Probabilistic machine learning estimation of ocean mixed layer depth from dense satellite and sparse in situ observations
Foster, D., Gagne, D. J., Whitt, D.. (2021). Probabilistic machine learning estimation of ocean mixed layer depth from dense satellite and sparse in situ observations. Journal of Advances in Modeling Earth Systems, doi:https://doi.org/10.1029/2021MS002474
| Title | Probabilistic machine learning estimation of ocean mixed layer depth from dense satellite and sparse in situ observations |
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| Genre | Article |
| Author(s) | D. Foster, David John Gagne, Daniel Whitt |
| Abstract | The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal ( |
| Publication Title | Journal of Advances in Modeling Earth Systems |
| Publication Date | Dec 30, 2021 |
| Publisher's Version of Record | https://doi.org/10.1029/2021MS002474 |
| OpenSky Citable URL | https://n2t.org/ark:/85065/d72f7rzj |
| OpenSky Listing | View on OpenSky |
| CISL Affiliations | TDD, AIML |