I'm currently deepening my knowledge of atmospheric dynamics and machine learning to use new computational and data-driven methods to understand better the processes that affect people's everyday lives, including extreme weather events, climate variability, and climate change. My Ph.D. research is focused on using artificial intelligence to study the characteristics and potential future changes of the different Earth system processes that contribute to the subseasonal-to-seasonal (S2S) predictability of large-scale atmospheric patterns. Previously, I studied tropical cyclones' internal dynamics for my master's thesis and the origin of extreme precipitation events using back-trajectories for my undergraduate thesis. 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 satellites, reanalysis, ground-based stations, radar, and model outputs.
- Pérez-Carrasquilla, J.,Hoyos, C. (2020). Tropical Cyclone Internal Dynamics and its Influence on the Intensity Changes: WRF Idealized Simulation in a Quiescent Environment and GOES-R IR case study. 10.13140/RG.2.2.35939.99360.
- Hoyos, C., Ceballos, L., Pérez-Carrasquilla, J., Sepúlveda, J., López-Zapata, S., Zuluaga, M., Giron, N., Herrera-Mejía, L., Hernández, O., Guzman, G., Zapata, M. (2019). Meteorological conditions leading to the 2015 Salgar flash flood: lessons for vulnerable regions in tropical complex terrain. Natural Hazards and Earth System Sciences. 19. 2635-2665. 10.5194/nhess-19-2635-2019.