Methods of Ensemble Data Assimilation on Adaptive Moving Meshes ...
Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. In particular, neXtSIM is a 2D model of sea-ice that is numerically solved on a Lagrangian mesh that does not conserve the number of mesh points. In this dissertation, we present two novel approache...
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The University of North Carolina at Chapel Hill University Libraries
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ftdatacite:10.17615/bwc6-3891 2024-03-31T07:55:19+00:00 Methods of Ensemble Data Assimilation on Adaptive Moving Meshes ... Guider, Colin Thomas 2019 https://dx.doi.org/10.17615/bwc6-3891 https://cdr.lib.unc.edu/concern/dissertations/1831cq45m en eng The University of North Carolina at Chapel Hill University Libraries In Copyright - Educational Use Permitted http://rightsstatements.org/vocab/InC-EDU/1.0/ Text thesis Dissertation Thesis 2019 ftdatacite https://doi.org/10.17615/bwc6-3891 2024-03-04T11:37:53Z Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. In particular, neXtSIM is a 2D model of sea-ice that is numerically solved on a Lagrangian mesh that does not conserve the number of mesh points. In this dissertation, we present two novel approaches to the formulation of ensemble data assimilation for models with this underlying computational structure. Specifically, we map ensemble members onto a common reference mesh, where the Ensemble Kalman Filter (EnKF) can be performed. Numerical experiments are carried out using 1D prototypical models: Burgers and Kuramoto-Sivashinsky equations, with both Eulerian and Lagrangian synthetic observations assimilated. One of the approaches is very effective, while the other is significantly less so. We also present a novel approach in the formulation of the Local Ensemble Transform Kalman Filter (LETKF) on a conservative moving mesh model. This is also achieved by mapping the ensemble members onto a common reference ... Thesis Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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English |
description |
Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. In particular, neXtSIM is a 2D model of sea-ice that is numerically solved on a Lagrangian mesh that does not conserve the number of mesh points. In this dissertation, we present two novel approaches to the formulation of ensemble data assimilation for models with this underlying computational structure. Specifically, we map ensemble members onto a common reference mesh, where the Ensemble Kalman Filter (EnKF) can be performed. Numerical experiments are carried out using 1D prototypical models: Burgers and Kuramoto-Sivashinsky equations, with both Eulerian and Lagrangian synthetic observations assimilated. One of the approaches is very effective, while the other is significantly less so. We also present a novel approach in the formulation of the Local Ensemble Transform Kalman Filter (LETKF) on a conservative moving mesh model. This is also achieved by mapping the ensemble members onto a common reference ... |
format |
Thesis |
author |
Guider, Colin Thomas |
spellingShingle |
Guider, Colin Thomas Methods of Ensemble Data Assimilation on Adaptive Moving Meshes ... |
author_facet |
Guider, Colin Thomas |
author_sort |
Guider, Colin Thomas |
title |
Methods of Ensemble Data Assimilation on Adaptive Moving Meshes ... |
title_short |
Methods of Ensemble Data Assimilation on Adaptive Moving Meshes ... |
title_full |
Methods of Ensemble Data Assimilation on Adaptive Moving Meshes ... |
title_fullStr |
Methods of Ensemble Data Assimilation on Adaptive Moving Meshes ... |
title_full_unstemmed |
Methods of Ensemble Data Assimilation on Adaptive Moving Meshes ... |
title_sort |
methods of ensemble data assimilation on adaptive moving meshes ... |
publisher |
The University of North Carolina at Chapel Hill University Libraries |
publishDate |
2019 |
url |
https://dx.doi.org/10.17615/bwc6-3891 https://cdr.lib.unc.edu/concern/dissertations/1831cq45m |
genre |
Sea ice |
genre_facet |
Sea ice |
op_rights |
In Copyright - Educational Use Permitted http://rightsstatements.org/vocab/InC-EDU/1.0/ |
op_doi |
https://doi.org/10.17615/bwc6-3891 |
_version_ |
1795037008259710976 |