Last Updated: January 22, 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, (OU/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:
Most applicable majors:
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. Forecast Evaluation
Mentors: Harold Brooks (NSSL) and Michael Baldwin (CIWRO)
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, (OU/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: Sean Waugh (NSSL) and 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
Recent REU projects with mentors on this team:
5. Statewide Wind Speed Reduction
Mentors: Brad Illston (OK Mesonet, OU/School of Meteorology)
Description: A new study led by University of Illinois Urbana-Champaign researchers (has found that average wind speeds across the state have declined over the past three decades. This has serious implications on wind energy production. This project will analyze historical Oklahoma Mesonet wind data to determine if similar results can be found in Oklahoma.
Desired skills: Basic Unix/Linux, Programming experience (Python preferred)
Most applicable majors: Meteorology, Atmospheric Science, Environmental Sustainability, Engineering, Geography, GIS, Physics, Computer Science
Recent REU projects with mentors on this team:
- Analysis of wet-bulb globe temperature calculations using in-situ observations
- Near Surface Meteorological Impacts Resulting from a Total Solar Eclipse
- Radar Rainfall Estimates Versus Station Rainfall Data
- An Examination of the Accuracy of Objective Analysis Schemes
- Determining Tornadic Near-Misses via Oklahoma and Kentucky Mesonet Observations
6. Ka-band phased array radar simulation studies
Mentors: David Bodine (OU/School of Meteorology and ARRC)
Description: As part of a National Science Foundation grant, the University of Oklahoma is developing two mobile Ka-band phased array radars for cloud, precipitation, and hazardous weather studies. These dual-polarization phased array radars will be capable of collecting volumetric scans in 20 s and will be deployed to acquire multi-Doppler wind retrievals for unprecedented kinematic and microphysical studies of clouds. This project will involve analyzing simulation data (and running simulations, if desired) using Cloud Model 1 to study the benefits of these Ka-band phased array radars to a meteorological topic of interest to the REU student. Possible topics include, but are not limited to, convection initiation, entrainment processes, orographic snowstorms, or in-cloud or clear-air turbulence.
Desired skills: Programming (Python preferred), statistical analysis
Most applicable majors: Meteorology/atmospheric science, electrical/computer engineering, math or physics
Recent REU projects with mentors on this team:
- Examining the Potential of ZDR Columns to Predict Tornado Formation and Intensity in Quasi-Linear Convective Systems
- Comparing the Forecasting Performance of the UFS and WRF Models during High-Impact Severe Weather Events
- Tornadoes in the Southeast United States: Investigating the Relationship between Intensity, Damage, and Terrain
- Analysis of Tornadogenesis Failure Using Rapid-Scan Data from the Atmospheric Imaging Radar
7. Aviation Weather Hazards and Machine Learning
Mentors: Aaron Hill (OU/School of Meteorology) and Stacey Hitchcock (OU/School of Meteorology)
Description: Weather hazards like turbulence, icing, and convection pose significant risks to aviation safety. Forecast guidance on potentially hazardous conditions is provided by the Federal Aviation Administration and NOAA’s Aviation Weather Center and is typically reserved for the day-of timeframe (i.e., less than 24 hours). But there is growing need to anticipate and forecast hazardous conditions on multiple-day timescales. However, numerical weather prediction guidance is limited in resolution and may not adequately capture the localized nature of aviation weather threats. An alternative option to generate useful and operational guidance is machine learning which associate historical events (e.g., severe turbulence) with the meteorological environments, providing a mechanism by which future meteorological conditions can be used to derive probabilistic forecasts of aviation weather hazards. This project will investigate and analyze multiple publicly available datasets of aviation weather hazards to understand their strengths, weaknesses, and biases before training machine learning prediction models. The student involved in this project will get hands-on experience analyzing novel hazard datasets, characterizing climatologies and biases in hazards, and informing the development of future machine learning models.
Desired skills: Python programming or other coding experience strongly encouraged
Most applicable majors: Meteorology/atmospheric science, aviation, computer science, or related field
Recent REU projects with mentors on this team:
- Assessing Machine Learning Probabilistic Forecast Utility for Severe Weather Forecasting
- Relationships Between Atmospheric Stability and Near-Storm Turbulence
8. Two Project Topics with OU's School of Meteorology (Choose 1)
Project 1: Regional variability of ice processes in tropical MCSs
Mentors: Mani Rajagopal (OU/School of Meteorology) and Zach Lebo (OU/School of Meteorology)
Description: Unlike the contiguous United States (CONUS), radar coverage is sparse in tropical land regions and nearly absent over tropical oceans. Therefore, satellite observations are crucial to studying deep convection in the tropics. Recently, the National Aeronautics and Space Administration (NASA) carried out the "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission, which launched four satellites into polar orbits inclined at ~33° to the equatorial plane. These satellites carried identical passive microwave sensors (PMW) that can see through clouds, compared to visible and infrared imagery from Geostationary Operational Environmental Satellite (GOES) satellites, which see cloud tops. Because PMW sensors can see through clouds, they are used to indirectly retrieve column-integrated ice (from ice scattering effects) as well as profiles of temperature and humidity.
TROPICS satellites were placed in orbits such that a tropical location has subsequent overpasses from two satellites within a short interval (with a median revisit of ~60 minutes). The revisit in a short interval provides a rate of change in the observed quantities, a core objective of the TROPICS mission. Through this project, we aim to understand changes in the cloud ice concentrations and their relationship to surface rainfall. We hypothesize that this relationship be different across lifecycle stages and various tropical regions. Depending on the student's interest, we will choose different tropical regions to investigate ice processes and rainfall relationships, and contrast them between regions. In this project, the student will learn about variability in tropical deep convection, passive microwave remote sensing, bivariate statistical methods, and visualization.
More information about the TROPICS mission can be found at https://weather.ndc.nasa.gov/tropics/
Desired skills: Basic statistics, programming experience, working knowledge of UNIX/LINUX, maybe familiar with deep convection dynamics and microphysics
Project 2: How do southern United States onshore and offshore storms differ in their microphysical characteristics?
Description: Land and ocean regions exhibit contrasting thunderstorm characteristics. The storms over land typically have intense updrafts, high lightning flash rates, and dominant cold-rain processes, whereas ocean storms have weak updrafts, low lightning flash rates, and a dominant warm-rain process. One reason for the dominance of warm rain processes over the ocean is the lower aerosol concentration over the ocean than over land. With fewer aerosols and the same amount of water vapor, cloud droplets are larger over the ocean, leading to earlier precipitation onset than on land.
In this project, we aim to determine whether this dichotomy appears in onshore and offshore convection along the southern United States (US) coastline, extending from Texas to Alabama. For this research, we will use Next Generation Weather Radar (NEXRAD) data for reflectivity, surface precipitation, and dual-polarization fields to analyze differences in microphysical signatures at various altitudes between onshore and offshore systems. In this project, the student will learn about radar meteorology, dual-polarization variables, microphysical processes in deep convection, statistical analysis, and visualization.
Desired skills: Basic statistics, programming experience, working knowledge of UNIX/LINUX, and maybe radar meteorology
Most applicable majors: Meteorology, Atmospheric Science; Other majors are welcome, provided they are highly motivated and demonstrate good analysis and programming skills
9. Interpreting convection using unified lightning data during the PERiLS field campaign
Mentors: Vanna Chmielewski (NOAA/NSSL) and Sarah Stough (OU/CIWRO)
Description: Lightning flashes are a common occurrence in a wide variety of storm modes, which is not only a hazard on its own, but it can also give us information on overall storm intensity. We have several different methods available to monitor lightning from satellites to long-range ground networks, but they monitor different parts of the lightning process and provide different information. This project focuses on when we can expect that information to be in agreement and how we can use differences to inform us about the complete storm state. Depending on the interests of the student, this project could focus on examining the different detection scales and lightning flash properties within the unified lightning dataset, or comparisons of integrated lightning information to both mid-level, radar-based analyses and resulting surface impacts in the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) Project.
Desired skills: Willingness to learn about storm electrification, meteorological evolution and basic python
Most applicable majors: Meteorology, atmospheric science, physics, computer science
Recent REU projects with mentors on this team:
- Lightning Hole and Lightning Jump Relationships in the PERILS Campaign
- Investigating Lightning Flash Rates in Tornadic Environments
- Investigation of Lightning and Storm Electrification Processes Using a Phased Array Radar and Lightning Mapping Array
10. TBD
Mentors:
Description:
Desired skills:
Most applicable majors:
Recent REU projects with mentors on this team:
The following projects will be funded through other external grants:
1. Investigating the ITCZ and Hadley Cell Variability Associated with the Madden-Julian Oscillation
Mentors: Naoko Sakaeda (OU/SoM)
Description: The Madden-Julian Oscillation (MJO) is a tropical atmospheric phenomenon that can be a source of extended prediction skills of global weather. To further utilize the MJO for improving global prediction skills, it is important to extend our knowledge of how the MJO modulates global circulation. Through this project, the student will investigate how the MJO modulates the intensity and location of the ITCZ and Hadley cells, which are major features of tropical atmosphere that are also tied to extratropics through energy transport.
Desired skills: Some knowledge of Python or MATLAB programming language and statistics, or willingness to learn those
Most applicable majors: atmospheric sciences, meteorology, geography, or related fields
2. Do methods for predicting supercell motion perform well for tornadic supercells in landfalling hurricanes?
Mentors: Ben Schenkel (CIWRO) and Matt Brown (NSSL)
Description: Landfalling hurricanes frequently spawn tornadoes that are often difficult to forecast. One source of this reduced forecast skill are the methods used for predicting supercell motion, which are key to estimating the environmental favorability for tornadoes. However, limited prior work has suggested that previously derived methods for predicting supercell motion in hurricanes may be inaccurate warranting a broader climatological evaluation to determine if a re-derivation of motion methods is necessitated. Hence, this project will test how well supercell motion methods perform in landfalling hurricanes.
Desired skills: None
Most applicable majors: Meteorology, science, or mathematics