Published March 12, 2024 | Version v1
Dataset Open

SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed

  • 1. ROR icon Columbia University
  • 2. ROR icon Lamont-Doherty Earth Observatory
  • 3. ROR icon Pacific Marine Environmental Laboratory
  • 4. ROR icon National Oceanic and Atmospheric Administration
  • 5. ROR icon Yale University

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).

Files

Files (2.4 GB)

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md5:4d89c5b531b16172d9bb7d39ef0d2a02
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md5:589fde2b23259a7d68d9777c1bc92c56
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md5:c084a4c38956f6c2d08a8daf7409b912
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md5:df2adfe41e6924bf95c3776323b8ebf1
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md5:ae06b5857d9f6ed4ca7222b5f34f2c12
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md5:56448af14abed7a029e08bb682dea4ef
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Additional details

Related works

Is supplement to
Journal article: 10.5194/bg-2023-160 (DOI)

Software

Programming language
Python, Jupyter Notebook