Dataset, scripts, and figures for the article: "Machine-learning emulation of SBDART spectral transmittance and reflectance under turbid and cloudy skies: physical interpretability and rapid sensitivity analysis"

General description This repository brings together the data, Python scripts, tables, and figures used for the study devoted to the machine-learning emulation of spectral transmittance and reflectance simulated by SBDART under turbid and cloudy skies. The repository includes in particular: • the database generated from large-scale SBDART radiative simulations; • the scripts for data generation, post-processing, statistical analysis, and machine learning; • the descriptive tables used in the methodology; • the main figures of the article; • the SHAP interpretability figures; • the rapid sensitivity figures in the aerosol–cloud parameter space.