Articles | Volume 9, issue 2
https://doi.org/10.5194/ascmo-9-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
22 Dec 2023
| 22 Dec 2023
Forecasting 24 h averaged PM2.5 concentration in the Aburrá Valley using tree-based machine learning models, global forecasts, and satellite information
Jhayron S. Pérez-Carrasquilla
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, USA
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Paola A. Montoya
CORRESPONDING AUTHOR
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia
Juan Manuel Sánchez
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia
K. Santiago Hernández
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia
Mauricio Ramírez
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
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Anja Katzenberger, Jhayron S. Perez-Carrasquilla, Keighan Gemmell, Evgenia Galytska, Christine Leclerc, P. Punya, Indrani Roy, Arianna Varuolo-Clarke, Milica Tošić, and Nina Črnivec
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Multi-model ensembles are a central approach in climate model analysis, but their use involves many complex considerations. In this work, we review relevant literature and synthesize existing studies to contribute to the development of guidelines for designing and conducting ensemble analyses. This is complemented by a collection of useful resources and a discussion of emerging trends, supported by statistics tracing the number of publications.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-4744, https://doi.org/10.5194/egusphere-2025-4744, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
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Multi-model ensembles are a central approach in climate model analysis, but their use involves many complex considerations. In this work, we review relevant literature and synthesize existing studies to contribute to the development of guidelines for designing and conducting ensemble analyses. This is complemented by a collection of useful resources and a discussion of emerging trends, supported by statistics tracing the number of publications.
Maria P. Velásquez-García, K. Santiago Hernández, James A. Vergara-Correa, Richard J. Pope, Miriam Gómez-Marín, and Angela M. Rendón
Atmos. Chem. Phys., 24, 11497–11520, https://doi.org/10.5194/acp-24-11497-2024, https://doi.org/10.5194/acp-24-11497-2024, 2024
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In the Aburrá Valley, northern South America, local emissions determine air quality conditions. However, we found that external sources, such as regional fires, Saharan dust, and volcanic emissions, increase particulate concentrations and worsen chemical composition by introducing elements like heavy metals. Dry winds and source variability contribute to seasonal influences on these events. This study assesses the air quality risks posed by such events, which can affect broad regions worldwide.
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Short summary
This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM2.5 predictability.
This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region....
