Extracting XCO2 NASA data with XCODEX: A Python package designed for data extraction and structuration

Monitoring carbon dioxide (CO 2 ) is an essential part of studying climate change and its impacts. NASA has played a key role in global CO 2 monitoring using the OCO-2 satellite. With this, daily records of column averaged CO 2 dry air mole fraction (XCO 2 ) are made available on the GES DISC platform for the study of sources and sinks of CO 2 in the atmosphere. However, these records are presented in netCDF (nc4), which is complex and multidimensional files, requiring specialized software and programming knowledge for the efficient manipulation of data. To offer a tool, whose advantages extend from obtaining the files to the extraction and organization of the data, in a spatio-temporal series, in addition to identifying possible missing data in regions of interest, the XCODEX package (XCO 2 Daily EXtractor) was developed in Python language. The package was applied by comparing data from the Mauna Loa observatory and a simple linear regression model was adjusted. The adjusted model showed high correlation (adjusted R² = 0.983), high accuracy (RMSE = 0.659 ppm) and small trend (SE = 0.025 ppm). We also apply to 10 locations around the globe. The XCODEX package, faced with the complexity of handling nc4 files, was able to download and extract daily data from XCO 2 in a correct, intuitive and simple way, being available for free in the PyPi repository.