The vast improvement in forecasting is only one part of the story of this new research. The other is how the team made it happen. Conventional forecasting uses a numerical model, which means the research is based on equations for the movement of gasses and fluids in the atmosphere.

The limitations were obvious to Choi and his team. The numerical process is slow, making results expensive to obtain, and accuracy is limited. “Accuracy with the numerical model starts to drop after the first three days,” Choi said.

“This was very challenging. Nobody had done this previously. I believe we are the first to try to forecast surface ozone levels two weeks in advance,” said Yunsoo Choi, professor of atmospheric chemistry and AI deep learning at UH’s College of Natural Sciences and Mathematics. The findings are published online in the scientific journal, Scientific Reports–Nature.

Applying deep learning to air quality and weather forecasting is like searching for the holy grail, just like in the movies,” said Choi, who is a big fan of action plots. “In the lab, we went through some difficult times for a few years. There is a process. Finally, we’ve grasped the holy grail. This system works. The AI model ‘understands’ how to forecast. Despite the years of work, it somehow still feels like a surprise to me, even today.”