Unveiling diurnal aerosol layer height variability from space using deep learning

The vertical distribution of aerosols is crucial for extensive climate and environment studies but is severely constrained by the limited availability of ground-based observations and the low spatiotemporal resolutions of Lidar satellite measurements. Multi-spectral passive satellites offer the potential to address these gaps by providing large-scale, high-temporal-resolution observations, making them a promising tool for enhancing current aerosol vertical distribution data. However, traditional methods, which rely heavily on physical assumptions and prior knowledge, often struggle to deliver robust and accurate aerosol vertical profiles. Thus, we develop a novel retrieval framework that combines two advanced deep-learning models, locally-feature-focused Transformer and globally-feature-focused Fully Connected Neural Network (FCNN), referred to as TF-FCNN, to estimate hourly aerosol distributions at different heights (i.e., 0.01–1 km, 1–2 km, and 2–3 km) with 2-km spatial resolution, using multi-source satellite data, including Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Himawari-8 and Moderate Resolution Imaging Spectroradiometer (MODIS). This hybrid framework is thoroughly analyzed using an eXplainable Artificial Intelligence (XAI)-based SHapley Additive exPlanations (SHAP) approach, which reveals that shortwave bands and brightness temperature are the most influential features, contributing approximately 63 % to the model predictions. Validation results demonstrate that the model provides reliable hourly aerosol vertical distributions across different heights in Australia, achieving high overall sample-based cross-validation coefficients of determination (CV-R²) ranging from 0.81 to 0.90 (average = 0.88). Our hourly retrievals indicate higher aerosol loadings at lower altitudes (0.01–1 km) than higher ones (1–2 km and 2–3 km) in most areas, likely due to significant anthropogenic and natural emissions from the ground. Furthermore, we observe substantial increases in aerosol concentrations over time and enhanced diurnal variations across altitudes during highly polluted cases, including urban haze and wildfires. These unique insights into the spatial distribution of aerosol vertical layers are crucial for effective air pollution control and management.