InMOS - Integration of models and observations across scales
InMOS is a project funded by Schmidt Sciences. InMOS will use AI and machine learning to build a framework for integrating both oceanic and atmospheric data across a wide range of space and time scales to improve our ability to quantify warming, deoxygenation, and acidification of the global ocean. This project will rely heavily on M²LInES work, another Schmidt Sciences project led by Laure Zanna.
More information in the Schmidt Sciences press release
Laure Zanna
Joseph B. Keller and Herbert B. Keller Professor in Applied Mathematics; Professor of Mathematics and Data Science
My research interests include Climate Dynamics, Physical Oceanography and Data Science.