The future of extreme weather prediction may lie in modernizing a piece of technology from the past. Researchers recently developed a new technique to augment an old-fashioned weather forecasting method with the power of deep learning, a subset of artificial intelligence (AI). Once the deep learning system is fully trained, it is able to predict extreme weather events like heat waves and cold spells with 80% accuracy up to 5 days beforehand. "This is a very inexpensive way of predicting extreme events at least a few days ahead of time," said Ashesh Chattopadhyay, a mechanical engineering graduate student at Rice University in Houston and lead author on the project. The project began when Pedram Hassanzadeh, an assistant professor of mechanical engineering at Rice, realized that extreme weather events like heat waves and cold spells usually arise from very unusual atmospheric circulation patterns that could potentially be taught to a pattern recognition computer program.

The future of extreme weather prediction may lie in modernizing a piece of technology from the past. Researchers recently developed a new technique to augment an old-fashioned weather forecasting method with the power of deep learning, a subset of artificial intelligence (AI). Once the deep learning system is fully trained, it is able to predict extreme weather events like heat waves and cold spells with 80% accuracy up to 5 days beforehand. "This is a very inexpensive way of predicting extreme events at least a few days ahead of time," said Ashesh Chattopadhyay, a mechanical engineering graduate student at Rice University in Houston and lead author on the project. The project began when Pedram Hassanzadeh, an assistant professor of mechanical engineering at Rice, realized that extreme weather events like heat waves and cold spells usually arise from very unusual atmospheric circulation patterns that could potentially be taught to a pattern recognition computer program.

Talk about the weather, generally considered a neutral topic for conversations, is about to get extremely interesting. January 2020 was the Earth's hottest January in the past 141 years of climate records according to scientists at NOAA's National Centers for Environmental Information. Globally, extreme weather and climate disasters pose a threat to public health, economic well-being, and geopolitical stability. Economically, the U.S. has incurred $1.75 trillion in losses since 1980 due to 258 weather and climate disasters according to NOAA figures. Predicting extreme weather is a complex science, and an area where artificial intelligence (AI) machine learning, specifically the pattern-recognition capabilities of deep learning, can make a difference in forecasting accuracy.

Thanks to advances in machine learning over the last two decades, it's no longer in question whether humans can beat computers at games like chess; we'd have about as much chance winning a bench-press contest against a forklift. But ask the current computer champion, Google's AlphaZero, for advice on chess theory, like whether a bishop or a knight is more valuable in the Ruy Lopez opening, and all you'll get is a blank stare from a blinking cursor. Theory is a human construct the algorithm has no need for. The computer knows only how to find the best move in any given position because it's trained extensively--very extensively--by practicing against itself and learning what works. Even with a lead time of 18 months, the neural network was able to see El Niño events coming.