El Plan Beveridge.docx
Abstract: Predicting extreme weather events in a warming world at fine scales is a grand challenge faced by climate scientists. Policy makers and society at large depend on reliable predictions to plan for the disastrous impact of climate change and develop effective adaptation strategies. Deep learning (DL) offers novel methods that are potentially more accurate and orders of magnitude faster than traditional weather and climate models for predicting extreme events. The Fourier Neural Operator (FNO), a novel deep learning method has shown promising results for predicting complex systems, such as spatio-temporal chaos, turbulence, and weather phenomena. I will give an overview of the method as well as our recent results.
el plan beveridge.docx
Submissions for the Proposals track should describe detailed ideas for how machine learning can be used to solve climate-relevant problems. While less constrained than the Papers track, Proposals will be subject to a very high standard of review. Ideas should be justified as extensively as possible, including motivation for why the problem being solved is important in tackling climate change, discussion of why current methods are inadequate, explanation of the proposed method, and discussion of the pathway to climate impact. Preliminary results are optional. 041b061a72