Last Updated: January 6, 2026
This is our holding place for project descriptions while we get them all identified. *These are draft descriptions.* Keep in mind that research projects can change quite a bit. That is part of the nature of research. You don't know how things will work out when no one has done your project before.
We put a link to recent REU projects for that mentor, when possible. That doesn't mean the proposed project is like the others, but will give you a flavor of that mentor's research. Likewise, some projects have "most directly applicable majors" listed.
Students applying to the program can mention any particular projects of interest in their essays if they wish, but that is not required and we may not have all projects identified during the application period.
Students who are selected to the program will be asked to rank the projects. This does not necessarily limit who can choose the project, but instead is meant to reveal something of the nature of the project.
The following projects have been proposed.
1. The Future of Extreme Events
Mentor: Emma Kuster, SC-CASC and Jason Furtado, School of Meteorology
Description: The South Central Climate Adaptation Science Center (CASC) has produced a suite of projections to help us explore how extreme events may change over time at a regional scale. Some resource managers have observed changes in the frequency or intensity of precipitation events and want to know how to prepare and adapt to these changes. Compound events, meaning extreme events that occur simultaneously or sequentially, are of growing concern to many resource management communities. The combination of extreme events (e.g., drought/flood; extreme heat/poor air quality; heatwave/drought) often results in greater negative impacts than a single extreme event. Knowing how these events have changed and are expected to change can help inform adaptation planning efforts. Using the South Central CASC downscaled projections, this project will build upon existing work to explore the future of compound extreme events and identify how the resource management community may be impacted. By the end of this proposed REU project, the student will have gained an understanding of the uncertainties associated with long-term weather projections, experience with computing statistics and generating graphics, and identified relevant concerns of resource managers regarding compound extreme events.
Desired skills:
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Recent REU projects with mentors on this team:
- Assessing Climate Change and Whiplash Events in National Parks
- Preparing to adapt: Are public expectations in line with climate projections?
2. Coming
Mentors: Harold Brooks, National Severe Storms Laboratory
Description: Coming
Desired skills:
Most applicable majors:
Recent REU projects with mentors on this team:
- Week 2 Severe Weather Forecasts: What Should We Predict?
- Characteristics and Statistics of Tropical Cyclone Tornadoes and Warning Skill
- Comparing Road and Bridge Surface Temperatures During the Winter Season in Ohio
- An Investigation into the Characteristics of Lightning as it Interacts with Towers
- Do Forecasters Add Value to Machine Learning Algorithms of Cloud-to-Ground Lightning?
- A Comparison of Lightning Flashes Observed by Ground-Based Detection Networks and Video
- Impact of Earth Networks Lightning Data and Dangerous Thunderstorm Alerts on Forecasters' Warning Decision and Confidence
- Verification of Earth Network's Dangerous Thunderstorm Alerts and National Weather Service Warnings
- Evaluation of a Lightning Jump Algorithm with High Resolution Storm Reports
3. Exploring the Connections Between Meteorological Conditions and Population Health Over the United States
Mentors: Xiaodong Chen, Department of Geography and Environmental Sustainability and Civil Engineering
Description: One major controlling factor of our community’s health conditions is the atmospheric environment. Temperature, radiation, and aerosols are a few examples that are responsible for heatwaves, air pollution, and other hazardous weather. Previous studies (from both statistical and biological perspectives) reveal their close connection to population health through case studies or analysis over a limited region. However, their spatial variability at fine scales (i.e., how the heatwave impacts differ across different states/counties) remains less well understood. Fortunately, now we have reasonably good information on these meteorological conditions as well as health data at a much finer resolution. By analyzing these meteorological, population, economic, and population health data, we will explore the impact of different environmental trends and regional health trends, using a range of tools from simple linear regression to advanced machine learning models.
Desired skills:
Most applicable majors:
Recent REU projects with mentors on this team:
4. Analysis of Hailstone Fall Characteristics Using a High-Speed Hail Camera System
Mentors: Dr. Sean Waugh (NSSL), Jacob Segall (CIWRO)
Description: The way hailstones move through the atmosphere is not well understood, and there are large uncertainties in our understanding of the fall behavior of hailstones. These uncertainties reduce the accuracy of radar forward operators and microphysical parameterizations used in weather models as well as radar algorithms used by operational weather agencies. For decades, most hail research—especially that involving large or giant hail—has relied on collecting stones after impact. However, once hailstones hit the ground, they often melt or break apart, altering their true physical properties. In addition, they generally provide no information about the orientation(s) of the hailstones prior to impact with the ground, nor can use fallen hailstones to learn about how liquid water is distributed and/or sheds from the hail as it melts. These limitations mean that post-impact observations provide little insight into how hailstones actually fall and only represent the stones that survive long enough to be collected. As a result of a dearth of observations of real hailstones in natural freefall, forecasting tools and hail identification methods (to include size and quantity estimates) are based on incomplete data, reducing their accuracy and increasing uncertainty.
To address these challenges, NOAA’s National Severe Storms Laboratory has developed a new, one-of-a-kind observation system called the HailCam. This truck-based platform captures images of hailstones in freefall before they reach the ground, enabling direct observations of their size, shape, orientation, fall behavior, meltwater content, and overall size distributions. The system became fully operational during the 2025 spring field season and is providing groundbreaking data on hail behavior.
This research project will focus on analyzing data collected with the Hail Camera to identify unique fall characteristics of individual hailstones and determine bulk properties across entire storm events. Students will have the opportunity to:
- Participate in field deployments (May–June) to help collect data during severe weather events.
- Select and analyze either their own field-collected case or an existing dataset.
- Process high-resolution hail images to identify stones, measure their physical properties, and calculate summary statistics at both the individual and event level.
Through this project, students will gain hands-on experience with severe storms fieldwork, learn how specialized research instruments are developed and tested, and see how their findings can directly improve weather forecasts, severe storm warnings, and decision-making in the operational meteorology community.
Required skills:
- Computer Programming: python, Matlab, C/C++
- Data analysis and statistics: ability to calculate summary statistics and analyze distributions and/or variability across storm events
- Data management: Comfort working with large datasets and organizing files
- Basic meteorology knowledge and understanding of severe storms
- Independent research ability: capacity to formulate questions and devise solutions with some guidance
- Scientific communication skills: ability to summarize results of scientific data
- Teamwork/collaboration: Willingness to work with researchers, field teams, and other students
- Basic technical writing skills: ability to document findings and contribute to reports/publications
Desired skills:
- Image Processing: OpenCV, ImageJ, etc (not required but nice)
- Interest in (or experience with) field work: willingness to participate in mobile field operations (not required)
Most applicable majors: Meteorology, Environmental Science, Engineering, Physics, Computer/Data Science
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