Whether for weather forecasting, climate modeling, or the analysis of extreme events — understanding clouds is key to better grasping atmospheric processes.
I supported our partner Geo Engine in developing an AI-based processing pipeline that automatically classifies meteorological satellite data from the MSG series (EUMETSAT) by cloud type.
The focus was on preprocessing the raw data and creating suitable training datasets to prepare them for use in Convolutional Neural Networks (CNNs). To achieve this, the extensive MSG data were spatially and temporally segmented to make them usable for model training and validation. In total, ten years of European MSG data with a temporal resolution of 15 minutes (around 35,000 images) were processed.
The resulting pipeline can now apply cloud classifications to new MSG imagery in real time.