deepC: High-Resolution Carbon Emissions Monitoring via Spatio-Temporal Generative Data Assimilation

High-resolution Greenhouse Gas (GHG) estimation is critical for verifying emissions inventories and informing climate policy. Current state-of-the-art estimates rely on "bottom-up" inventories, which are expensive to maintain, subject to reporting lags, and sensitive to inconsistent data supply chains. Conversely, "top-down" global reanalysis products, such as CarbonTracker, offer high quality but lack the spatial resolution required for actionable local policy, and high accuracy estimation of individual large polluters.

To bridge this gap, we present a deepC, a method that leverages high-resolution simulation data to inform a generative prior while assimilating diverse ground-truth observations. We learn a patch-based diffusion prior from multi-resolution simulations of regional and global carbon transport to model the joint distribution of winds, surface fluxes, column concentrations, and emissions. We then apply a Bayesian posterior formulation to guide the generation process using sparse observations from six satellite missions, ground stations, and coarse global reanalysis. To ensure consistency over large regions, we employ a novel spatio-temporal Markov blanket scheme during posterior sampling, producing carbon emissions estimates at 1km resolution.

We demonstrate the model's efficacy in CONUS and Western Europe, achieving stable emissions trajectories with low error relative to high-quality ground sensor and TCCON data. Early experiments suggest that conditioning the prior on embeddings from remote sensing foundation models significantly improves generalization to unseen domains. Furthermore, the model is robust to distribution shifts -- maintaining coherence under simulated future background CO₂​ levels. Finally, our approach yields well-calibrated uncertainty quantification at high inference speeds with ensemble generation, highlighting its potential for rapid, transparent emissions stocktaking, and lag-free policymaking.