Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate scientists the ability to use machine learning to identify extreme weather events in huge climate simulation datasets. Predictive accuracies ranging from 89.4% to as high as 99.1% show that trained deep learning neural networks (DNNs) can identify weather fronts, tropical cyclones, and long narrow air flows that transport water vapor from the tropics called atmospheric rivers. As with image recognition, Michael Wehner (senior staff scientist, LBNL) noted they found the machine learning output outperforms humans. The strong relationship between ground truth and the neural network prediction can be seen in the classification plus regression results reported by Wehner at the recent Intel Developer Conference in Denver, Colorado. When explaining the importance of this work, Wehner believes that the big impact lies in assessing the impact of climate change as exemplified by the recent painful experiences of hurricanes Harvey (tied with hurricane Katrina as the costliest tropical cyclone on record), Irma (the strongest storm on record to exist in the open Atlantic region), and Maria (regarded as the worst natural disaster on record in Dominica and Puerto Rico).