Tracking Dust Storms and Identifying Source Areas Using MSG Seviri

Traditionally, studies on dust rely on polar-orbiting satellites whose limited temporal coverage does not offer a detailed picture of how each dust storm evolves and changes over time. To address this, our study develops a method to identify and track individual dust storms via hourly images from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on the Eumetsat geostationary orbit satellites. The process involves using the SE-VIRI Dust RGB false color composite to highlight airborne dust in images. We then use the DBSCAN machine learning model to cluster pixels into storms based on their spatial and temporal connectivity. Post-processing enables us to analyze properties such as the storm’s source area and source activity. Through these methods, we gain insights into dust storm sources, emission factors, and seasonal effects, which are key for understanding their impacts on air quality, health, and the environment.