Why This Is Interesting
Data driven approaches to prediction chaotic spatiotemporal dynamical systems have been shown to be successfully for a number of high dimensional, complex systems. One of the most important chaotic systems which impacts our lives daily is the atmosphere. This, naturally, leads to the question whether a purely data driven machine learning algorithm can accurately predict the weather. In this talk, we present a prototype machine learning only model that can skillfully predict the three-dimensional atmosphere for 3-5 days. The parallel machine learning technique used is computationally highly efficient and allows training to take place over thousands of computer cores.
Discussion Points
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Details about the methodology of this novel approach
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Robustness and sensitivity analysis for this approach
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Uncertainty Quantification and generalizability
Takeaways
Discussed a novel coupling method for PDE solutions and deterministic ML training for weather forecasting tasks