Articles | Volume 19, issue 7
https://doi.org/10.5194/gmd-19-2691-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-2691-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Apr 2026
Development and technical paper |
| 10 Apr 2026
| 10 Apr 2026
Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models
Nilton Évora do Rosário
CORRESPONDING AUTHOR
Departamento de Ciências Ambientais, Universidade Federal de São Paulo, Diadema, SP, Brazil
Karla M. Longo
Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP, Brazil
Pedro H. Toso
Departamento de Ciências Ambientais, Universidade Federal de São Paulo, Diadema, SP, Brazil
Saulo R. Freitas
Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP, Brazil
Marcia A. Yamasoe
Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, Cidade Universitária, São Paulo, SP, Brazil
Luiz Flávio Rodrigues
Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP, Brazil
Otavio Medeiros
Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP, Brazil
Haroldo Campos Velho
Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP, Brazil
Isilda da Cunha Menezes
Center for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Ana Isabel Miranda
Center for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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Short summary
This study maps aerosol regimes over the Iberian Peninsula using AERONET data and machine learning. Five types were identified, from Saharan dust to smoke, highlighting differences in particle size and absorption. Combining observations with model data improves aerosol representation in climate simulations, reducing uncertainties and enhancing understanding of regional air quality and climate impacts.
This study maps aerosol regimes over the Iberian Peninsula using AERONET data and machine...
