Neural Operators for Weather Forecasting and Climate-Change Mitigation

Go back to programme

Neural Operators for Weather Forecasting and Climate-Change Mitigation

  • Register

    * Register (or log in) to the AI4G Neural Network to add this session to your agenda or watch the replay

  • Neural Operators for Weather Forecasting and Climate-Change Mitigation

    Predicting extreme weather events in a warming world at fine scales is a grand challenge facing climate scientists. We depend on reliable predictions to plan for the disastrous impact of climate change and develop effective adaptation strategies. Deep learning (DL) offers novel methods that are potentially more accurate and orders of magnitude faster than traditional weather and climate models for predicting extreme events. The Fourier Neural Operator (FNO), a novel deep-learning method, has shown promising results for predicting complex systems, such as spatiotemporal chaos, turbulence, and weather phenomena. Our results show promise for large-scale DL potentially competing with state-of-the-art numerical weather prediction. In addition, we use FNO to model the multiphase flow systems used in Carbon Capture and Storage (CCS) systems, and we can provide highly accurate gas saturation and pressure buildup predictions under diverse reservoir conditions, geological heterogeneity, and injection schemes. The predictions are 700,000 times faster than traditional numerical simulations, with spatial resolutions exceeding most typical models run with existing simulators. The work presented here is a significant step toward building a reliable, high-fidelity, high-resolution digital twin of Earth for weather modeling and climate change mitigation. 

    Physics to machine learning and machine learning back to physics

    Over the last couple of years, we have witnessed an explosion in the use of machine learning for Earth system science application ranging from Earth monitoring to modeling. Machine learning has shown tremendous success in emulating complex physics such as atmospheric convection or terrestrial carbon and water fluxes using satellite or high-fidelity simulations in a supervised framework. However, machine learning, especially deep learning, is opaque (the so-called black box issue) and thus a question remains: what (new) understanding have we really developed?  

    The talk will illustrate the value of machine learning for specific examples and some of the needed advances in machine learning to push climate science forward. 

    This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

    Share this session
    In partnership with