Unsupervised classification of the northwestern European seas based on satellite altimetry data

Poropat, Lea; Jones, Dani; Thomas, Simon D. A.; Heuzé, Céline

doi:https://doi.org/10.5194/os-20-201-2024

Articles | Volume 20, issue 1

https://doi.org/10.5194/os-20-201-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Special Issue for the 54th International Liège Colloquium...

https://doi.org/10.5194/os-20-201-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 1

Research article

|

20 Feb 2024

Research article |

| 20 Feb 2024

Unsupervised classification of the northwestern European seas based on satellite altimetry data

Lea Poropat, Dani Jones, Simon D. A. Thomas, and Céline Heuzé

Data sets

The GEBCO_2022 Grid - a continuous terrain model of the global oceans and land GEBCO Bathymetric Compilation Group 2022 https://doi.org/10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c

Global Ocean Gridded L 4 Sea Surface Heights And Derived Variables Reprocessed 1993 Ongoing M.-I. Pujol and F. Mertz https://doi.org/10.48670/moi-00148

Model code and software

leapor/GMMensemble: GMM ensemble (v1.0.0) Lea Poropat and Simon D. A. Thomas https://doi.org/10.5281/zenodo.10356064

Short summary

In this study we use a machine learning method called a Gaussian mixture model to divide part of the ocean (northwestern European seas and part of the Atlantic Ocean) into regions based on satellite observations of sea level. This helps us study each of these regions separately and learn more about what causes sea level changes there. We find that the ocean is first divided based on bathymetry and then based on other features such as water masses and typical atmospheric conditions.