Skip to main content

Python tools for obtaining and working with model synthetic spherical harmonic coefficients for comparing with data from the NASA/DLR GRACE and NASA/GFZ GRACE Follow-on missions

Project description

License Documentation Status PyPI commits-since zenodo

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

Resources

Dependencies

References

I. Velicogna, Y. Mohajerani, G. A, F. Landerer, J. Mouginot, B. Noël, E. Rignot, T. C. Sutterley, M. van den Broeke, J. M. van Wessem, and D. Wiese, “Continuity of ice sheet mass loss in Greenland and Antarctica from the GRACE and GRACE Follow‐On missions”, Geophysical Research Letters, 47, (2020). doi: 10.1029/2020GL087291

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

Download

The program homepage is:
A zip archive of the latest version is available directly at:

Disclaimer

This project contains work and contributions from the scientific community. 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. It is provided here for your convenience but with no guarantees whatsoever.

License

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.

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

model_harmonics-1.1.2.tar.gz (232.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

model_harmonics-1.1.2-py3-none-any.whl (400.5 kB view details)

Uploaded Python 3

File details

Details for the file model_harmonics-1.1.2.tar.gz.

File metadata

  • Download URL: model_harmonics-1.1.2.tar.gz
  • Upload date:
  • Size: 232.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.3

File hashes

Hashes for model_harmonics-1.1.2.tar.gz
Algorithm Hash digest
SHA256 cc35f1f42e4e3f4f2b12dc6910e5d50519a8bcca0ca3c4b03add05f71f92c6d8
MD5 cb061a8765ba5132537d491b43e25511
BLAKE2b-256 4375d1b4beb370f0f82659681a0e27b1575627263df557ca3d379e2d49719ec9

See more details on using hashes here.

File details

Details for the file model_harmonics-1.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for model_harmonics-1.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 dd257f40b5f32fafca1293b8c06e9f7cdce9f2d5343a886651a877024868bc3c
MD5 309885a19fdbd983549a57c3ce9c09d6
BLAKE2b-256 292c2cccb16ef8023c0eba10ccc0460ab8a8f1771e196e6ccd58fba21eb74f53

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page