Conditional Diffusion-Based Retrieval of Atmospheric CO2 From Earth Observing Spectroscopy

Satellite-based estimates of greenhouse gas (GHG) properties from observations of reflected solar spectra are integral for understanding and monitoring complex terrestrial systems and their impact on the carbon cycle due to their near global coverage.

Known as retrieval, making GHG concentration estimations from these observations is a non-linear Bayesian inverse problem, which is operationally solved using a computationally expensive algorithm called Optimal Estimation (OE), providing a Gaussian approximation to a non-Gaussian posterior.

This leads to issues in solver algorithm convergence, and to unrealistically confident uncertainty estimates for the retrieved quantities. Upcoming satellite missions will provide orders of magnitude more data than the current constellation of GHG observers. Development of fast and accurate retrieval algorithms with robust uncertainty quantification is critical. Doing so stands to provide substantial climate impact of moving towards the goal of near continuous real-time global monitoring of carbon sources and sinks which is essential for policy making.

To achieve this goal, we propose a diffusion-based approach to flexibly retrieve a Gaussian or non-Gaussian posterior, for NASA’s Orbiting Carbon Observatory-2 spectrometer, while providing a substantial computational speed-up over the current operational state-of-the-art.

William R. Keely, Otto Lamminpää, Steffen Mauceri, Sean M. R. Crowell, Christopher W. O’Dell, Gregory R. McGarragh

Comments: Published as a workshop paper in “Tackling Climate Change with Machine Learning”, ICLR 2025 Workshop on Tackling Climate Change with Machine Learning. this https URL
Subjects: Machine Learning (cs.LG); Instrumentation and Methods for Astrophysics (astro-ph.IM)
Cite as: arXiv:2504.17074 [cs.LG] (or arXiv:2504.17074v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2504.17074
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Submission history
From: William Keely
[v1] Wed, 23 Apr 2025 19:46:18 UTC (326 KB)
[v2] Wed, 30 Apr 2025 03:31:24 UTC (326 KB)
https://arxiv.org/abs/2504.17074
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