Atmospheric Radiation - Using Machine Learning for the Unknowable and Uncomputable - Robert Pincus

Explore a conference talk on leveraging machine learning techniques to address challenges in atmospheric radiation modeling. Delve into how big data and advanced algorithms are revolutionizing climate science, enabling researchers to gain insights into previously unknowable and uncomputable aspects of the Earth's atmosphere. Discover how machine learning is being applied to improve understanding of complex atmospheric processes, enhance climate predictions, and bridge gaps in theoretical knowledge. Gain valuable insights from Robert Pincus as he discusses the intersection of atmospheric science and artificial intelligence, highlighting the potential for data-driven approaches to solve vexing questions in climate research. Learn about the broader context of the Machine Learning for Climate conference at the Kavli Institute for Theoretical Physics, which aims to foster interdisciplinary collaboration and advance climate science through innovative computational methods.

Atmospheric radiation: using machine learning for the unknowable and uncomputable ▸ Robert Pincus