Calculating global annual methane increases from satellite data using an ensemble dynamic linear model approach
Description
This code provides a minimal working example to recreate Fig. 04 of the accompanying paper 'Zonal variability of methane trends derived from satellite data' (Hachmeister et al., 2024; 10.5194/acp-24-577-2024 ). The figure shows global annual methane increases derived from Sentinel-5P TROPOMI WFMD XCH4 data. The code includes all processing steps from the gridding to the ensemble dynamic linear model fit and the calculation of the global annual methane increases.
The input data can be downloaded here (~100 GB).
Alternatively, the L3 data can be downloaded here (~700 MB) to skip the gridding step.
Notes
This example code uses and contains version 0.1.0 of the dlm_helper package. A recent version of this package can be found on github: https://github.com/JonasHach/dlmhelper
Files
Calculating_global_annual_methane_increases.zip
Files
(43.7 kB)
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Additional details
Related works
- Compiles
- Dataset: 10.5281/zenodo.8185672 (DOI)
- Has part
- Software: 10.5281/zenodo.8186498 (DOI)
- Is supplemented by
- Preprint: 10.5194/egusphere-2023-1680 (DOI)
- Publication: 10.5194/acp-24-577-2024 (DOI)
- Requires
- Dataset: https://www.iup.uni-bremen.de/carbon_ghg/products/tropomi_wfmd/index.php (URL)
References
- Schneising, Oliver, Michael Buchwitz, Jonas Hachmeister, Steffen Vanselow, Maximilian Reuter, Matthias Buschmann, Heinrich Bovensmann, and John P. Burrows. "Advances in Retrieving XCH4 and XCO from Sentinel-5 Precursor: Improvements in the Scientific TROPOMI/WFMD Algorithm." Atmospheric Measurement Techniques 16, no. 3 (February 3, 2023): 669–94. https://doi.org/10.5194/amt-16-669-2023.