Lightning is one of the most destructive forces of nature, as in 2020 when it sparked the massive California Lightning Complex fires, but it remains hard to predict. A new study led by the University of Washington shows that machine learning — computer algorithms that improve themselves without direct programming by humans — can be used to improve lightning forecasts. 

Better lightning forecasts could help to prepare for potential wildfires, improve safety warnings for lightning and create more accurate long-range climate models.

“The best subjects for machine learning are things that we don’t fully understand. And what is something in the atmospheric sciences field that remains poorly understood? Lightning,” said Daehyun Kim, a UW associate professor of atmospheric sciences. “To our knowledge, our work is the first to demonstrate that machine learning algorithms can work for lightning.”

recasts with a machine learning equation based on analyses of past lightning events. The hybrid method, presented Dec. 13 at the American Geophysical Union’s fall meeting, can forecast lightning over the southeastern U.S. two days earlier than the leading existing technique.

“This demonstrates that forecasts of severe weather systems, such as thunderstorms, can be improved by using methods based on machine learning,” said Wei-Yi Cheng, who did the work for his UW doctorate in atmospheric sciences. “It encourages the exploration of machine learning methods for other types of severe weather forecasts, such as tornadoes or hailstorms.” 

Researchers trained the system with lightning data from 2010 to 2016, letting the supercomputer discover relationships between weather variables and lightning strokes. Then they tested the technique on weather from 2017 to 2019, comparing the AI-supported technique and an existing physics-based method, using actual lightning observations to evaluate both.

The new method was able to forecast lightning with the same skill about two days earlier than the leading technique in places, like the southeastern U.S., that get a lot of lightning. Because the method was trained on the entire U.S., its performance wasn’t as accurate for places where lightning is less common.

The approach used for comparison was a recently developed technique to forecast lightning based on the amount of precipitation and the ascent speed of storm clouds. That method has projected more lightning with climate change and a continued increase in lightning over the Arctic.

“The existing method just multiplies two variables. That comes from a human’s idea, it’s simple. But it’s not necessarily the best way to use these two variables to predict lightning,” Kim said. 

The machine learning was trained on lightning observations from the World Wide Lightning Location Network, a collaborative based at the UW that has tracked global lightning since 2008.

“Machine learning requires a lot of data — that’s one of the necessary conditions for a machine-learning algorithm to do some valuable things,” Kim said. “Five years ago, this would not have been possible because we did not have enough data, even from WWLLN.”

Commercial networks of instruments to monitor lightning now exist in the U.S., and newer geostationary satellites can monitor one area continuously from space, supplying the precise lightning data to make more machine learning possible.

“The key factors are the amount and the quality of the data, which are exactly what WWLLN can provide us,” Cheng said. “As machine learning techniques advance, having an accurate and reliable lightning observation dataset will be increasingly important.”

The researchers hope to improve their method using more data sources, more weather variables, and more sophisticated techniques. They would like to improve predictions of particular situations like dry lightning, or lightning without rainfall since these are especially dangerous for wildfires.

Researchers believe their method could also be applied to longer-range projections. Longer-range trends are important partly because lightning affects air chemistry, so predicting lightning leads to better climate models.

“In atmospheric sciences, as in other sciences, some people are still skeptical about the use of machine learning algorithms — because as scientists, we don’t trust something we don’t understand,” Kim said. “I was one of the skeptics, but after seeing the results in this and other studies, I am convinced.”

Spin waves, a change in electron spin that propagates through a material, could fundamentally change how devices store and carry information. These waves, also known as magnons, don’t scatter or couple with other particles. Under the right conditions, they can even act as a superfluid, moving through a material with zero energy loss. 

But the very properties that make them so powerful also make them nearly impossible to measure.  In a previous study, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) demonstrated the ability to both excite and detect spin waves in a two-dimensional graphene magnet, but they couldn’t measure any of the wave’s specific properties.

Now, SEAS researchers have demonstrated a new way to measure the quintessential properties of spin waves in graphene.

“In previous experiments, we only knew that we could generate spin waves, but we didn’t know anything about their properties in a quantitative way,” said Amir Yacoby, Professor of Physics and Applied Physics at SEAS and senior author of the paper. “With this new work, we can determine all these quantitative numbers, including the energy and number of spin waves, their chemical potential, and temperature. This is an extremely important tool that we can use to explore new ways of generating magnons and get closer to achieving spin superfluidity.”

Measuring the properties of a spin-wave is like measuring the properties of a tidal wave if the water itself was undetectable. If you couldn’t see water, how could you measure the speed, height, or the number of tidal waves? One way would be to introduce something into the system that you can measure, like a surfer. The speed of the tidal wave could be detected by measuring the speed of the surfer.

In this case, Yacoby and his team used an electron surfer.

The researchers began with a quantum Hall ferromagnet. Quantum Hall ferromagnets are magnets made from 2D materials, in this case, graphene, where all the electron spins are in the same direction.  If an electron with a different spin is introduced into this system, it will use energy to try to flip the spins of its neighbors.

But the research team found that when they injected an electron with a different spin into the system and then generated spin waves, the energy the electron needed to flip its neighbors went down.

“It’s striking that somehow the electrons that we’re putting into the system are sensitive to the presence of spin waves,” said Andrew T. Pierce, a graduate student at SEAS and co-first author of the study. “It’s almost as if these electrons are grabbing onto the wave and using it to help flip the spins of their neighbors.”

“Spin waves don’t like to interact with anything but by using electrons and this energy cost as a proxy to probe the properties of a spin waves, we can determine the chemical potential, which combined with knowing the temperature and a few other properties, gives us a full description of the magnon,” said Yonglong Xie, a postdoctoral fellow at SEAS and co-first author of the study. “This is critical to knowing whether the wave is approaching the limit where it achieves superfluidity.”

The research could also provide a general approach to studying other hard-to-measure exotic systems, such as the recently discovered moiré materials which are expected to support a variety of waves like the spin-wave studied in this work.