SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed ...
Here we provide sampling masks used in the study "Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling" (Heimdal et al., 2023, https://doi.org/10.5194/bg-2023-160). In this paper, we reconstruct surface ocean pCO2 using the La...
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Online Access: | https://dx.doi.org/10.5281/zenodo.10811018 https://zenodo.org/doi/10.5281/zenodo.10811018 |
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ftdatacite:10.5281/zenodo.10811018 2024-04-28T08:39:16+00:00 SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed ... Heimdal, Thea H. McKinley, Galen Sutton, Adrienne Fay, Amanda Gloege, Lucas 2024 https://dx.doi.org/10.5281/zenodo.10811018 https://zenodo.org/doi/10.5281/zenodo.10811018 unknown Zenodo https://dx.doi.org/10.5194/bg-2023-160 https://dx.doi.org/10.5281/zenodo.10811017 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 dataset Dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.1081101810.5194/bg-2023-16010.5281/zenodo.10811017 2024-04-02T11:34:20Z Here we provide sampling masks used in the study "Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling" (Heimdal et al., 2023, https://doi.org/10.5194/bg-2023-160). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 11 different sampling masks that correspond to the experiments presented in Heimdal et al. (2023), which include different sampling patterns of USV Saildrones in the Southern Ocean (SOCAT+USV sampling). ... Dataset Southern Ocean DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
description |
Here we provide sampling masks used in the study "Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling" (Heimdal et al., 2023, https://doi.org/10.5194/bg-2023-160). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 11 different sampling masks that correspond to the experiments presented in Heimdal et al. (2023), which include different sampling patterns of USV Saildrones in the Southern Ocean (SOCAT+USV sampling). ... |
format |
Dataset |
author |
Heimdal, Thea H. McKinley, Galen Sutton, Adrienne Fay, Amanda Gloege, Lucas |
spellingShingle |
Heimdal, Thea H. McKinley, Galen Sutton, Adrienne Fay, Amanda Gloege, Lucas SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed ... |
author_facet |
Heimdal, Thea H. McKinley, Galen Sutton, Adrienne Fay, Amanda Gloege, Lucas |
author_sort |
Heimdal, Thea H. |
title |
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed ... |
title_short |
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed ... |
title_full |
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed ... |
title_fullStr |
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed ... |
title_full_unstemmed |
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed ... |
title_sort |
socat+usv sampling masks for ml reconstruction of surface ocean pco2 using the large ensemble testbed ... |
publisher |
Zenodo |
publishDate |
2024 |
url |
https://dx.doi.org/10.5281/zenodo.10811018 https://zenodo.org/doi/10.5281/zenodo.10811018 |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
https://dx.doi.org/10.5194/bg-2023-160 https://dx.doi.org/10.5281/zenodo.10811017 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.1081101810.5194/bg-2023-16010.5281/zenodo.10811017 |
_version_ |
1797570377518415872 |