Using AI to predict the weather

Using AI to predict the weather

Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us.

In this episode, researchers discuss advances in AI-enabled weather forecasting.

In this episode, we cover:
•[00:00] Introduction
•[01:12] Jeffrey Shrader explains what 48 expert forecasters had to say about how weather predictions might further improve through 2100, including the potential role of AI.
•[03:43] Ignacio Lopez-Gomez explains how he used generative AI to downscale large-scale earth system models into finer-scale regional climate projections.
•[05:35] Xiaofeng Li explains how he used a machine learning model to forecast whether tropical cyclones will rapidly intensify.
•[07:40] Hui Su explains what nowcasting is and how her deep diffusion model works.
•[09:52] Pedram Hassanzadeh explains what grey swans are and why they may be challenging for AI to predict.
•[10:47] Qiang Sun explains how he and his colleagues tested the ability of AI to predict gray swans.
•[12:28] Final thoughts and conclusion.

About Our Guests:
Jeffrey Shrader
Associate Professor of International and Public Affairs
Columbia University

Ignacio Lopez-Gomez
Research Scientist
Google

Xiaofeng Li
Research Scientist
Institute of Oceanology, Chinese Academy of Sciences

Hui Su
Chair Professor
Hong Kong University of Science and Technology

Pedram Hassanzadeh
Associate Professor
University of Chicago

Qiang Sun
Research Scientist
University of Chicago

View related content here:
https://www.pnas.org/doi/full/10.1073/pnas.2523372123
https://www.pnas.org/doi/full/10.1073/pnas.2420288122
https://www.pnas.org/doi/full/10.1073/pnas.2415501122
https://www.pnas.org/doi/full/10.1073/pnas.2517520122
https://www.pnas.org/doi/full/10.1073/pnas.2420914122

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