Learning Aerosol Particle Size by Embedding Airmass Historical Pathways in Multi-Model Deep Learning

Particle number size distribution (PNSD) is a cornerstone property of atmospheric aerosols and is essential for quantifying aerosol–cloud interactions. Although PNSD over continents has been studied comprehensively in the past decades via an extensive in-situ observational network worldwide, estimating marine PNSD (where clouds are more susceptible to aerosol and exert larger climate forcing) remains highly uncertain because of sparse observations, and PNSD varies strongly in space and time during the transport of air parcels. Here, we introduce a framework that integrates air-parcel location history with co-located aerosol, cloud, meteorological, and gas-phase information into deep learning (DL) approaches to constrain aerosol size distributions better. We employ three DL models: two Long Short-Term Memory (LSTM) models and one Bidirectional Long Short-Term Memory (BiLSTM) model. Evaluated against measured PNSD at the Cape Verde Atmospheric Observatory (CVAO) in the central Atlantic over 10 years, all three models achieve a mean fractional error (MFE) below 0.17. We further transfer the well-trained models to Ascension Island (ASI) in the South Atlantic; the predicted PNSD agrees with measurements with an MFE below 0.14, demonstrating strong model transferability. These DL models can therefore be used to project PNSD in remote marine environments. We also assess feature importance across the three models using the SHapley Additive exPlanations (SHAP) method. The models yield inconsistent interpretations of input features, suggesting they do not capture the mechanisms of aerosol formation pathways during transport. We therefore caution that, when using deep learning for mechanistic interpretation, multiple models should be applied for cross-validation to ensure the stability and reproducibility of the results.