GOES-East Data Used to Quantify Atmospheric CO2

This study explores the use of the Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-East) to quantify atmospheric carbon dioxide ($CO_2$). Recognizing the limitations of current space-based sensors in providing sufficient spatial and temporal resolution for tracking greenhouse gasses, the researchers developed a single-pixel, physics-guided neural network to estimate dry-air column $CO_2$ mole fraction ($XCO_2$). The model leverages GOES-East's 16 spectral bands, lower tropospheric meteorology data from ECMWF ERA5, MODIS surface reflectance, solar and satellite viewing geometry, and day of year. Training involved collocated GOES-East and OCO-2/OCO-3 observations. Case studies demonstrated the model's ability to observe $XCO_2$ enhancements over urban areas and drawdown over agricultural regions. While acknowledging that the precision of GOES-East derived $XCO_2$ may not match dedicated instruments, the study emphasizes the potential of its contiguous geographic coverage, 10-minute temporal frequency, and multi-year record to observe atmospheric $CO_2$ variability currently unseen from space. This approach offers a cost-effective method for enhancing global greenhouse gas monitoring capabilities by utilizing existing satellite infrastructure. The success of this method hinges on continuous refinement and validation of the neural network model, ensuring the accuracy and reliability of the derived $XCO_2$ estimates. Future research should focus on integrating data from multiple sources to improve the robustness of the monitoring system.

*Transparency Footnote: The AI-generated content in this 'deep_analysis' section is based exclusively on the provided source material. No external data or assumptions were used in its creation.*

_Context: This intelligence report was compiled by the DailyOrbitalWire Strategy Engine. Verified for Art. 50 Compliance._