Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
arXiv:2606.17413v1 Announce Type: new Abstract: Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget. NASA's Orbiting Carbon Observatory-2 (OCO-2) estimates column-averaged dry-air mole fractions of CO2 (XCO2) using high-resolution spectra. However, current operational retrieval algorithms are computationally expensive and do not properly quantify uncertainties. We present a novel deep learning framework that addresses these challenges. Due to the difficulties of ground-truth data for real satellite observations, we develop and valid
The increasing availability of high-resolution satellite data and advancements in deep learning techniques are converging to make more efficient and accurate climate monitoring possible.
Improved, faster, and more certain measurement of atmospheric CO2 levels is crucial for effective climate modeling, policy-making, and understanding the global carbon budget with greater precision.
The computational cost and uncertainty quantification of CO2 retrieval from satellite data can be significantly reduced, allowing for more dynamic and precise monitoring capabilities.
- · Climate scientists
- · Environmental agencies
- · Deep learning researchers
- · Satellite data providers
- · Current computationally expensive CO2 retrieval methods
- · Organizations relying on less precise carbon measurement data
More accurate and timely global carbon emissions data becomes available to researchers and policymakers.
Enhanced data can inform more precise carbon accounting, potentially influencing carbon markets and international climate agreements.
Improved monitoring could expose regional emissions discrepancies, leading to geopolitical pressure and refined accountability for climate targets.
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