Rice University engineers have created a Deep learning computer system that taught itself to accurately predict extreme weather events, like heat waves, up to five days in advance using minimal information about current weather conditions.
Ironically, Rice's self-learning "capsule neural network" uses an analog method of weather forecasting that computers made obsolete in the 1950s. During training, it examines hundreds of pairs of maps. Each map shows surface temperatures and air pressures at five-kilometers height, and each pair shows those conditions several days apart. The training includes scenarios that produced extreme weather – extended hot and cold spells that can lead to deadly heat waves and winter storms. Once trained, the system was able to examine maps it had not previously seen and make five-day forecasts of extreme weather with 85% accuracy.
With further development, the system could serve as an early warning system for weather forecasters, and as a tool for learning more about the atmospheric conditions that lead to extreme weather, said Rice's Pedram Hassanzadeh, co-author of the study. The accuracy of day-to-day weather forecasts has improved steadily since the advent of computer-based numerical weather prediction (NWP) in the 1950s.
But even with improved numerical models of the atmosphere and more powerful computers, NWP cannot reliably predict extreme events like the deadly heat waves in France in 2003 and in Russia in 2010. "It may be that we need faster supercomputers to solve the governing equations of the numerical weather prediction models at higher resolutions," said Hassanzadeh, an assistant professor of mechanical engineering and of Earth, environmental and planetary sciences at Rice. "But because we don't fully understand the physics and precursor conditions of extreme-causing weather patterns, it's also possible that the equations aren't fully accurate, and they won't produce better forecasts, no matter how much computing power we put in." In late 2017, Hassanzadeh and study co-authors and graduate students Ashesh Chattopadhyay and Ebrahim Nabizadeh decided to take a different approach.
"When you get these heat waves or cold spells, if you look at the weather map, you are often going to see some weird behavior in the jet stream, abnormal things like large waves or a big high-pressure system that is not moving at all," Hassanzadeh said. "It seemed like this was a pattern recognition problem. So we decided to try to reformulate extreme weather forecasting as a pattern-recognition problem rather than a numerical problem." Deep learning is a form of artificial intelligence, in which computers are "trained" to make humanlike decisions without being explicitly programmed for them.
The mainstay of deep learning, the convolutional neural network, excels at pattern recognition and is the key technology for self-driving cars, facial recognition, speech transcription and dozens of other advances. "We decided to train our model by showing it a lot of pressure patterns in the five kilometers above the Earth, and telling it, for each one, 'This one didn't cause extreme weather. This one caused a heat wave in California. This one didn't cause anything. This one caused a cold spell in the Northeast,'" Hassanzadeh said. "Not anything specific like Houston versus Dallas, but more of a sense of the regional area.
