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Artificial Intelligence Gives Advance Ozone Layer Warning

Ozone Layer in the earth’s troposphere can be predicted with accuracy up to two weeks in advance, a development over current systems that can predict ozone levels only three days ahead. The new artificial intelligence system developed in the University of Houston’s Air Quality Forecasting and Modeling Lab could lead to improved ways to control high ozone problems and even contribute to solutions for climate change issues.The findings are published online in the scientific journal, Scientific Reports-Nature.

Yunsoo Choi, professor of atmospheric chemistry and AI deep learning at UH’s College of Natural Sciences and Mathematics said that this was very challenging. They believe we are the first to try to forecast surface ozone levels two weeks in advance.Ozone, a colorless gas, is helpful in the right place and amount. As a part of the earth’s stratosphere 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.

Artificial Intelligence Gives Advance Ozone Layer WarningAlqamah Sayeed, a researcher in Choi’s lab and the first author of the research paper and Doctoral student said that Ozone Layer is a secondary pollutant, and it can affect humans in a bad way. 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 Layer have become a 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.

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