full |
Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131 |
title_fullStr |
Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131 |
title_full_unstemmed |
Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131 |
title_sort |
hierarchical bayesian approach to boundary value problems with stochastic boundary conditions. monthly weather review 131 |
publishDate |
2003 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.333.430 http://www.stat.missouri.edu/~wikle/BCfinalMWR154.pdf |
genre |
Labrador Sea |
genre_facet |
Labrador Sea |
op_source |
http://www.stat.missouri.edu/~wikle/BCfinalMWR154.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.333.430 http://www.stat.missouri.edu/~wikle/BCfinalMWR154.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766061165292552192 |