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AI model quantifies CO2 using satellite weather data

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model quantifies CO2 using satellite weather data

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

  1. arXiv cs.LG TIER_1 English(EN) · Aaron Sonabend-W, Sean Campbell, John Platt, Christopher Van Arsdale, Anna M. Michalak ·

    Quantification of atmospheric carbon dioxide from the Geostationary Operational Environmental Satellite (GOES East)

    arXiv:2605.23991v1 Announce Type: cross Abstract: There is a growing urgency to track greenhouse gasses with the resolution, precision and accuracy needed to support independent verification of $CO_2$ fluxes at local to global scales. The current generation of space-based sensors…

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