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Machine Learning for Forecasting Global Atmospheric Models
Wednesday Jul 15 2020 23:30 GMT
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Machine Learning for Forecasting Global Atmospheric Models
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
  • Details about the methodology of this novel approach

  • Robustness and sensitivity analysis for this approach

  • Uncertainty Quantification and generalizability

Takeaways

Discussed a novel coupling method for PDE solutions and deterministic ML training for weather forecasting tasks

Time of Recording: Wednesday Jul 15 2020 23:30 GMT