Metadata-Version: 2.1
Name: sagea
Version: 0.1.0a6
Summary: satellite gravity post-processing and error assessment
Home-page: https://github.com/NCSGgroup/SaGEA
Author: Shuhao Liu
Author-email: liushuhao@hust.edu.cn
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.9.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy==2.0.2
Requires-Dist: scipy==1.13.1
Requires-Dist: h5py==3.14.0
Requires-Dist: pandas==2.3.0
Requires-Dist: tqdm==4.67.1
Requires-Dist: netCDF4==1.7.2
Requires-Dist: geopandas==1.0.1
Requires-Dist: shapely==2.0.7
Requires-Dist: pyproj==3.6.1


# 1. Introduction
The level-2 time-variable gravity fields obtained from Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) mission are widely used in multi-discipline geo-science studies. However, the post-processing of those gravity fields to obtain a desired signal is rather challenging for users that are not familiar with the level-2 products. In addition, the error assessment/quantification of those derived signals, which is of increasing demand in science application, is still a challenging issue even among the professional GRACE(-FO) users. In this effort, the common post-processing steps and the assessment of complicated error (uncertainty) of GRACE(-FO), are integrated into an open-source, cross-platform and Python-based toolbox called SAGEA (SAtellite Gravity Error Assessment). With diverse options, SAGEA provides flexibility to generate signal along with the full error from level-2 products, so that any non-expert user can easily obtain advanced experience of GRACE(-FO) processing. Please contact Shuhao Liu (liushuhao@hust.edu.cn) and Fan Yang (fany@plan.aau.dk) for more information.

When referencing this work, please cite:
> Liu, S., Yang, F., & Forootan, E. (2025). SAGEA: A toolbox for comprehensive error assessment of GRACE and GRACE-FO based mass changes. Computers & Geosciences, 196, 105825. https://doi.org/10.1016/j.cageo.2024.105825

