ray-chew/probabilistic_forecasting_examples

Reproduced the examples and results from the textbook "Probabilistic Forecasting and Bayesian Data Assimilation" in Python

Examples in Python from the textbook Probabilistic Forecasting and Bayesian Data Assimilation

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

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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.