Enhancing Climate Prediction with Knowledge-Infused Deep Learning Models

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• Simone Monaco, Politecnico if Torino
• Thursday 26 September 2024, 17:00-18:00
• Lecture Theatre 2, Computer Laboratory, William Gates Building.

If you have a question about this talk, please contact Pietro Lio.

Modern deep learning models require large amounts of data and computational resources, often overlooking valuable domain knowledge that could enhance their accuracy and efficiency. In this talk, we present an innovative approach that integrates climate science knowledge into deep learning models to boost performance and uncover meaningful relationships between inputs and outputs. Specifically, we applied a cutting-edge GraphCast-like architecture for long-term climate prediction, enhanced by an attention mechanism that accounts for region-specific climate dynamics, such as the influence of the El Niño southern oscillator. This enables the model to capture non-local interactions with greater expressivity. The resulting hybrid model significantly improves prediction accuracy and highlights key teleconnection patterns, offering deeper insights into how oscillators influence global climate states. This method presents a promising avenue for developing more interpretable and precise climate models.

This talk is part of the Foundation AI series.

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• Lecture Theatre 2, Computer Laboratory, William Gates Building

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