Current Projects
| Project Period | Funding Source | Project Title | PI | Co-PI |
|---|---|---|---|---|
| 2025 - 2030 | National Science Foundation | Center for Interdisciplinary Research on Convective Storms (CIRCS) | V. Gensini | W. Ashley, and A. Haberlie |
| 2025 - 2028 | National Science Foundation | Polarimetric Radar and Modeling Perspectives of Updraft, Cold Pool, and Mesovortex Evolution in Quasi-Linear Convective Systems | M. Van Den Broeke | A. Haberlie, and W. Ashley |
| 2025 - 2026 | National Science Foundation | Harnessing the power of machine learning to generate ensembles of regional climate projections | A. Haberlie |
Textbooks
Python Programming for the Geosciences
Online and free textbook with Python examples, explanations of important coding concepts, exercises and homework assignments, and an example syllabus.
Datasets
This webpage provides a “one stop shop” for over 500,000 data rich, geospatial, radar reflectivity images centered on high-impact weather events. These images have consistent dimensions and intensity values on a grid with relatively low spatial distortion over the Conterminous United States. So, they are perfect for training and validating machine learning models. Crowd-sourced labels will provide a consistent dataset on which to test model performance.
In 2017, a collaborative group of researchers at Northern Illinois University and Colorado State University were awarded a National Science Foundation grant for a project titled “Observed and Future Dynamically Downscaled Estimates of Precipitation Associated with Mesoscale Convective Systems”. The goal of this project was to assess the importance of mesoscale convective system (MCS) precipitation in the United States now and in the future. While myriad observational datasets exist to examine current MCS activity, future projections of MCS activity are much more difficult to generate for a number of reasons, including:
- Critical MCS processes occur at relatively small spatial scales (~1 km) and temporal scales (~1000 s) requiring grid spacings of around 4 km or less;
- Natural, year-to-year, variability in MCS activity requires a sufficiently long simulation period to reveal the stable spatiotemporal frequency of simulated MCSs;
- MCSs are sensitive to distant mesoscale processes, requiring a large modeling domain that encompasses the Conterminous United States (CONUS);
- The high spatiotemporal resolution, continental modeling domain, and long simulation periods require immense computational and storage costs; and
- Analyzing the resulting petabyte-scale dataset requires high-performance computing and specialized software packages and techniques.
Despite these technological barriers, the team generated five sets of simulations covering three different temporal periods and two different warming scenarios–namely, Representative Concentration Pathway (RCP) 4.5 and 8.5 from the 5th iteration of the Coupled Model Intercomparison Project (CMIP5)
Research Groups
Weather Climate and Society Research Group
The homepage of the WCS research group at Northern Illinois University. The group is comprised of faculty, graduate students, and undergraduate students.
Center for Interdisciplinary Research on Convective Storms
“Advancing our understanding of the multifaceted impacts that convective storms impose on society and the economy.”
Online Tools
GEFS Severe Weather Forecasting Products
Unique visualizations of GEFS ensemble data to support decision support for stakeholders who are vulnerable to severe weather perils.
GitHub Repositories Supporting MCS NSF Project
Code and examples supporting publications that describe the work associated with an NSF project looking at historical and potential future MCS activity.