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Machine Learning Roles in Earth System Modeling
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The School of Earth and Atmospheric Sciences Presents Dr. David John Gagne, National Center for Atmospheric Research
Machine Learning Roles in Earth System Modeling
Machine learning has the potential to improve many aspects of Earth System modeling by accelerating computationally intensive process models with emulation and by bridging modeling gaps where no clear physical equation can be derived. However, many challenges remain in ensuring that machine learning models can perform robustly and integrate seamlessly within Earth System models.
The NCAR Analytics and Integrative Machine Learning group is currently working on machine learning challenges for ocean mixed layer depth retrievals, bin microphysics emulation, and atmospheric chemistry modeling. We will discuss our successes as well as challenges in ensuring robust online performance and incorporating emulators within existing simulations.
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- Workflow Status:Published
- Created By:nlawson3
- Created:12/10/2020
- Modified By:nlawson3
- Modified:01/25/2021
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