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Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection

CITE Title : Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection Personal Author(s) : Sanford, Clayton;Kwa, Anna;Watt‐Meyer, Oliver;Clark, Spencer K.;Brenowitz, Noah;McGibbon, Jeremy;Bretherton, Christopher; Published Date : 2023 Source : Journal of Advances in Modeling Earth Systems, 15(11) DOI : https://doi.org/10.1029/2023ms003809 Journal Title : Journal of Advances in Modeling Earth Systems Sanford, Clayton et al. (2023). Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection. 15(11). https://doi.org/10.1029/2023ms003809 Sanford, Clayton et al. "Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection" 15, no. 11 (2023), https://doi.org/10.1029/2023ms003809 Sanford, Clayton et al. "Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection" vol. 15, no. 11, 2023, https://doi.org/10.1029/2023ms003809 Export RIS Citation Information.

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Sanford, Clayton

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Kwa, Anna

2023 | Journal of Advances in Modeling Earth Systems, 15(11)

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Using machine learning (ML) for the online correction of coarse‐resolution atmospheric models has proven effective in reducing biases in near‐surf...

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