2022
DOI: 10.1002/essoar.10512905.1
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Towards emulating an explicit organic chemistry mechanism with random forest models

Abstract: Predicting secondary organic aerosol (SOA) formation relies either on extremely detailed, numerically expensive models accounting for the condensation of individual species or on extremely simplified, numerically affordable models parameterizing SOA formation for large-scale simulations. In this work, we explore the possibility of creating a random forest to reproduce the behavior of a detailed atmospheric organic chemistry model at a fraction of the numerical cost. A comprehensive dataset was created based on… Show more

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“…The data set that was constructed to train and test the random forests and the Python code implementing the random forest training and testing have been uploaded on Zenodo (Mouchel‐Vallon & Hodzic, 2022).…”
Section: Data Availability Statementmentioning
confidence: 99%
“…The data set that was constructed to train and test the random forests and the Python code implementing the random forest training and testing have been uploaded on Zenodo (Mouchel‐Vallon & Hodzic, 2022).…”
Section: Data Availability Statementmentioning
confidence: 99%