Contents:

• L96-description.ipynb : Equation and demonstration of the single time-scale model
• L96-two-scale-description.ipynb : Equations and demonstration of the two time-scale model
• L96_model.py : Functions providing tendancies and integrators for the L96 models

• jupyter (for notebooks)
• numpy (used in computations)
• matplotlib (for plots)
• numba (for significant speed up)

If you are starting from scratch, install conda and then:

conda create -n py3 jupyter numpy matplotlib
conda activate py3
jupyter notebook

Arnold, H. M., I. M. Moroz, and T. N. Palmer. “Stochastic Parametrizations and Model Uncertainty in the Lorenz ’96 System.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1991 (May 28, 2013): 20110479. https://doi.org/10.1098/rsta.2011.0479.

Brajard, Julien, Alberto Carrassi, Marc Bocquet, and Laurent Bertino. “Combining Data Assimilation and Machine Learning to Infer Unresolved Scale Parametrization.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (April 5, 2021): 20200086. https://doi.org/10.1098/rsta.2020.0086.

Schneider, Tapio, Shiwei Lan, Andrew Stuart, and João Teixeira. “Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations.” Geophysical Research Letters 44, no. 24 (December 28, 2017): 12,396-12,417. https://doi.org/10.1002/2017GL076101.

Wilks, Daniel S. “Effects of Stochastic Parametrizations in the Lorenz ’96 System.” Quarterly Journal of the Royal Meteorological Society 131, no. 606 (2005): 389–407. https://doi.org/10.1256/qj.04.03.