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ML for Medium-Range Weather Forecasting

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In this week’s reading group, we will discuss recent advances in medium-range weather prediction and the key challenges that remain. We will start with an overview of the medium-range weather modeling pipeline and the types of data available for training large-scale AI systems. Next, we will explore some of the critical considerations in designing such systems, including: The trade-offs between deterministic and probabilistic approaches, Strategies for integrating observational data into the modeling pipeline, and The potential benefits of leveraging vast datasets governed by similar dynamical principles to train a foundation model for the atmosphere, rather than a weather-specific model. We will conclude by highlighting some of the most exciting directions for future research.

This talk is part of the Machine Learning Reading Group @ CUED series.

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