arXiv Machine Learning By Chiara Zugarini, Cristina Sgattoni, Luca Sgheri

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

Read the original on arXiv Machine Learning →

arXiv: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.

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arXiv AI
Jun 19

SIMBA: ABidirectional Retrieval Forward Simulation Framework for Modeling FY-4A GIIRS Hyperspectral Infrared Radiances Toward NWP Applications

arXiv:2606. 19943v1 Announce Type: cross Abstract: Hyperspectral infrared observations are an important data source for numerical weather prediction (NWP) because they provide rich information on the vertical structure of atmospheric temperature and humidity.

By Jingdong Shen, Fu Wang*, Qifeng Lu, Hao Huang, Chunqiang Wu, Chi Yang, Xiaofang Liu
arXiv Machine Learning
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Urban Heat MiniCubes: An AI-Ready dataset for urban heat research

arXiv:2606. 11534v1 Announce Type: cross Abstract: Urban heat is amplified by impermeable surfaces and heterogeneous built environments, yet street-level variability remains difficult to quantify because multi-sensor observations are rarely available in consistent, analysis-ready form at the necessary spatiotemporal scales.

By Jonathan Starfeldt, Maria J. Molina, Alexander Kerr, Adam Yang, Thomas R. H. Holmes, Christopher R. Hain