This perspective presents a lightweight probabilistic machine learning framework for satellite-based trace gas retrievals with uncertainty quantification.Using likelihood-based loss functions and snapshot ensembles wi...This perspective presents a lightweight probabilistic machine learning framework for satellite-based trace gas retrievals with uncertainty quantification.Using likelihood-based loss functions and snapshot ensembles without requiring uncertainty labels,it achieves scalable,fast,and reliable XCO_(2) retrieval for OCO-2,matching the physics-based method at a fraction of the computational cost.展开更多
基金supported by the National Natural Science Foundation of China(grants nos.52276077 and 52120105009).
文摘This perspective presents a lightweight probabilistic machine learning framework for satellite-based trace gas retrievals with uncertainty quantification.Using likelihood-based loss functions and snapshot ensembles without requiring uncertainty labels,it achieves scalable,fast,and reliable XCO_(2) retrieval for OCO-2,matching the physics-based method at a fraction of the computational cost.
