Python tools for obtaining and working with model synthetic spherical harmonic coefficients for comparing with data from the the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) and the NASA/GFZ Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions
These are extension routines for the set of gravity-toolkit tools
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For more information: see the documentation at model-harmonics.readthedocs.io
From PyPI:
python3 -m pip install model-harmonicsTo include all optional dependencies:
python3 -m pip install model-harmonics[all]Using conda or mamba from conda-forge:
conda install -c conda-forge model-harmonicsmamba install -c conda-forge model-harmonicsDevelopment version from GitHub:
python3 -m pip install git+https://github.com/tsutterley/model-harmonics.gitAlternatively, you can use Pixi for a streamlined workspace environment:
- Install Pixi following the installation instructions
- Clone the project repository:
git clone https://github.com/tsutterley/model-harmonics.git- Move into the
model-harmonicsdirectory
cd model-harmonics- Install dependencies and start a shell:
pixi shellThis will automatically create the environment, install all dependencies, and open a bash shell for executing programs.
- NASA GRACE mission site
- NASA GRACE-FO mission site
- JPL GRACE Tellus site
- JPL GRACE-FO site
- UTCSR GRACE site
- GRACE at the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC)
- GRACE at the GFZ Information System and Data Center
- cdsapi: Python client libraries for the CDS Web API
- ecmwf-datastores-client: programmatic access to the CDS, ADS and EWDS data catalogues
- fiona: Python wrapper for vector data access functions from the OGR library
- geoid-toolkit: Python utilities for calculating geoid heights from static gravity field coefficients
- gravity-toolkit: Python tools for working with GRACE/GRACE-FO data
- h5py: Python interface for Hierarchal Data Format 5 (HDF5)
- netCDF4: Python interface to the netCDF C library
- pyproj: Python interface to PROJ library
- shapely: PostGIS-ish operations outside a database context for Python
- scikit-learn: Machine Learning in Python
The program homepage is:
https://github.com/tsutterley/model-harmonics
A zip archive of the latest version is available directly at:
https://github.com/tsutterley/model-harmonics/archive/main.zip
This package includes software developed at the University of California at Irvine (UCI), the NASA Jet Propulsion Laboratory (JPL), NASA Goddard Space Flight Center (GSFC) and the University of Washington Applied Physics Laboratory (UW-APL). This program is not sponsored or maintained by the Universities Space Research Association (USRA), the Center for Space Research at the University of Texas (UTCSR), the Jet Propulsion Laboratory (JPL), the German Research Centre for Geosciences (GeoForschungsZentrum, GFZ) or NASA. The software is provided here for your convenience but with no guarantees whatsoever.
This project contains work and contributions from the scientific community. If you would like to contribute to the project, please have a look at the contribution guidelines, open issues and discussions board.
T. C. Sutterley, I. Velicogna, and C.-W. Hsu, "Self-Consistent Ice Mass Balance and Regional Sea Level From Time-Variable Gravity", Earth and Space Science, 7, (2020). doi: 10.1029/2019EA000860
T. C. Sutterley and I. Velicogna, "Improved estimates of geocenter variability from time-variable gravity and ocean model outputs", Remote Sensing, 11(18), 2108, (2019). doi: 10.3390/rs11182108
The content of this project is licensed under the Creative Commons Attribution 4.0 Attribution license and the source code is licensed under the MIT license.