Climate scientist @UCSD using machine learning to improve our understanding of clouds and aerosols. 🏄♂️
Find out more about the Climate Analytics Lab here: https://climate-analytics-lab.github.io/
📦 pip install jcm 📄 Paper: egusphere.copernicus.org/preprints/20... 💻 Code: github.com/climate-anal...
This release sets the stage for a fully coupled, differentiable Earth System Model. We are actively developing this next phase with colleagues at U Copenhagen, Columbia, DLR, and AI2.
Watch this space...
Introducing JCM v1.0: A fully differentiable, intermediate-complexity atmospheric model built in Python/JAX.
Training hybrid models is hard because legacy code lacks gradients. JCM solves this.
Reposted by Duncan Watson‐Parris
The talk will be live-streamed on 23 September at 17:00 CEST! Sign up for free here: neuralnetwork.aiforgood.itu.int/event/ai-for...
Reposted by Gavin A. Schmidt
Reposted by Duncan Watson‐Parris
Reposted by Chris Smith, Duncan Watson‐Parris
Reposted by Duncan Watson‐Parris