ESPLab anticipates hiring a Research Scientist sometime in the Jan/Feb timeframe. The research scientist will help to lead a project on Improving Sub-Seasonal to Seasonal Forecast Interpretability for Global Water Security. The project tasks are:
- Past Predictions of High Impact Events and Prediction Skill in Geographic Hotspots
- Sources of Predictability and Model Errors
It is anticipated that the position will require: A PhD + 2+ years of experience, strong data analysis skills in Python, knowledge of hindcast datasets, evidence of mentorship capabilities, ability to work in a collaborative team environment, and strong oral and written communication skills.
We do not have any postdoc positions at this time, but anticipate opening one in the near future. Please check back!
This prestigious NOAA-sponsored program appoints fellows that are hosted by a mentoring scientist at a U.S. university or research institution to work in an area of mutual interest. Please contact me (kpegion at ou dot edu) If you are looking for a host for a project related to Earth System Prediction on subseasonal to multi-year timescales.
ESPLab anticipates 2 Graduate Research Assistant Positions (M.S. or PhD)
for Fall 2024
GRA Position #1: Improving Sub-Seasonal to Seasonal Forecast Interpretability for Global Water Security
This project will use eXplainable artificial intelligence (XAI) with observations and modeling systems to understand sources of predictability and model errors in predicting extreme precipitation across subseasonal, seasonal, and interannual timescales (weeks to years). This methodology, based on work by (Pegion, Becker, and Kirtman 2022), can be applied to specific countries, states/provinces, and/or regions. Better understanding of predictability sources and their possible long-term changes based on geopolitical boundaries and regions can inform managers at the appropriate level regarding resources and infrastructure to better protect local populations.
GRA Position #2: Bridging Predictions and Projections: Understanding Predictability from Initialized Multi-Year to Decadal Predictions for High-Impact Climate Futures
The low frequency variability of North Atlantic sea surface temperature (SST) can affect summer US precipitation, winds, and heat through its impact on the North Atlantic Subtropical High (NASH). We will test the hypothesis that the NASH is a source of predictability for high-impact multi-year to decadal climate futures in hydrology/water resources, extreme temperatures, and coastal inundation, and that initialized multi-year to decadal predictions can better predict these high-impact climate futures than uninitialized projections. To our knowledge previous multi-year to decadal prediction studies have not investigated high-impact climate futures such as extreme precipitation and heat, drought, and coastal inundation.
If our hypothesis is true, then the implication is that we need an initialized multi-year to decadal prediction system to make skillful and useful predictions of high-impact climate futures on these timescales. However, either outcome is of value given that a real-time impacts-based multi-year to decadal prediction system does not currently exist, and the potential benefit of developing such a system needs to be fully investigated.
To test this hypothesis, we will investigate how initialization of N. Atlantic SST impacts the predictability of the NASH, and how the prediction of the NASH relates to prediction of extreme heat and precipitation, drought, and coastal inundation during the summer in the US. NCAR-CESM and GFDL-SPEAR predictions and projections will be used and compared to identify the role of SST initial conditions in these predictions. We will also investigate prediction of extreme indices of temperature and precipitation in the five US climate regions most impacted by the NASH, and prediction of onshore winds along the East Coast of the US to explore coastal inundation.
Interested students should also review the Prospective Graduate Student Information from the School of Meteorology and the section on Knowledge Expectations for Incoming Graduate Students.
If you meet the Knowledge Expectations and are genuinely interested in these projects, please contact me to further discuss your research interests (kpegion at ou dot edu).
Note that you must formally apply to the University of Oklahoma School of Meteorology to be considered for admission.
Recommendations for application:
The projects listed above are the ones for which I have or expect to have funding to support a graduate student starting in Fall 2024.
Your application will stand out if you describe your genuine interest in one of the projects described above and explain why the project interests you.
These projects involve data analysis, coding in Python, and many hours working in front of a computer. I expect incoming graduate students to have taken at least one programming class, to enjoy programming, and to be eager to learn more. If that describes you, then I can help you further develop these skills. If you do not enjoy coding, data analysis, or sitting in front of a computer, you will likely not enjoy this type of research. Your application will stand out if you describe your data analysis and coding experience and skills as well as your enthusiasm for research that involves these skills.
ESPLAb anticipates hiring two undergradaute researchers in Spring 2024
Both positions will involve data management, analysis, and product development in support of research projects using subseasonal, seasonal, multi-year, and decadal forecast data from weather and climate models. Expected skills are:
- Enrolled as an undergraduate student at OU in Spring 2024.
- Majoring in meteorology, geography, environmental science, computer science, engineering, or a related discipline.
- Completed one course (e.g., METR 1313) or other demonstrated experience in Python programming.
- Completed one course in meteorology (e.g., METR 1003 or METR 1014)
- An ability to work both independently and as part of a team.
- Evidence of responsibility through employment, volunteering, family, or other relevant experiences.
Additional Desired Qualifications (not required)
- Genuine interest in climate
- Genuine interest in application of computer programming to climate data
- Demonstrated experience working in a Unix/Linux environment