https://github.com/ray-chew/probabilistic_forecasting_examples

Reproduced the examples and results from the textbook "Probabilistic Forecasting and Bayesian Data Assimilation" in Python
https://github.com/ray-chew/probabilistic_forecasting_examples

bayesian-optimization data-assimilation probabilistic-forecasting textbook-example

Last synced: over 1 year ago
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Reproduced the examples and results from the textbook "Probabilistic Forecasting and Bayesian Data Assimilation" in Python

• Host: GitHub
• URL: https://github.com/ray-chew/probabilistic_forecasting_examples
• Owner: ray-chew
• License: apache-2.0
• Created: 2024-03-19T12:34:51.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2024-03-23T23:45:43.000Z (about 2 years ago)
• Last Synced: 2025-01-11T13:54:41.688Z (over 1 year ago)
• Topics: bayesian-optimization, data-assimilation, probabilistic-forecasting, textbook-example
• Language: Jupyter Notebook
• Homepage:
• Size: 9.4 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
Examples in Python from the textbook [*Probabilistic Forecasting and Bayesian Data Assimilation*](https://www.math.uni-potsdam.de/~sreich/probabilisticForecastingAndBayesianDataassimilation.html)

In November 2018, I read this textbook from cover to cover and reproduced the examples to gain an understanding of data assimilation.

---

### Todo:

1. Check mean values for chap5ex17.

2. Complete Chapter 7 example 13

    1. Implement ESRF filter

    2. Fix the implementation of the SIR

    3. Fix ETPF 3d residual calculations

    4. Use a FORTRAN subroutine for the implicit solver

3. Check what is wrong with chapter 8 example 5.

4. Chapter 8 example 9: The matrix PP is introduced to make sure that the mean of the generated ensemble spread does not change (sum over all the ensemble members at a given spatial grid point equals zero). Is this true? Think about it.

5. Might want to implement chap8ex13 and chap8ex21 as a challenge.