US-based researchers have trained a deep-learning model capable of learning complex physical systems to provide accurate extreme weather predictions across the globe a full five days in advance.
Researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and Caltech worked with tech developer Nvidia to train the Fourier Neural Operator (FNO) deep-learning model to emulate atmospheric dynamics and provide high-fidelity extreme weather predictions.
The researchers used decades of data from ERA5 – the European Center for Medium-range Weather Forecasts’ (ECMWF) high-resolution Earth data set – to train the FNO model, which was scaled up to 128 Nvidia A100 GPUs on Perlmutter, the new HPC system at the National Energy Research Scientific Computing Center (NERSC).
The team developed a global FNO weather forecasting model at 30km resolution. The model predicts wind velocities and pressures at multiple levels in the atmosphere up to 120 hours in advance with high fidelity. In a case study on Hurricane Matthew from 2016, the model’s predictions of the hurricane’s winds and track were within the uncertainties of the NOAA National Hurricane Center’s forecast cones. In addition, the model can predict the behavior of certain classes of extreme weather events across the globe days in advance in just 0.25 seconds on a single Nvidia GPU.
Physics-informed deep-learning models such as the FNO offer the potential for accurate predictions of the spatio-temporal evolution of the Earth system orders of magnitude faster than traditional numerical models.
The research is an ongoing effort, and the team is investigating the comparative accuracy of deep learning and traditional numerical weather models in collaboration with experts in atmospheric modeling and numerical weather prediction.
The FNO model developed through the Berkeley Lab/Caltech/Nvidia collaboration is a significant step toward building a digital twin Earth, the researchers noted. Digital twin Earths are digital replicas of planet Earth – simulators grounded in physics, driven by AI, and constrained by real-time data. As described in the ambitious 10-year EU project Destination Earth, a digital twin Earth will give both expert and non-expert users tailored access to high-quality information, services, models, forecasts and visualizations in the realms of climate monitoring, modeling, mitigation and adaptation.