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
☁ [May] Hannah Bao graduated from the Department of Atmospheric and Oceanic Science with a Bachelor of Science degree. Congratulations, Hannah! Hannah will be continuing her studies at UMD in the Master's of Professional Studies in Data Science and Analytics program.
☁ [May] Jhayron S. Perez Carrasquilla and Maria J. Molina participated in the AMS Washington Forum which was held in the NOAA Center for Weather and Climate Prediction. [Link]
☁ [May] Maria J. Molina participated in a workshop on the potential for AI to transform weather prediction jointly hosted by the White House Office of Science and Technology Policy (OSTP) and NOAA. [Read More]
☁ [May] Maria J. Molina will serve a four-year term on the US CLIVAR Predictability, Predictions, and Applications Interface (PPAI) Panel.
More news available [here].
Recent Publications
☁ 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]
☁ Shah, S. H., C. O'Lenick, A. Ramos Valle, J. Wan, O. Wilhelmi, K. Ash, C. M. Edgeley, M. J. Molina, J. Moulite, C. C. Pizzaro, K. Emard, O. Cameron, J. Done, C. W. Hazard, T. Hopson, M. Jones, F. Lacey, M. A. Lachaud, D. Lombardozzi, M. Mendez, R. Morss, K. Ricke, F. Tormos-Aponte, W. Wieder, and C. Williams (2023). Connecting Physical and Social Science Datasets: Challenges and Pathways Forward. Environmental Research Communications. [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.
