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