Awesome

WeatherDiff

Description

This Code4Earth challenge explores the potential of Diffusion Models for weather prediction, more specificially we test it on the WeatherBench benchmark data set.

This repository contains functions to benchmark the diffusion models developed in diffusion-models-for-weather-prediction. It builds on existing code from WeatherBench.

Roadmap

This repository is part of a ECMWF Code4Earth Project, which takes place between May 1 2023 and September 20 2023.

Installation

Repository:

• The repository is formatted with black formatter and also uses pre-commit

  • make sure that pre-commit package is installed or pip install pre-commit
  • to set up the git hook scripts pre-commit install.

• The main repository has two submodules that can be installed as follows:

Clone the main repository. Clone the <subodules> | Make sure you have access to them. Then:

1. git submodule init
2. git submodule update

Data download:

• Our code requires the WeatherBench to be downloaded as described in this repository. We tested the 5.625° and 2.8125° resolutions.

Setup:

• Setting up conda environments. We create 3 environments, the requirements of each of them are contained in a .yml file. Run conda env create -f <env_config_file> to create each environment.
  • env_data.yml creates an environment WD_data that is used to preprocess the data
  • env_model.yml creates an environment WD_model that is used to train and make prediction with machine learning models.
  • env_eval.yml creates an environment WD_eval with packages required to analyse and plot results.
• The workflow requires paths being set for a few different directories. These paths are specified in the config/paths/ directory and make the following choices:
  • dir_WeatherBench: Directory the weatherBench dataset was downloaded to.
  • dir_PreprocessedDatasets: Preprocessed datasets get stored here
  • dir_SavedModels: Checkpoints and tensorboard logs are stored here
  • dir_HydraConfigs: When running jobs, the selected configuration files are logged here.
  • dir_ModelOutput: Predictions with the ML models get saved here.

Workflow:

The workflow to train and predict with the diffusion models is as follows:

• Dataset creation: Creating a preprocessed dataset from the raw WeatherBench dataset. This can be obtained with s1_write_dataset.py and submit_script_1_dataset_creation.sh (if submitting jobscripts is required)
  • configurations for the dataset creation process and other parameter choices in the process are managed with hydra. The name of a configuration ("template") has to be selected when running the script, e.g. python s1_write_data.py +template=<name_of_template>. The corresponding file <name_of_template>.yaml should be contained in the config/template directory.
  • preprocessed datasets get saved as zarr files in the dir_PreprocessedDirectories/ directory.
• Training a model: Select the appropriate script (e.g. s2_train_pixel_diffusion.py). Configuration choices are made in the config/train.yaml file, and experiment specific choices (model architecture, dataset, ...) are listed in the files in the /config/experiment directory. A experiment name has to be given, analogously the dataset creation. A model can for example be trained by python s2_train_pixel_diffusion.py +experiment=<name_of_experiment>. The selected configuration, including the experiment get logged to dir_HydraConfigs.
  • The training progress can be monitored with tensorbaord.
• Once the training is finished, predictions can be written with the trained models. Selecting an appropriate script (e.g. s3_write_predictions_conditional_pixel_diffusion.py), predictions can be made as follows python s3_write_predictions_conditional_pixel_diffusion.py +data.template=<name_of_template> +experiment=<name_of_experiment> +model_name=<name_of_the_model_run> n_ensemble_members=<number_of_ensemble_members>. Here <name_of_experiment> and <name_of_experiment> are the choices made when creating the employed dataset and training the model. By default, <name_of_the_model_run> should be the time that the model run was started. To find this information, have a look at the logged configurations for training in dir_HydraConfigs/training. As the name suggests, <number_of_ensemble_members> determines how many ensemble predictions should be produces simultaneously. The predictions and ground truth get rescaled and saved in .nc files in dir_ModelOutput. They can be opened with xarray, and contain data of the following dimensionality: (ensemble_member, init_time, lead_time, lat, lon). init_time is the "starting/initialization" time of the forecast, and lead_time specifies how far one wants to predict into the future.

Contributing

Script on guidelines for contributions will be added in the future.

Authors and acknowledgment

Participants:

• Mohit Anand
• Jonathan Wider

Mentors:

• Jesper Dramsch
• Florian Pinault

License

This project is licensed under the Apache 2.0 License. The submodules contain code from external sources and are subject to the licenses included in these submodules.

Project status

Code4Earth project finished.