Résumé
I will discuss the Neural GCM, which we built by building a dynamical core in JAX and then training the parameterization on ERA5 on 5-day forecasts. The quality of the forecasts up to 1 year portends a potential revolution in improving model parameterizations of physical systems described by nonlinear partial differential equations, of which weather and climate models are only one example. I will discuss some other problems in this spirit we are working on and try to draw conclusions.
Michael Brenner
Michael Brenner is the Michael F. Cronin Professor of Applied Mathematics and Professor of Physics at Harvard University, and a Research Scientist at Google Research. He is broadly interested in finding new ways of applying mathematics to the sciences.
