Predicting Extreme Weather Risk Three Weeks in Advance With FourCastNet
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136 дней – 11 3113:12Опубликовано 3 июля 2023, 12:53
A small 50-member ensemble takes one hour to generate on a large supercomputer using traditional numerical simulations. But small ensembles often miss low-probability, high-impact events.
By running FourCastNet—an AI framework developed by researchers at NVIDIA, Caltech, and Lawrence Berkeley Lab—in NVIDIA Modulus, and leveraging FNO, we were able to generate 21-day global weather trajectories of 1,000 ensemble members in a tenth of the time it previously took to do a single trajectory, while using 1,000X less energy.
In this demo, we look at how a 1,000-member ensemble was able to predict the increased risk of an extreme 2018 heat wave in North Africa that broke all records.
arxiv.org/abs/2306.03838
arxiv.org/abs/2208.05419
arxiv.org/abs/2202.11214
#FourCastNet, #Physics-ML, #The-Berlin-Summit-For-EVE
By running FourCastNet—an AI framework developed by researchers at NVIDIA, Caltech, and Lawrence Berkeley Lab—in NVIDIA Modulus, and leveraging FNO, we were able to generate 21-day global weather trajectories of 1,000 ensemble members in a tenth of the time it previously took to do a single trajectory, while using 1,000X less energy.
In this demo, we look at how a 1,000-member ensemble was able to predict the increased risk of an extreme 2018 heat wave in North Africa that broke all records.
arxiv.org/abs/2306.03838
arxiv.org/abs/2208.05419
arxiv.org/abs/2202.11214
#FourCastNet, #Physics-ML, #The-Berlin-Summit-For-EVE
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