A novel approach to computing super observations for probabilistic wave model validation
This is the final version. Available on open access from Elsevier via the DOI in this record In the field of wave model validation, the use of super observations is a common strategy to smooth satellite observations and match the simulated spatiotemporal scales. An approach based on averaging along...
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Online Access: | http://hdl.handle.net/10871/37861 https://doi.org/10.1016/j.ocemod.2019.101404 |
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ftunivexeter:oai:ore.exeter.ac.uk:10871/37861 2024-09-15T17:50:06+00:00 A novel approach to computing super observations for probabilistic wave model validation Bohlinger, P Breivik, O Economou, T Muller, M 2019 http://hdl.handle.net/10871/37861 https://doi.org/10.1016/j.ocemod.2019.101404 en eng Elsevier Vol. 139, article 101404 doi:10.1016/j.ocemod.2019.101404 60-CMEMS MFC ARCTIC http://hdl.handle.net/10871/37861 1463-5003 Ocean Modelling © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/) http://creativecommons.org/licenses/BY/4.0/ Wave model Validation Super observation Gaussian process Machine learning Article 2019 ftunivexeter https://doi.org/10.1016/j.ocemod.2019.101404 2024-07-29T03:24:16Z This is the final version. Available on open access from Elsevier via the DOI in this record In the field of wave model validation, the use of super observations is a common strategy to smooth satellite observations and match the simulated spatiotemporal scales. An approach based on averaging along track is widely applied because it is straightforward to implement and adjustable. However, the choice of an appropriate length scale for obtaining the averages can be ambiguous, affecting subsequent analyses. Despite this dilemma, no uncertainty for the validation metric is provided when proceeding with wave model validation. We show that super observations computed from averaging data points applying an inappropriate length scale can lead to a misrepresentation of the wave field which can introduce errors into the wave model validation. Modelling the mean of observations as a Gaussian Process mitigates those errors and reliably identifies outliers by exploiting information hidden in the observational time series. Moreover, the uncertainty accompanying the validation statistic is readily accessible in the Gaussian Process framework. The flexibility of a Gaussian process makes it an attractive candidate for the probabilistic validation of wave models with steadily increasing horizontal resolution. Moreover, this approach can be applied to measurements from other platforms (e.g. buoys) and other variables (e.g. wind). Copernicus Marine Environmental and Monitoring Service Article in Journal/Newspaper Arctic University of Exeter: Open Research Exeter (ORE) Ocean Modelling 139 101404 |
institution |
Open Polar |
collection |
University of Exeter: Open Research Exeter (ORE) |
op_collection_id |
ftunivexeter |
language |
English |
topic |
Wave model Validation Super observation Gaussian process Machine learning |
spellingShingle |
Wave model Validation Super observation Gaussian process Machine learning Bohlinger, P Breivik, O Economou, T Muller, M A novel approach to computing super observations for probabilistic wave model validation |
topic_facet |
Wave model Validation Super observation Gaussian process Machine learning |
description |
This is the final version. Available on open access from Elsevier via the DOI in this record In the field of wave model validation, the use of super observations is a common strategy to smooth satellite observations and match the simulated spatiotemporal scales. An approach based on averaging along track is widely applied because it is straightforward to implement and adjustable. However, the choice of an appropriate length scale for obtaining the averages can be ambiguous, affecting subsequent analyses. Despite this dilemma, no uncertainty for the validation metric is provided when proceeding with wave model validation. We show that super observations computed from averaging data points applying an inappropriate length scale can lead to a misrepresentation of the wave field which can introduce errors into the wave model validation. Modelling the mean of observations as a Gaussian Process mitigates those errors and reliably identifies outliers by exploiting information hidden in the observational time series. Moreover, the uncertainty accompanying the validation statistic is readily accessible in the Gaussian Process framework. The flexibility of a Gaussian process makes it an attractive candidate for the probabilistic validation of wave models with steadily increasing horizontal resolution. Moreover, this approach can be applied to measurements from other platforms (e.g. buoys) and other variables (e.g. wind). Copernicus Marine Environmental and Monitoring Service |
format |
Article in Journal/Newspaper |
author |
Bohlinger, P Breivik, O Economou, T Muller, M |
author_facet |
Bohlinger, P Breivik, O Economou, T Muller, M |
author_sort |
Bohlinger, P |
title |
A novel approach to computing super observations for probabilistic wave model validation |
title_short |
A novel approach to computing super observations for probabilistic wave model validation |
title_full |
A novel approach to computing super observations for probabilistic wave model validation |
title_fullStr |
A novel approach to computing super observations for probabilistic wave model validation |
title_full_unstemmed |
A novel approach to computing super observations for probabilistic wave model validation |
title_sort |
novel approach to computing super observations for probabilistic wave model validation |
publisher |
Elsevier |
publishDate |
2019 |
url |
http://hdl.handle.net/10871/37861 https://doi.org/10.1016/j.ocemod.2019.101404 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Vol. 139, article 101404 doi:10.1016/j.ocemod.2019.101404 60-CMEMS MFC ARCTIC http://hdl.handle.net/10871/37861 1463-5003 Ocean Modelling |
op_rights |
© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/) http://creativecommons.org/licenses/BY/4.0/ |
op_doi |
https://doi.org/10.1016/j.ocemod.2019.101404 |
container_title |
Ocean Modelling |
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139 |
container_start_page |
101404 |
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1810291953161469952 |