raghul-parthipan/l96_rnn: v1.0.0

Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model. https://arxiv.org/abs/2203.14814

Parent directory

requirements.txt gives the required packages. Everything here is configured to run without needing a GPU. Of course, if you have a GPU things will be quicker. You can install requirements using a conda env as below:

1. conda create --prefix ./envs python==3.8.5
2. conda activate ./envs
3. pip install -r requirements.txt

In all notebooks, paths need to be updated based on where data is and where you want things to be saved.

create_l96_data

This folder is used for preparing all the training and evaluation data from the "truth" two-level L96 model.

• create_l96_data.ipynb creates the data.
• If, like done here, you can't run a full 50,000 MTU simulation run due to OOM issues, you can run consecutive chunks and then merge them using merge_evaluation_datasets.ipynb.
• weather_analysis_truth_data.ipynb is used to create the data used for weather analysis.

saved_models

Here are all the models, both their training and how they are used to create the results.

RNN

rnn_training.ipynb is for training the model and rnn_results.ipynb is for generating data and calculating hold-out likelihoods. rnn_diagnostics.ipynb contains an example of how likelihood is used to diagnose what can be improved in the RNN model.

Polynomial

Christensen_polynomial_parameterisation.ipynb trains the polynomial model and is used to simulate data and calculate hold-out likelihood.

GAN

gan_training.ipynb is used to train the GAN, and gan_results.ipynb to generate data.

The importance sampler is trained in importance_sampler_for_gan_training.ipynb and the results (i.e. hold-out likelihood) are calculated in importance_sampler_for_gan_training.ipynb

analysis

If due to OOM issues you've needed to create separate chunks of simulation data, merge_simulation_datasets.ipynb is there to merge it.

The notebooks in analysis_notebook are used to create the plots and resulst shown in the Results of the paper.