Jana Nascimento

My research mainly uses ML algorithms combined with satellite observational data and numerical regional/global models to improve the understanding of how physics-based, Al/ML-based, or hybrid approaches can enhance numerical weather prediction and air quality forecasting models capabilities, particularly focusing on: (i) fire intensity and spread; (ii) lightning; (iii) precipitation in convective parameterization.

I worked previous performing numerical experiments by using the WRF-Chem model to study the atmospheric aerosols in the Amazonian region, focusing on the effects that anthropogenic emissions have on the Amazon forest, especially on the ozone and secondary organic aerosols production.

My research interest involves a broad and interdisciplinary approach including investigating and better understanding atmospheric composition processes and their interconnections. Particularly, chemistry and physics modeling; fire behavior and emissions; anthropogenic-biogenic emissions interactions, air quality forecasting, convection cell tracking methods and convective parameterization. Most of the projects I current work are aligned with the applicability of ML/AI methods to improving high-resolution numerical weather and air quality forecasts models and how to develop connections and ability to couple code between Al/ML to NWP-AQM.