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

TL;DR

This paper presents a data-driven SVM method with physical constraints for cloud detection in IASI satellite measurements, achieving high accuracy and consistency with MODIS data, useful for climate and weather applications.

Contribution

The study introduces CISVM, a novel SVM-based cloud detection approach that incorporates physical constraints and feature selection for improved infrared radiance classification.

Findings

Achieved 88.3% agreement with reference labels.

Demonstrated robustness and efficiency for operational cloud classification.

Showed strong consistency with MODIS cloud masks.

Abstract

Cloud detection is essential for atmospheric retrievals, climate studies, and weather forecasting. We analyze infrared radiances from the Infrared Atmospheric Sounding Interferometer (IASI) onboard Meteorological Operational (MetOp) satellites to classify scenes as clear or cloudy. We apply the Support Vector Machine (SVM) approach, based on kernel methods for non-separable data. In this study, the method is implemented for Cloud Identification (CISVM) to classify the test set using radiances or brightness temperatures, with dimensionality reduction through Principal Component Analysis (PCA) and cloud-sensitive channel selection to focus on the most informative features. Our best configuration achieves 88.30 percent agreement with reference labels and shows strong consistency with cloud masks from the Moderate Resolution Imaging Spectroradiometer (MODIS), with the largest discrepancies in polar regions due to sensor differences. These results demonstrate that CISVM is a robust, flexible, and efficient method for automated cloud classification from infrared radiances, suitable for operational retrievals and future missions such as Far infrared Outgoing Radiation Understanding and Monitoring (FORUM), the ninth European Space Agency Earth Explorer Mission.