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. Ozone, a colorless gas, is helpful in the right place and amount. As a part of the earth's stratosphere ("the ozone layer"), it protects by filtering out UV radiation from the sun. But when there are high concentrations of ozone near earth's surface, it is toxic to lungs and hearts. "Ozone is a secondary pollutant, and it can affect humans in a bad way," explained doctoral student Alqamah Sayeed, a researcher in Choi's lab and the first author of the research paper. Exposure can lead to throat irritation, trouble breathing, asthma, even respiratory damage. Some people are especially susceptible, including the very young, the elderly and the chronically ill. Ozone levels have become a frequent part of daily weather reports. But unlike weather forecasts, which can be reasonably accurate up to 14 days ahead, ozone levels have been predicted only two or three days in advance -- until this breakthrough. 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. The research team used a unique loss function in developing the machine learning algorithm. A loss function helps in optimization of the AI model by mapping decision to their associated costs. In this project, researchers used index of agreement, known as IOA, as the loss function for the AI model over conventional loss functions. IOA is a mathematical comparison of gaps between what is expected and how things actually turn out. In other words, team members added historical ozone data to the trials as they gradually refined the program's reactions. The combination of the numerical model and the IOA as the loss function eventually enabled the AI algorithm to accurately predict outcomes of real-life ozone conditions by recognizing what happened before in similar situations. It is much like how human memory is built. "Think about a young boy who sees a cup of hot tea on a table and tries to touch it out of curiosity.

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 …