Grace Level 3 Python package.
Project description
This package processes Earth gravity field data—provided as spherical harmonic coefficients—into gridded, domain-specific datasets. It also includes uncertainty estimation and the generation of regional mean time series.
License and Citation
geogravL3 is distributed under the GNU General Public License v3.0 or later (GPL-3.0-or-later).
When using the software, please cite:
Boergens, E., Rabe, D., Charly, A., Wilms, J., Scheffler, D. (2025): geogravL3 - a Python Package for Processing Earth Gravity Field Data. GFZ Data Services. https://doi.org/10.5880/GFZ.DQTO.2025.002
Documentation
The documentation with details on the installation, usage, and configuration can be found at https://grace_l3.git-pages.gfz-potsdam.de/geogravl3/doc/.
Description
geogravL3 provides the processing pipeline from Level-2 data (spherical harmonic coefficients) to domain-specific gridded Level-3 data and the computation of regional mean time series.
Input:
Spherical harmonic coefficients, provided as one file per time step, in either ICGEM (*.gfc), YAML (without file ending), or SINEX format (*.snx).
Configuration file steering the processing steps. Either in Json format (*.json) or XML format (*.xml).
Output
NetCDF files (*.nc) per domain containing gridded data
CSV files (*.csv) per domain containing mean regional time series
Processing Details
Details of the processing steps, or their omission, are govern by the configuration file. Details on the configuration file is provided at https://grace_l3.git-pages.gfz-potsdam.de/geogravl3/doc/config.html.
Domain-Independent Processing Steps
Removal of the mean field
Filtering of spherical harmonic coefficients: - DDK - Gaussian filter - VDK (requires SINEX input)
Replacement of C20 and C30 with external data
Subtraction of the GIA model
Estimation and insertion of geocentre motion coefficients (C10, C11, S11)
Subtraction of the 161-day aliased signal
Domain-Specific Processing Steps
Land — Terrestrial Water Storage (TWS)
Spherical harmonic synthesis
Masking of the ocean
Uncertainty estimation based on open-ocean noise
Ocean — Ocean Bottom Pressure (OBP)
Spherical harmonic synthesis (land and ocean domains only)
Separation of the ocean signal into: - Barystatic sea level, estimated via the sea-level equation - Residual circulation
Uncertainty estimation based on residual time-series signals
Ice Sheets — Greenland and Antarctica
Gridded data estimated using the sensitivity-kernel approach
Uncertainty estimation based on residual time-series signals
Status
See also the latest coverage report and the pytest HTML report.
History / Changelog
You can find the protocol of recent changes in the geogravL3 package here.
Credits
This software package was developed under ESA contract 4000145266/24/NL/SC – NGGM and MAGIC End-to-End Mission Performance Evaluation Study.
The scientific and methodological development was led by Eva Boergens (eva.boergens@gfz.de). Martin Horwath (martin.horwath@tu-dresden.de) contributed the ice-processing methodology. A complete list of contributors—covering both software development and the heritage code that was translated into Python as part of this project—is available here.
The FERN.Lab (fernlab@gfz.de) contributed to the development, documentation, continuous integration, and testing of the package. This package was created using Cookiecutter and the fernlab/cookiecutter-py-package project template.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file geogravl3-1.2.3.tar.gz.
File metadata
- Download URL: geogravl3-1.2.3.tar.gz
- Upload date:
- Size: 115.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.14.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
313a9eed80dfc16174d607e5b234051b16d4f1d2e2c0244554c8bf8c4ea590f8
|
|
| MD5 |
b7d2725f7476d5d4cadff4f38cf232c7
|
|
| BLAKE2b-256 |
f763061f3fb55b166e0f774119cff5637c765acbbb75079180d76bdf3597edb8
|