Welcome! We focus on Predictability and Applied Research for the Earth-system with Training and Optimization (PARETO). As such, we use machine learning (e.g., neural networks) and numerical modeling systems (e.g., CESM) to answer pressing questions and address challenges in modeling the Earth system. Examples of problems that we are tackling include:
Our research also strives to incorporate open-source software and data, accessible communication, and multi-discipline collaboration (particularly with computer science).
Our group name, PARETO, is inspired by the Pareto frontier, a fundamental concept in optimization theory. The concept was named after the Italian economist Vilfredo Pareto (1848-1923) and delineates trade-offs between competing objectives.
Fall 2023 group photo. Pictured from left to right: Jhayron Steven Perez Carrasquilla, Emily Faith Wisinski, Erin Elise Evans, Hannah Bao, Cumulus, and Assistant Professor Maria J. Molina. Learn more about our group [here].
If you are interested in joining our group as a graduate researcher, please note that all interested applicants must apply online to be considered.
Recent News
☁ [September] Jonathan Starfeldt and Maria J. Molina are presenting this week at the 6th NOAA AI Workshop, which focuses on heat resilience this year. Read more [here]!
☁ [September] Maria J. Molina (PI; AOSC) and Christopher A. Metzler (co-PI; CS) were awarded an NSF Collaborations in Artificial Intelligence and Geosciences (CAIG) grant. Read more [here]!
☁ [August] Erin Evans graduated with an MS degree from AOSC. Congratulations, Erin!
☁ [August] The PARETO group participated in the Reddit "Ask Science" Ask Me Anything Series hosted by UMD CMNS. Check it out [here]!
More news available [here].
Recent Publications
☁ Molina, M. J., B. DePodwin, E. Smith, K. Putsavage, M. Behl, T. Washington, K. Goebbert, M. Lacke, and M. Glackin (In Press). AMS Early Career Leadership Academy: From Idea to Reality to Evolution. Bulletin of the American Meteorological Society. [Link]
☁ Eyring, V., W. D. Collins, P. Gentine, E. A. Barnes, M. Barreiro, T. Beucler, M. Bocquet, C. S. Bretherton, H. M. Christensen, K. Dagon, D. J. Gagne, D. Hall, D. Hammerling, S. Hoyer, F. Iglesias-Suarez, I. Lopez-Gomez, M. C. McGraw, G. A. Meehl, M. J. Molina, C. Monteleoni, J. Mueller, M. S. Pritchard, D. Rolnick, J. Runge, P. Stier, O. Watt-Meyer, K. Weigel, R. Yu, and L. Zanna (2024). Pushing the Frontiers in Climate Modeling and Analysis with Machine Learning. Nature Climate Change. [Link]
☁ Fasullo, J., J. C. Golaz, J. Caron, N. Rosenbloom, G. Meehl, W. Strand, S. Glanville, S. Stevenson, M. J. Molina, C. Shields, C. Zhang, J. Benedict, and T. Bartoletti (2024). An Overview of the E3SM version 2 Large Ensemble and Comparison to other E3SM and CESM Large Ensembles. Earth System Dynamics. [Link]
☁ *Campbell, T., G. M. Lackmann, M. J. Molina, and M. D. Parker (2024). Severe Convective Storms in Limited Instability Organized by Pattern and Distribution. Weather and Forecasting. [Link]
More publications available [here].
Any opinions, findings and conclusions or recommendations expressed herein do not necessarily reflect the views of the University of Maryland.
