University of Kentucky Receives Grant for Advanced Weather Forecasting

Lexington, Kentucky, November 22, 2025

Researchers at the University of Kentucky’s Karen and Stanley Pigman College of Engineering have been awarded a $2 million National Science Foundation grant to develop an advanced neighborhood-scale weather forecasting system. The five-year project, titled LEAP-HI, aims to provide detailed weather insights to aid decision-makers and emergency responders. By utilizing unmanned aircraft systems and high-resolution predictive models, the initiative seeks to address gaps in current weather forecasting, offering local data on diverse environmental impacts such as urban infrastructure and crop cycles.

Lexington, Kentucky – Researchers at the University of Kentucky’s Karen and Stanley Pigman College of Engineering have secured a $2 million grant from the National Science Foundation (NSF) to develop an advanced weather forecasting system in collaboration with the NSF’s National Center for Atmospheric Research (NCAR). The five-year project, titled “LEAP-HI: Advancing Precision Neighborhood Scale Weather Forecasting with Autonomous Aircraft Systems and Adaptive Microscale Models,” aims to provide decision-makers and emergency responders with detailed, neighborhood-level weather insights.

The initiative addresses a critical gap in current weather models, which often lack the resolution to capture environmental features influencing local conditions. By examining how urban infrastructure, tree cover, roadway systems, and crop cycles affect hyperlocal phenomena such as heat, wind, and air quality extremes, the project aims to refine weather forecasts with unprecedented precision.

Central to the research is the development of a novel system that combines unmanned aircraft systems (UAS) with a high-resolution numerical weather prediction model. Fleets of small, autonomous aircraft will collect fine-scale atmospheric data, which will be used to improve weather forecasts, providing accurate depictions of neighborhood-scale weather extremes. Machine learning techniques, combined with microscale weather models, will produce more detailed forecasts for real-time decision-making, while the system learns from past performance relative to UAS observations to correct errors and further improve accuracy.

The project is led by Dr. Sean Bailey, with Drs. Jesse Hoagg and Alexandre Martin serving as co-principal investigators. Dr. James Pinto, a senior scientist at NCAR’s Research Applications Laboratory, also contributes as a co-principal investigator.

This initiative is part of a broader effort to enhance localized weather forecasting capabilities. For instance, a recent study by the University of Reading demonstrated that machine learning methods improved the prediction of urban heatwaves by up to 11% compared to standard weather forecasts. By integrating citizen observations and land-use data, the study achieved temperature forecasts at a resolution of 100 meters, highlighting the potential for hyper-local predictions. Similarly, the University of Sheffield, in collaboration with the Universities of Lincoln and Reading, developed the NARMAX model, an AI and machine learning method that has shown a high degree of predictive accuracy in forecasting seasonal weather conditions over Northwest Europe. These advancements underscore a growing commitment to leveraging technology and interdisciplinary research to enhance the precision and applicability of weather forecasts, particularly at the neighborhood level, to better inform decision-making and disaster preparedness.

Frequently Asked Questions

What is the LEAP-HI project?

The LEAP-HI project, titled “Advancing Precision Neighborhood Scale Weather Forecasting with Autonomous Aircraft Systems and Adaptive Microscale Models,” is a five-year initiative funded by the National Science Foundation. It aims to develop an advanced weather forecasting system that provides detailed, neighborhood-level weather insights to assist decision-makers and emergency responders in preparing for and responding to extreme weather events.

How does the LEAP-HI project plan to improve weather forecasting?

The project plans to improve weather forecasting by integrating autonomous unmanned aircraft systems (UAS) with high-resolution numerical weather prediction models. Fleets of small, autonomous aircraft will collect fine-scale atmospheric data, which will be used to refine weather forecasts, providing accurate depictions of neighborhood-scale weather extremes. Machine learning techniques, combined with microscale weather models, will produce more detailed forecasts for real-time decision-making, while the system learns from past performance relative to UAS observations to correct errors and further improve accuracy.

Who are the principal investigators of the LEAP-HI project?

The LEAP-HI project is led by Dr. Sean Bailey, with Drs. Jesse Hoagg and Alexandre Martin serving as co-principal investigators. Dr. James Pinto, a senior scientist at NCAR’s Research Applications Laboratory, also contributes as a co-principal investigator.

What other initiatives are working on improving localized weather forecasting?

Other initiatives include a study by the University of Reading, which demonstrated that machine learning methods improved the prediction of urban heatwaves by up to 11% compared to standard weather forecasts. Additionally, the University of Sheffield, in collaboration with the Universities of Lincoln and Reading, developed the NARMAX model, an AI and machine learning method that has shown a high degree of predictive accuracy in forecasting seasonal weather conditions over Northwest Europe.

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