PyGLDA Installation and Practical Examples

PyGLDA is a large-scale computing software intended to run on a HPC (high performance cluster). For a demonstration related to this summer school, we made PyGLDA run on personal computers, regardless of whether the operating system is Windows or Linux. However, due to the limited computational resources on the personal computer, the practical exercises provided below may not be realistic. However, one can still use these exercises to explore our PyGLDA toolbox, and if interested, one can install PyGLDA on your own cluster to unlock its full potential.Some Jupyter notebooks are provided to start the journey, which are available in both our Github repository (https://github.com/AAUGeodesyGroup/PyGLDA), and in the Drive (https://drive.google.com/drive/folders/1ZDU9oUnpUaTC9H9Bv4spZa8-_z9d-9Ib?usp=drive_link). These notebooks include:1. README.md: An installation guideline for PyGLDA2. Guide.ipynb: under the folder of ’./demo/’. This is a step-by-step guide for a complete and general data assimilation workflow.3. Visualization.ipynb: under the folder of ’./demo/’. This is a guide for visualizing the main results of data assimilation.4. Exercise 1.ipynb: under the folder of ’./demo/’. An exercise for testing the impact of different perturbation strategies.5. Exercise 2.ipynb: under the folder of ’./demo/’. An exercise of switching the GRACE-Mascon solution to the GRACE-SH solution.6. Exercise 3.ipynb: under the folder of ’./demo/’. An exercise to test the impact of different ensemble sizes.7. Exercise 4.ipynb: under the folder of ’./demo/’. An exercise to demonstrate the implementation of the DA in a desired region.Please review these notebooks for the details.