Random Forest–Predicted 1 km² Monthly Surface Ozone over Sub-Saharan Africa, 2005–2025

Le résumé fourni par la source

Monthly 1 km² surface ozone predictions from the Random Forest model developed in “Machine Learning-Based Prediction of Monthly Surface Ozone Over Sub-Saharan Africa Using Satellite-Derived Precursors, Meteorology, and Surface Measurements.” The model was trained primarily on INDAAF observations from predominantly rural and semi-savannah environments, with a few sites near urban centers. Therefore, predicted ozone magnitudes in urban areas should be interpreted with caution. The predictions are provided on a 1 km² grid; however, this fine prediction grid does not imply 1 km² native spatial information for all predictors, as some input datasets have coarser native spatial resolutions. Consequently, the ability of the dataset to resolve fine-scale urban ozone gradients may be limited, and the machine-learning model may smooth extreme ozone concentrations. For details on the model, training data, predictors, validation, and methodology, please refer to the accompanying paper.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.