edu.ucar.opensky::articles:26353 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T19:31:54.926460 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d78056m2 https://doi.org/10.5281/zenodo.7327053 https Related Dataset #1 Toward emulating an explicit organic chemistry mechanism with a random forest model: dataset and training code https://doi.org/10.5065/D6RX99HX https Related Service #1 Cheyenne: SGI ICE XA Cluster Toward emulating an explicit organic chemistry mechanism with random forest models 2023-05-27T00:00:00Z publication https://n2t.org/ark:/85065/d78056m2 https://doi.org/10.1029/2022JD038227 Mouchel-Vallon, C. author Hodzic, Alma author UCAR/NCAR - Library publisher 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 data set was created based on thousands of individual detailed simulations, randomly initialized to account for the variety of atmospheric chemical environments. Recurrent random forests were trained to predict organic matter formation from dodecane and toluene precursors, and the partitioning between gas and particle phases. Validation tests show that the random forests perform well without any divergence over 10 days of simulations. The distribution of errors shows that the sampling of initial conditions for the training simulations needs to focus on chemical regimes where SOA production is the most sensitive. Sensitivity tests show that specializing multiple random forests for a specific chemical regime is not more efficient than training a single general random forest for the entire data set. The most important predictors are those providing information about the chemical regime, oxidants levels, and existing organic mass. The choice of predictors is crucial as using too many unimportant predictors reduces the performances of the random forests. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2023 American Geophysical Union (AGU). None eng geoscientificInformation