Last Updated: April 4, 2024
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 will be funded through this REU (first set of projects) or an NSF CAREER Grant.
1. The Seasonality of Madden-Julian Oscillation (MJO) Teleconnections in the Southern Hemisphere
Mentors: Dr. Naoko Sakaeda (OU/SoM) and Tatiana Esteva-Ingram (OU/SoM)
Description: This project aims to analyze the seasonality of the MJO teleconnections in the Southern Hemisphere using satellite and reanalysis data. The MJO is known to have significant tropical-extratropical interactions via MJO teleconnections. MJO teleconnections are the linkage of weather events in distant regions that are associated with MJO convective anomalies. However, the Southern Hemisphere teleconnections aren’t studied as extensively compared to the Northern Hemisphere even though tropical-extratropical interactions in the Southern Hemisphere is important to understanding climate variability . Therefore, this research will examine the atmospheric patterns in the Southern Hemisphere that are associated with the MJO and how they vary seasonally.
Desired skills: Basic MATLAB or other programming experience
Most applicable majors: (none specified)
Recent REU projects with mentors on this team:
- Connecting African Easterly Wave Development and Offshore Precipitation
- Factors Affecting Precipitation over Western Puerto Rico during the NASA 2021 CPEX-AW Project
- Evolution of the Diurnal Cycle of Tropical Rainfall associated with the Madden-Julian Oscillation
2. Novel Lightning Analysis
Mentors: Dr. Michael Stock and Dr. Vanna Chmielewski (NOAA/NSSL)
Description: There are many storm electrification topics which have been understudied from high-resolution, three-dimensional aspects of how storms electrify and lightning is produced to long-term applications. This project will work with the mentors to define a research project involving lightning. Examples of investigation topics include but are not limited to: 1) the electrical structure of lake effect snow storms in New York, 2) climatological trends of global thunder and lightning data, and 3) three-dimensional lightning patterns in QLCS storms in the southeast US.
Desired skills:
Most applicable majors:
Recent REU projects with mentors on this team:
3. Evaluating Mesoscale Convective Systems using Rapid-Update Data from a Dual-Polarization Phased Array Radar
Mentors: Kristofer Tuftedal (CIWRO), Charles Kuster (NOAA/NSSL), forecaster (National Weather Service Norman)
Description: Previous research has demonstrated the potential benefits of dual-polarization radar data for anticipating the development of downbursts in low-shear environments with disorganized convection. However, further examination is needed to explore the potential benefits of dual-polarization data in nowcasting damaging winds and/or mesovortices in other storm modes including mesoscale convective systems. Therefore, this research project will focus on an analysis of rapid-update, dual-polarization, phased array radar data in mesoscale convective systems to identify any useful radar signatures associated with strong winds and/or tornadoes. We specifically aim to examine potential operational benefits of KDP cores observed with rapid volumetric updates and compare the evolution of KDP cores with signatures in Doppler velocity data. The analysis will include quantifying the differences in radar signatures of severe and nonsevere thunderstorms (i.e., “null cases”), and examining rapidly-evolving trends in these radar signatures, which might provide valuable information to warning forecasters. Opportunities may also exist to assist researchers with radar data collection and shadowing a National Weather Service forecaster.
Desired skills: Knowledge or interest in meteorology including severe weather radar signatures (e.g., KDP core, tornado vortex signature), basic computer programming skills (any language)
Most applicable majors: Meteorology or a closely-related field
Recent REU projects with mentors on this team:
- Comparing Differential Reflectivity Arcs Using Phased Array and Conventional Radar Data
- Evolution of Dual-Pol Radar Signatures of QLCS Tornado Development
4. coming
Mentors: Dr. Harold Brooks (NOAA/NSSL) and Dr. Kim Klockow McClain (UCAR/CPAESS & NWS/NCEP; REU 2004)
Topic: Update some old work from Simmons and Sutter on Tornado Warnings, Lead Time, and Death.
Desired skills:
Most applicable majors:
Recent REU projects with mentors on this team:
- The Public’s Prioritization of Probability and Intensity in Tornado Risks
- Anti-Goldilocks Forecasting: Modeling Forecast Probability Going From Small to Large Scales
- Climate Change Projections of Severe Weather Environments by Synoptic Patterns in GFDL-CM3
- Illustrating Predictability for Nocturnal Tornado Events in the Southeastern United States
5. Severe weather and perceptions of wellbeing in Oklahoma
Mentors: Dr. Maggie Leon-Corwin (Postdoctoral Research Associate; IPPRA) and Dr. Joe Ripberger (IPPRA)
Description: Pre-event factors, such as community and individual well-being and resources, can bolster preparedness and facilitate recovery. Reciprocally, severe weather impacts can deplete available resources, generate community and individual level stressors. This project aims to investigate the relationship between severe weather and perceptions of wellbeing in Oklahoma. To do so, this project will leverage data from the M-SISNet, a random sample panel survey of Oklahoma households (n = 2,000). Under guidance from REU mentors, the student will develop a literature review and conduct preliminary quantitative analysis to provide insight into well-being and perceptions of severe weather. In doing so, this project will establish a baseline understanding of the connection between wellbeing, severe weather, and climate.
Desired skills: Time management. Previous coursework on statistics for social sciences. Familiarity with statistical analysis programs (i.e., STATA, R, SPSS).
Most applicable majors: Sociology, Political Science, Psychology, or other related fields with an interest in the social sciences.
Recent REU projects with mentors on this team:
6. Convective clouds in coastal cities
Mentors: Yongjie Huang (CAPS) and Dhwanit J. Mise (CAPS & CIWRO)
Description: Convective clouds play a crucial role in Earth’s weather and climate systems, as they transport matter and energy across the troposphere and markedly affect large-scale atmospheric circulations. However, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) highlights the significant challenge of representing convective clouds and their interactions with the surrounding environments in climate models. The sensitivity of convective clouds to aerosol and meteorological environments, and the constraints posed by limited observational data for parameterizations, introduces significant uncertainties into weather and climate modeling. The Tracking Aerosol Convection Interactions Experiment (TRACER), conducted in the vicinity of Houston, Texas, provides a valuable dataset by observing numerous isolated convective cells under a range of meteorological and aerosol conditions. This dataset offers a unique opportunity to study the influence of environmental parameters on the properties of convective clouds. Leveraging observations from the TRACER experiment, this study seeks to enhance our understanding of the evolution of convective clouds under a wide range of environmental conditions.
Desired skills: programming experience, but not required
Most applicable majors: Atmospheric sciences, physics, or mathematics preferred, but not required.
7. Evaluating Rotating Thunderstorm Predictions in a Next-Generation Warn-on-Forecast System with 1-km Grid Spacing
Mentors: Dr. Derek Stratman (OU/CIWRO & NOAA/NSSL) and Dr. Chris Kerr (OU/CIWRO & NOAA/NSSL; REU2010)
Description: A next-generation version of the Warn-on-Forecast System (WoFS), which provides probabilistic guidance of individual thunderstorms, has been running experimentally the past three years at the National Severe Storms Laboratory. The new experimental system (i.e., WoFS-1km) consists of a domain with 1-km horizontal grid spacing nested within a traditional WoFS domain with 3-km horizontal grid spacing. WoFS-1km provides a unique opportunity to explore the impact of grid spacing on ensemble predictions of thunderstorms. Recent research used an object-based method with composite reflectivity to show that forecasts with 1-km grid spacing are more skillful at predicting small thunderstorms than forecasts with 3-km grid spacing. This project will extend that work by using updraft helicity in addition to composite reflectivity to assess whether a 1-km version of WoFS is better able to predict low- and mid-level rotation in thunderstorms.
Desired skills: Experience with basic Unix/Linux commands and a programming language (preferably Python)
Most applicable majors: Meteorology or related field
Recent REU projects with mentors on this team:
8. Examining the use of color in Weather Forecasting Office (WFO) graphics for extreme weather events
Mentors: Dr. Zoey Rosen (PostDoc at IPPRA), Abby Bitterman, MPA (Research Associate at IPPRA), Anna Wanless, MS (Research Associate at IPPRA), and Dr. Joe Ripberger (Deputy Director for Research IPPRA)
Description: This project is interested in looking at how the NWS uses different colors in their hazard and risk graphics. Color is one of the most prominent aspects of graphic design used in weather data visualization, and we want to capture both how colors are used and discussed for major weather events. The student in this project will take inventory of WFO graphics for a specific weather hazard–we are initially interested in flood graphics, but are very open towards pivoting hazards if the student is interested in another type of weather. After data collection, the student will run preliminary quantitative analyses of the inventory data and qualitative thematic analyses of the graphics with the mentors of this project.
Desired skills: Data collection, organization, and visualization. Interest in developing social science qualitative and quantitative skills that pertain to the weather domain.
Most applicable majors: Communication, Meteorology, Atmospheric Science, Psychology, Other related social sciences
Recent REU projects with mentors on this team:
- Preparing to adapt: Are public expectations in line with climate projections?
- The Public’s Prioritization of Probability and Intensity in Tornado Risks
9. Examining Dynamically Downscaled Regional Climate Simulations of Severe Thunderstorms in novel ways
Mentors: Dr. Mike Baldwin (OU/CIWRO & NOAA/SPC), Dr. Kim Hoogewind (OU/CIWRO & NOAA/NSSL), and Dr. Harold Brooks (NOAA/NSSL)
Description: Studies assessing potential climate change influences on future severe weather climatology have mostly focused on estimating changes of environmental parameters (e.g., convective available potential energy, deep-layer shear) in global climate models, or more recently, through severe weather proxy occurrences within high-resolution dynamically downscaled simulations. However, these approaches fail to explicitly account for any potential changes in the mid-latitude circulation. This project will attempt to marry traditional methods with more novel methods which incorporate information about the synoptic pattern (and thus potential circulation changes), which can indirectly influence severe weather probability. The project has two potential avenues which the student may choose:
- Expand upon a previous REU research project which evaluated projections of severe weather environments partitioned by synoptic patterns, by applying the same approach to the dynamically downscaled WRF simulations of Hoogewind et al. (2017). The main research goal of this study is to evaluate whether the incorporation of synoptic pattern information plays a role in the projections of future severe weather climatology.
- Evaluate a machine learning approach, which trains on environmental information from climate model projections to predict severe weather occurrences in dynamically downscaled WRF simulations using different proxies as “observations” of severe thunderstorms. The main research questions of this approach seek to 1) whether training on a historical period of climate model and dynamically downscaled simulations provide a reasonable estimate of future severe weather as compared to the WRF simulations and 2) whether incorporating information about the synoptic pattern impacts the skill of the ML model. High-resolution simulations are very computationally expensive and take a significant amount of time to complete, so the study may provide insight as to the utility of high-resolution regional climate modeling for studying changes in future severe thunderstorms.
Desired skills: Basic knowledge of severe thunderstorms and their environments, interest in climate variability and change, and basic Python programming skills.
Most applicable majors: Meteorology or a closely-related field
Recent REU projects with mentors on this team:
- Climate Change Projections of Severe Weather Environments by Synoptic Patterns in GFDL-CM3
- A 180-year Climatology of Severe Weather Environments in the United States
10. Possible project(s) coming from WDTD
Mentors: Stephanie Edwards, Jim LaDue, Chris Spannagle, Melissa Lamkin, Lexy Elizalde (REU 2021),Samantha Boyd, Alyssa Bates
Description: What’s the General Idea: This project would extend the work of Kuster and co-authors published in 2021 and assess the skill in using Kdp cores in downburst detection and short-term prediction to an independent dataset. Several storm, and near-storm environment attributes will be used as predictor variables to predict the strength of a downburst as detected by the peak divergent velocity difference and storm reports found within the following 30 minutes. In addition, there will be an additional proposal to evaluate an additional radar-based signature for verification, the rate of expansion of the outflow boundary.
The project will focus on collecting low shear convection events commonly found in weakly-forced synoptic environments collected by the student. A sufficient number of cases shall be collected from previous seasons in a variety of environments where storms exist within an optimal range interval from a WSR-88D.
The results of this study will be immediately beneficial to the warning decision training that the NWS conducts.
Skills needed: The student will need to have basic statistics knowledge and will need to be able to use either spreadsheet software and/or a more detailed statistical package such as R. Basic scripting is an additional desirable skill. The mentors will teach the student how to interpret radar-based downburst signatures and the basic research methodology.
Most applicable majors:
REU projects with mentors on this team:
- Errors in the WSR-88D ZS (Snow) Algorithm
- Nonmeteorological Factors in Warning Verification
- Toward a Climatology of the Enhanced-V Signature
11. A Qualitative Content Analysis of Journalistic “Place-Making” in Televised Tornado Coverage
Mentors: Dr. Darren Purcell and Victoria Johnson (OU/DGES)
Description: This project will explore how local broadcast meteorologists communicate risk information during their televised coverage of tornado outbreaks. In particular, this work will examine how journalistic “place-making”, a process of constructing and shaping the meanings associated with and perceptions of geographic locations, informs their communicative strategies. The student researcher will conduct an in-depth analysis of the content of televised tornado warning coverage to demonstrate how broadcast meteorologists utilize the “place-making” process to establish localized meanings of risk within the places affected by tornado threats, even when these communities may lack physical or direct connection to the discussed areas. The aim of this project is to uncover how risk messages are tailored to meet specific community needs, as their varied understanding of the communities they serve directly influences how they engage in "place-making" practices. Through this analysis, the student will deepen their understanding of how the act of place-making influences the interpretation and dissemination of tornado risk messages. Depending on the student’s interests, the project may focus on tornado outbreaks in the Southeast.
Desired skills: Interest in learning how to use qualitative methods and analysis; no prior experience necessary.
Most applicable majors: Geography, Meteorology, Communication, or a closely related field
Recent REU projects with mentors on this team:
12. Hydroclimate variability across the Southern Plains, Gulf of Mexico, Caribbean, and/or Central America
Mentors: Dr. Elinor Martin (SoM)
Description: Brief project description: Flexibility to develop specific project details depending on students interest, but can include understanding the variations in the onset and demise of wet seasons and associated impacts or the representation of varying SSTs between the Gulf of Mexico and the Caribbean in climate models and the impact on precipitation across the region.
Desired skills: Some Python programming, interest in climate and climate impacts
Most applicable majors: Meteorology, atmospheric science, geography
Recent REU projects with mentors on this team:
- Connecting African Easterly Wave Development and Offshore Precipitation
- Factors Affecting Precipitation over Western Puerto Rico during the NASA 2021 CPEX-AW Project
- Synoptic Analysis of Two 14-Day Extreme Precipitation Events in the Northwestern United States
13. Understanding the Reorganizing Process of Mesoscale Convective Systems over Coastal Ocean during CPEX-CV
Mentors: Dr. Shun-Nan Wu (SoM), Dr. Naoko Sakaeda (SoM)
Description: This project aims to examine the transitional process of coastal Mesoscale Convective Systems using remote sensing retrievals and sounding measurements collected during the NASA CPEX-CV field campaign. The West African offshore region is known to be where the North Atlantic hurricanes stem from. However, it is difficult to simulate convective activity over this area due to the scarce observations of atmospheric structure. Therefore, this project will use data collected during the CPEX-CV field campaign to examine the synoptic conditions and the transitional processes of coastal mesoscale convective systems over offshore West Africa.
Desired skills: Basic programming experience
Most applicable majors:
Recent REU projects with mentors on this team:
- Factors Affecting Precipitation over Western Puerto Rico during the NASA 2021 CPEX-AW Project
- Connecting African Easterly Wave Development and Offshore Precipitation
14. Surprise Project!
Mentors: ? Dr. Daphne LaDue (CAPS) and Alex Marmo (CAPS) ?
Description: This project might be a project with Daphne and Alex related to analyzing and improving NWS's decision support services to emergency managers and other partners.
Desired skills: Good text analysis and critical thinking skills
Most applicable majors: Any
Recent REU projects with mentors on this team:
- Before, During, and After Severe Weather: How the Brief Vulnerability Overview Tool May Impact Emergency Manager Decision-Making and NWS Forecaster-EM Communications
- Tornado Warning Decision Making with the Probabilistic Hazard Information Tool
- Tornado Damage Mitigation: What National Weather Center Visitors Know and Why They Aren't Mitigating
The following projects will be funded through AI2ES:
AI2ES-1. Deep Learning Approaches to Subseasonal Forecasting of Extreme Rainfall Events.
Mentors: Dr. Maria Madsen
Description: This project will include a collaboration with ECMWF (European Centre for Medium-Range Weather Forecasts).
Desired skills: Python programming, an interest in using machine learning for weather prediction, understanding basic Unix/Linux
Most applicable majors:
AI2ES-2. (untitled)
Mentors: OU postdoc co-mentoring with Dr. Ann Bostrom (UW)
Description: This project will involve the convergence of AI, Environmental Science and Risk Communication.
Desired skills:
Most applicable majors: