Jana Nascimento
Post-Doctoral Associate
Research Interests
I was born and raised in Belém, Brazil. There I attended the University Center of the State of Para where I received my Bachelors degree in Information Systems. In 2013 I worked for the Brazilian Journal of Ornithology where I developed an electronic publishing system as a software analyst. When I completed my project with the Journal of Ornithology I decided to pursue a masters degree in computer science at the Federal University of Amazonas in Manaus. My masters thesis was on Ontology development and evolution, a sub field of artificial intelligence. In 2016 I began a PhD in Natural and Earth science at the National Institute of Amazonian Research under Dr. Paulo Artaxo. In 2017 I moved to São Paulo for a year and a half to work closely with my advisor and to interact with the Physics research group at the University of São Paulo. My research involved a numerical study of the atmospheric aerosols in the Amazonian region, focusing on regional models and aerosol optical properties.
Current Research
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.
Research Categories
Atmosphere, Chemistry, Climate and WeatherResearch Images
Honors and Awards
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About CECA
CECA connects and creates a supportive environment for graduate students and postdocs who come from various academic units to do research in CIRES.