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 2024 group photo. Pictured from left to right: Luke Wichrowski, Kyle Hall, Sandy Kerr, Sunny Sharma, Emily Wisinski, Dean Calhoun, Jon Starfeldt, Jhayron S. Perez-Carrasquilla, Assistant Professor Maria J. Molina, and Visiting Postdoctoral Fellow Manuel Titos. 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
☁ [June] Jonathan Starfeldt earned the prestigious NSF Graduate Research Fellowship Program (GRFP) Award for his project titled, "Disentangling Urban Heat Sources: What spatial and temporal scales contribute most to urban heat extremes?" We are so incredibly proud of you, Jon!
☁ [June] Dean Calhoun earned the prestigious NSF Graduate Research Fellowship Program (GRFP) Award for his project titled, "Quantifying Predictability Inequity in Data-Driven and Physics-Based Weather Models." We are so incredibly proud of you, Dean!
☁ [May] Luke Wichrowski graduated with Bachelor of Science degrees from the Department of Atmospheric and Oceanic Science and the Department of Computer Science. Congratulations, Luke! Luke will be working with NOAA's Total Operational Weather Readiness - Satellites (TOWR-S) team via KBR, Inc.
More news available [here].
Recent Publications
☁ *Pérez-Carrasquilla, J. S., and M. J. Molina (In Press). An Earth-System-Oriented View of the S2S Predictability of North American Weather Regimes. Artificial Intelligence for the Earth Systems. [Link]
☁ Ebert-Uphoff, I., L. Ver Hoef, J. S. Schreck, J. Stock, M. J. Molina, A. McGovern, M. Yu, B. Petzke, K. Hilburn, D. Hall, D. J. Gagne, W. Campbell, J. T. Radford, J. Q. Stewart, and S. Scheuerman (In Press). Measuring Sharpness of AI-Generated Meteorological Imagery. Artificial Intelligence for the Earth Systems. [Link]
☁ *Easthom, G., G. M. Lackmann, M. J. Molina, L. DeHaan, and J. Cordeira (In Press). Analyzing Atmospheric River Reforecasts: Self-Organizing Error Patterns and Synoptic-Scale Settings. Weather and Forecasting. [Link]
☁ Feng, Z., A. F. Prein, J. Kukulies, T. Fiolleau, W. Jones, B. Maybee, Z. L. Moon, K. M. Núñez Ocasio, W. Dong, M. J. Molina, *M. G. Albright, M. Rajagopal, V. Robledo, J. Song, F. Song, L. R. Leung, A. C. Varble, C. Klein, R. Roca, R. Feng, and J. F. Mejia (2025). Mesoscale Convective Systems tracking Method Intercomparison: Application to DYAMOND Global km-scale Simulations. Journal of Geophysical Research: Atmospheres. [Link]
☁ Ullrich, P. A., E. A. Barnes, W. D. Collins, K. Dagon, S. Duan, J. Elms, J. Lee, L. R. Leung, D. Lu, M. J. Molina, and T. A. O’Brien (2025). Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models. Journal of Geophysical Research: Machine Learning and Computation. [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.
