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

2025-04-23

AI Summary

Inverting atmospheric CO₂ concentrations from satellite remote sensing data constitutes a nonlinear Bayesian inverse problem; existing optimal estimation methods suffer from high computational cost, poor convergence, and reliance on Gaussian posterior assumptions—limiting real-time carbon source/sink monitoring. Method: This paper introduces conditional diffusion models to atmospheric constituent inversion for the first time, integrating physics-driven forward radiative transfer constraints with denoising score matching and gradient-guided sampling. Contribution/Results: The proposed framework ensures physical consistency while enabling flexible posterior modeling—whether Gaussian or non-Gaussian. Evaluated on OCO-2 data, it achieves over 10× speedup versus conventional methods, yields uncertainty quantification better aligned with the true posterior structure, and significantly improves inversion accuracy, robustness, and real-time capability. This work establishes a novel paradigm for dynamic global carbon cycle monitoring.

Technology Category

Computer Vision: Diffusion Models for VisionMachine Learning: Calibration & Uncertainty QuantificationCognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoTSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

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.

Problem

• Estimating atmospheric CO2 from satellite spectra accurately
• Overcoming computational inefficiency in current retrieval algorithms
• Improving uncertainty quantification for greenhouse gas monitoring

Innovation

• Diffusion-based approach for CO2 retrieval
• Flexible Gaussian or non-Gaussian posterior estimation
• Computationally faster than Optimal Estimation