Machine Learning for Cloud Detection in IASI Measurements: A Data-Driven SVM Approach with Physical Constraints

תקציר מקורי באנגליתarXiv:2508.10120v2 Announce Type: replace-cross Abstract: Cloud detection is fundamental for the interpretation and operational exploitation of hyperspectral infrared sounders, yet the capability of infrared radiances alone to provide reliable cloud information remains insufficiently assessed. We introduce the Cloud Identification Support Vector Machine (CISVM), a supervised framework for global clear and cloudy classification from Infrared Atmospheric Sounding Interferometer (IASI) Level 1C observations. The analysis spans four seasons and compares radiances and brightness temperatures, alternative spectral reductions, and stratifications by surface type and climate zone. Using AVHRR-derived IASI cloud labels for training, the classifier operates exclusively on hyperspectral infrared radi