Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon
When working with Earth system models, a considerable challenge that arises is the need to establish the set of parameter values that ensure the optimal model performance in terms of how they reflect real-world observed data. Given that each additional parameter under investigation increases the dim...
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Copernicus Publications
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00061968 2023-05-15T17:31:36+02:00 Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon Falls, Marcus Bernardello, Raffaele Castrillo, Miguel Acosta, Mario Llort, Joan Galí, Martí 2022-07 electronic https://doi.org/10.5194/gmd-15-5713-2022 https://noa.gwlb.de/receive/cop_mods_00061968 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061326/gmd-15-5713-2022.pdf https://gmd.copernicus.org/articles/15/5713/2022/gmd-15-5713-2022.pdf eng eng Copernicus Publications Geoscientific Model Development -- http://www.bibliothek.uni-regensburg.de/ezeit/?2456725 -- http://www.geosci-model-dev.net/ -- 1991-9603 https://doi.org/10.5194/gmd-15-5713-2022 https://noa.gwlb.de/receive/cop_mods_00061968 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061326/gmd-15-5713-2022.pdf https://gmd.copernicus.org/articles/15/5713/2022/gmd-15-5713-2022.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2022 ftnonlinearchiv https://doi.org/10.5194/gmd-15-5713-2022 2022-07-31T23:11:44Z When working with Earth system models, a considerable challenge that arises is the need to establish the set of parameter values that ensure the optimal model performance in terms of how they reflect real-world observed data. Given that each additional parameter under investigation increases the dimensional space of the problem by one, simple brute-force sensitivity tests can quickly become too computationally strenuous. In addition, the complexity of the model and interactions between parameters mean that testing them on an individual basis has the potential to miss key information. In this work, we address these challenges by developing a biased random key genetic algorithm (BRKGA) able to estimate model parameters. This method is tested using the one-dimensional configuration of PISCES-v2_RC, the biogeochemical component of NEMO4 v4.0.1 (Nucleus for European Modelling of the Ocean version 4), a global ocean model. A test case of particulate organic carbon (POC) in the North Atlantic down to 1000 m depth is examined, using observed data obtained from autonomous biogeochemical Argo floats. In this case, two sets of tests are run, namely one where each of the model outputs are compared to the model outputs with default settings and another where they are compared with three sets of observed data from their respective regions, which is followed by a cross-reference of the results. The results of these analyses provide evidence that this approach is robust and consistent and also that it provides an indication of the sensitivity of parameters on variables of interest. Given the deviation in the optimal set of parameters from the default, further analyses using observed data in other locations are recommended to establish the validity of the results obtained. Article in Journal/Newspaper North Atlantic Niedersächsisches Online-Archiv NOA Geoscientific Model Development 15 14 5713 5737 |
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Niedersächsisches Online-Archiv NOA |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Falls, Marcus Bernardello, Raffaele Castrillo, Miguel Acosta, Mario Llort, Joan Galí, Martí Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon |
topic_facet |
article Verlagsveröffentlichung |
description |
When working with Earth system models, a considerable challenge that arises is the need to establish the set of parameter values that ensure the optimal model performance in terms of how they reflect real-world observed data. Given that each additional parameter under investigation increases the dimensional space of the problem by one, simple brute-force sensitivity tests can quickly become too computationally strenuous. In addition, the complexity of the model and interactions between parameters mean that testing them on an individual basis has the potential to miss key information. In this work, we address these challenges by developing a biased random key genetic algorithm (BRKGA) able to estimate model parameters. This method is tested using the one-dimensional configuration of PISCES-v2_RC, the biogeochemical component of NEMO4 v4.0.1 (Nucleus for European Modelling of the Ocean version 4), a global ocean model. A test case of particulate organic carbon (POC) in the North Atlantic down to 1000 m depth is examined, using observed data obtained from autonomous biogeochemical Argo floats. In this case, two sets of tests are run, namely one where each of the model outputs are compared to the model outputs with default settings and another where they are compared with three sets of observed data from their respective regions, which is followed by a cross-reference of the results. The results of these analyses provide evidence that this approach is robust and consistent and also that it provides an indication of the sensitivity of parameters on variables of interest. Given the deviation in the optimal set of parameters from the default, further analyses using observed data in other locations are recommended to establish the validity of the results obtained. |
format |
Article in Journal/Newspaper |
author |
Falls, Marcus Bernardello, Raffaele Castrillo, Miguel Acosta, Mario Llort, Joan Galí, Martí |
author_facet |
Falls, Marcus Bernardello, Raffaele Castrillo, Miguel Acosta, Mario Llort, Joan Galí, Martí |
author_sort |
Falls, Marcus |
title |
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon |
title_short |
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon |
title_full |
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon |
title_fullStr |
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon |
title_full_unstemmed |
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon |
title_sort |
use of genetic algorithms for ocean model parameter optimisation: a case study using pisces-v2_rc for north atlantic particulate organic carbon |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/gmd-15-5713-2022 https://noa.gwlb.de/receive/cop_mods_00061968 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061326/gmd-15-5713-2022.pdf https://gmd.copernicus.org/articles/15/5713/2022/gmd-15-5713-2022.pdf |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
Geoscientific Model Development -- http://www.bibliothek.uni-regensburg.de/ezeit/?2456725 -- http://www.geosci-model-dev.net/ -- 1991-9603 https://doi.org/10.5194/gmd-15-5713-2022 https://noa.gwlb.de/receive/cop_mods_00061968 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061326/gmd-15-5713-2022.pdf https://gmd.copernicus.org/articles/15/5713/2022/gmd-15-5713-2022.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/gmd-15-5713-2022 |
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Geoscientific Model Development |
container_volume |
15 |
container_issue |
14 |
container_start_page |
5713 |
op_container_end_page |
5737 |
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1766129259499225088 |