Improving rainfall and water-cycle projections through machine learning | AI FOR GOOD DISCOVERY
0Changes in the global water cycle due to greenhouse gas emissions can have a major impact on society and ecosystems. However, climate models struggle to accurately simulate the global water cycle, making it difficult to predict the climate-change response. Common problems include errors in the intensity of rainfall (including extreme rainfall) and the surface winds that influence evaporation and the ocean circulation. We will discuss efforts to develop new machine-learning components of climate models to help address these problems. In this Discovery session we focus on approaches that are physically consistent, stable and robust, and which can be applied over a range of spatial resolutions.
Speakers:
Paul O’Gorman, Professor of Atmospheric Science, MIT
Janni Yuval, Postdoctoral fellow, MIT
@mit
Moderators:
Duncan Watson-Parris, Postdoctoral Research Associate, @oxforduniversity
Philip Stier, Head of Atmospheric, Oceanic and Planetary Physics, @oxforduniversity
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The AI for Good series is the leading action-oriented, global & inclusive United Nations platform on AI. The Summit is organized all year, always online, in Geneva by the ITU with XPRIZE Foundation in partnership with over 35 sister United Nations agencies, Switzerland and ACM. The goal is to identify practical applications of AI and scale those solutions for global impact.
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