Jhayron S. Pérez-Carrasquilla

Contact: jhayron@umd.edu

Ph.D. Student - Atmospheric and Oceanic Science Department at the University of Maryland - Research Assistant at the PARETO Group

About me

Originally from Colombia 🇨🇴, I'm currently a Ph.D. candidate studying atmospheric predictability and climate dynamics by leveraging machine learning techniques. I work with Dr. Maria Molina at the Atmospheric and Oceanic Science Department at the University of Maryland 🇺🇸. Currently, I am part of the Fresh Eyes on CMIP initiative, working with the Data Analysis group focusing on multi-model ensembles. I am also part of the American Meteorological Society's Committee on Artificial Intelligence Applications to Environmental Science. Additionally, I recently visited NCAR at Boulder, CO, as part of the Graduate Visitor Program. I focused on evaluating long-term changes on the large-scale mid-latitude circulation and the impacts on surface weather.

I earned a bachelor's degree in engineering and a master's degree in water resources from the Universidad Nacional de Colombia 🇨🇴. My main research interests are large-scale Earth system dynamics, variability, and predictability, extreme weather events, and climate change 🌦️⛈️🌪️. My work has mainly aimed at applying machine learning and numerical modeling to gain a deeper understanding of processes in climate, meteorology, hydrology, and air quality 🌎. In my free time, I enjoy playing and watching sports, watching movies, reading, music ⚽🏓📚🎵!

Research interests

I'm currently deepening my knowledge of Earth system dynamics and machine learning. More specifically, I use recently developed computational and data-driven methods to better understand the processes that modulate the occurrence and characteristics of mid-latitude large-scale weather regimes 🤓. These large-scale features affect people's everyday lives by driving the occurrence of extreme weather events under different climate variability change scenarios 🌎. Some methods I use in my research include tree-based machine learning, deep learning, eXplainable AI, data-driven causal discovery, and unsupervised clustering 💻. I combine these tools with Earth system reanalyses and models to unveil Earth system drivers of subseasonal-to-seasonal predictability and the effects of climate variability and change 🌍🌡️. I look forward to keep exploring these same topics in the future!

My master's thesis was mainly about how the internal dynamics of tropical cyclones behave when the storm is intensifying 🌀, and my undergraduate thesis was about how the origin of air parcels affected the characteristics of extreme precipitation events over the Colombian Andean region ⛰️. Additionally, I have some experience with idealized modeling and empirical forecasts of air quality, meteorological and hydrological variables. I have mostly used Python during my career to handle data from satellite, reanalysis, ground-based stations, radar, and model outputs 🐍.

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Awards and honors

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