You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
## M²LInES research and other relevant publications
9
9
10
-
If you are interested in understanding how M²LInES is using machine learning to improve climate models, we have developed an educational JupyterBook [Learning Machine Learning for Climate modeling with Lorenz 96](https://m2lines.github.io/L96_demo). This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHubhttps://github.com/m2lines/L96_demo.
10
+
If you are interested in understanding how M²LInES is using machine learning to improve climate models, we have developed an educational JupyterBook [Learning Machine Learning for Climate modeling with Lorenz 96](https://m2lines.github.io/L96_demo). This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHub.
11
11
12
12
<imgsrc="/images/newlogo.png"style="width: 1.5vw; height: 1.5hw; vertical-align: middle;"alt="DOI icon"> M²LInES funded research
<strong>William Gregory, Ronald MacEachern, So Takao, Isobel R. Lawrence, Carmen Nab, Marc Peter Deisenroth, Michel Tsamados </strong><br>
23
+
<a href="https://doi.org/10.1038/s41467-024-51900-x" target="_blank"><strong>Scalable interpolation of satellite altimetry data with probabilistic machine learning</strong></a><br>
<a href="https://doi.org/10.1038/s41558-024-02095-y" target="_blank"><strong>SPushing the frontiers in climate modelling and analysis with machine learning</strong></a><br>
0 commit comments