Extracting XCO₂-NASA data with XCODEX: a Python package designed for data extraction and structuration.

Area of Science:

• Earth and Environmental Sciences
• Atmospheric Science
• Climate Science

Background:

• Accurate atmospheric carbon dioxide (XCO2) monitoring is crucial for climate change research.
• NASA's OCO-2 satellite provides vital XCO2 data, but its netCDF4 format hinders efficient data utilization.
• Researchers face challenges in extracting and organizing OCO-2 data for analysis.

Purpose of the Study:

• To develop a user-friendly Python package (XCODEX) for automating the retrieval and structuring of OCO-2 XCO2 measurements.
• To streamline data processing for multiple geographic locations and minimize missing data.
• To facilitate focused research on analytical insights by reducing data preprocessing burdens.

Main Methods:

• Developed XCODEX, a Python package to automate daily XCO2 data retrieval from OCO-2.
• Implemented data processing steps including variable definition, date matching, and targeted data extraction.
• Validated XCODEX results against ground-based TCCON and Mauna Loa observations.

Main Results:

• XCODEX demonstrated high accuracy and reliability, with adjusted R2 > 0.97 and RMSE < 1 ppm compared to validation data.
• Regional analysis revealed significant XCO2 differences across 10 global sites.
• A consistent rising trend of approximately 2.4 ppm/year in atmospheric CO2 was observed, aligning with global trends.

Conclusions:

• XCODEX effectively automates OCO2 data processing, making it accessible in Pandas DataFrames.
• The package empowers researchers by simplifying data handling for global carbon cycle studies.
• XCODEX contributes to improved environmental monitoring and climate modeling efforts.