AI model quantifies CO2 using satellite weather data

Researchers have developed a physics-guided neural network capable of quantifying atmospheric carbon dioxide ($XCO_2$) using data from the Geostationary Operational Environmental Satellite (GOES-East). This model leverages the satellite's high temporal and spatial resolution, along with meteorological and surface reflectance data, to estimate $XCO_2$. While not as precise as dedicated instruments, the GOES-East derived data offers a unique view of $CO_2$ variability over large geographic areas with frequent updates, showing potential for observing urban enhancements and agricultural drawdowns. AI

IMPACT This AI-driven approach could enhance climate monitoring by providing more frequent and spatially comprehensive carbon dioxide data.

RANK_REASON The cluster contains an academic paper detailing a new methodology for scientific research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• neural network
• carbon dioxide
• ECMWF ERA5
• GOES-East
• OCO-2/OCO-3

• paper
• product

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