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| Name | [SPCL_Bcast] Building Digital Twins of the Earth for NVIDIA's Earth-2 Initiative |
| Video Uploader | Video From Scalable Parallel Computing Lab, SPCL @ ETH Zurich |
| Upload Date | This Video Uploaded At 08-04-2022 14:53:42 |
| Video Discription | Speaker: Karthik Kashinath Venue: SPCL_Bcast, recorded on 7 April, 2022 Abstract: NVIDIA is committed to helping address climate change. Recently our CEO announced the Earth-2 initiative, which aims to build digital twins of the Earth and a dedicated supercomputer, E-2, to power them. Two central goals of this initiative are to predict the disastrous impacts of climate change well in advance and to help develop strategies to mitigate and adapt to change. Here we present our work on an AI weather forecast surrogate trained on ECMWF's ERA5 reanalysis dataset. The model, called FourCastNet, employs a patch-based Vision-Transformer with a Fourier Neural Operator mixer. FourCastNet produces short to medium range weather predictions of about two dozen physical fields at 25-km resolution that exceed the quality of all related deep learning-based techniques to date. FourCastNet is capable of accurately forecasting fast timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor with important implications for wind energy resource planning, predicting extreme weather events such as tropical cyclones and atmospheric rivers, as well as extreme precipitation. We compare the forecast skill of FourCastNet with archived operational IFS model forecasts and find that the forecast skill of our purely data-driven model is remarkably close to that of the IFS model for forecast lead times of up to 8 days. Furthermore, it can produce a 10-day forecast in a fraction of a second on a single GPU. The enormous speed and high accuracy of FourCastNet provides at least three major advantages over traditional forecasts: (i) real-time user interactivity and analysis; (ii) the potential for large forecast ensembles; and (iii) the ability to combine fast surrogates to form new coupled systems. Large ensembles can capture rare but highly impactful extreme weather events and better quantify the uncertainty of such events by providing more accurate statistics. By plugging AI surrogates into Omniverse, users can generate, visualize, and explore potential weather outcomes interactively. See https://spcl.inf.ethz.ch/Bcast/ for more talks. |
| Category | Science & Technology |
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