Jia Xing, a research associate professor in the Department of Civil and Environmental Engineering at the University of Tennessee, Knoxville, is leveraging nearly two decades of expertise in atmospheric chemistry and physics modeling to enhance weather and air quality forecasting. His work focuses on understanding the sources and distribution of atmospheric chemicals and their implications for human health, ecosystems, and weather patterns.
Xing has developed a machine-learning-based reduced-form atmospheric chemistry model known as DeepRSM, recognized and promoted by the US Environmental Protection Agency for its global applications in air quality forecasting and management. His latest research aims to harness artificial intelligence and geoscience to create a new generation of forecasting tools that surpass current numerical model-based systems in speed, accuracy, and responsiveness.
Backed by funding from the National Science Foundation (NSF), Xing is now working on a machine learning-based surrogate for chemical transport models (CTMs), which are commonly used in atmospheric research but are often too resource-intensive for real-time forecasting. Furthermore, he leads a National Oceanic and Atmospheric Administration (NOAA) grant to integrate DeepCTM with NOAA’s operational National Air Quality Forecast Capability (NAQFC). This integration aims to improve the forecasting of atmospheric chemical concentrations, enhancing the efficiency, accuracy, and resolution of air quality predictions on both national and global scales.
“My advantage is that I not only know the weather, but I also know the chemistry,” Xing stated, emphasizing his background in studying air pollution in China. He added, “I know the aerosol and also some gases they might influence. They have given feedback from the weather system. We’re trying to implement the chemistry with the weather to improve the performance.”
The AI model proposed for the NSF project will be trained on a variety of geoscientific big data, which includes meteorological inputs, satellite remote sensing, and ground-based pollutant measurements. This training will enable the model to replicate how CTMs evolve over time and space, facilitating real-time forecasting of air pollution and weather patterns at a high resolution across extensive regions.
Xing has already conducted preliminary studies with assistance from the AI Tennessee program, discovering significant potential in his new model. “Unlike most existing AI models, this framework explicitly incorporates meteorology-chemistry interactions, such as the feedback between air pollutants and weather conditions,” he explained. “This allows the model to provide more realistic and scientifically grounded forecasts.”
The ability to predict weather accurately is not merely a matter of convenience; it has crucial implications for public health and emergency response systems, especially in the face of natural disasters like wildfires, heat waves, and floods. “It is very important for human health and emergency response systems,” Xing stressed. He noted that traditional modeling methods often suffer from error propagation issues, making long-term forecasting challenging. “AI has potential to help us better predict things,” he said.
For the NOAA study, Xing is collaborating with Youhua Tang, a senior researcher at George Mason University. While the project’s immediate benefits will directly support NOAA and air quality forecasters, it is also expected to yield a series of scientific publications aimed at enriching the broader scientific community’s understanding of atmospheric chemistry forecasting worldwide.
“We definitely need other people to help, because it’s a very big problem,” Xing remarked. He emphasized the complexity of the atmospheric system and reiterated his focus on connecting chemistry with weather. “I really hope to collaborate with other professors in the future to expand the knowledge.”
Since embarking on his journey to integrate AI into air quality and weather prediction, Xing has been driven by a singular vision. “I really hope it can improve our lives and help more people. That’s my dream,” he affirmed. He acknowledged the ongoing challenges posed by climate change but expressed optimism about the potential to protect human health and property while advancing scientific knowledge.
Provided by University of Tennessee at Knoxville
Citation: Xing’s Research Uses AI to Improve Weather, Air Quality Forecasting (2026, January 30) retrieved 30 January 2026 from https://sciencex.com/wire-news/531227393/xings-research-uses-ai-to-improve-weather-air-quality-forecastin.html
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