Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO₂ Emissions.

ABSTRACT: Satellite data provides essential insights into the spatiotemporal distribution of CO₂ concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulat

SUBMITTER: Jeong S 

PROVIDER: S-EPMC11912316 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature