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FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator
Jaideep Pathak · Shashank Subramanian · Peter Harrington · Thorsten Kurth · Andre Graubner · Morteza Mardani · David Hall · Karthik Kashinath · Anima Anandkumar

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in Tackling Climate Change with Machine Learning »
Event URL: https://www.climatechange.ai/papers/neurips2022/115 »

Accurate, reliable, and efficient means of forecasting global weather patterns are of paramount importance to our ability to mitigate and adapt to climate change. Currently, real-time weather forecasting requires repeated numerical simulation and data assimilation cycles on dedicated supercomputers, which restricts the ability to make reliable, high-resolution forecasts to a handful of organizations. However, recent advances in deep learning, specifically the FourCastNet model, have shown that data-driven approaches can forecast important atmospheric variables with excellent skill and comparable accuracy to standard numerical methods, but at orders-of-magnitude lower computational and energy cost during inference, enabling larger ensembles for better probabilistic forecasts. In this tutorial, we demonstrate various applications of FourCastNet for high-resolution global weather forecasting, with examples including real-time forecasts, uncertainty quantification for extreme events, and adaptation to specific variables or localized regions of interest. The tutorial will provide examples that will demonstrate the general workflow for formatting and working with global atmospheric data, running autoregressive inference to obtain daily global forecasts, saving/visualizing model predictions of atmospheric events such as hurricanes and atmospheric rivers, and computing quantitative evaluation metrics for weather models. The exercises will primarily use PyTorch and do not require detailed understanding of the climate and weather system. With this tutorial, we hope to equip attendees with basic knowledge about building deep learning-based weather model surrogates and obtaining forecasts of crucial atmospheric variables using these models.

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Author Information

Jaideep Pathak (NVIDIA Corporation)
Shashank Subramanian (Lawrence Berkeley National Laboratory)
Peter Harrington (Lawrence Berkeley National Laboratory)
Thorsten Kurth (Nvidia)
Andre Graubner (Tsinghua University)
Morteza Mardani (Nvidia)
David Hall (NVIDIA)
Karthik Kashinath (LBNL)
Anima Anandkumar (NVIDIA/Caltech)

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