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TorNet

Software to work with the TorNet dataset as described in the paper A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data

Updates (7/9/24)

  • The TorNet dataset has been updated to version 1.1. This update fixes a small number of incorrectly labeled frames in v1 of the dataset, and fixes the event and episode IDs of the warning categoies. Version 1.1 also provides the tornado start and end times in the metadata. We recommend re-downloading the newer version of the data using the links below.

  • The code and pretrained models are now compatible with keras 3.0. Users can now select their deep learning backend from tensorflow, torch, or jax. Backend-agnostic data loaders are also provided. Read more about this library at keras's website. Users of tf.keras should use the tf_keras branch of this repo.

  • The pretrained CNN model is now available on huggingface (tornet-ml/tornado_detector_baseline_v1). Instructions for downloading and using the pre-trained model can be found in models/README.md and in the VisualizeSamples.ipynb notebook.

Alt text

Downloading the Data

The TorNet dataset can be downloaded from the following location:

Zenodo

TorNet is split across 10 files, each containing 1 year of data. There is also a catalog CSV file that is used by some functions in this repository.

If downloading through your browser is slow, we recommend downloading these using zenodo_get (https://gitlab.com/dvolgyes/zenodo_get).

After downloading, there should be 11 files, catalog.csv, and 10 files named as tornet_YYYY.tar.gz. Move and untar these into a target directory, which will be referenced using the TORNET_ROOT environment variable in the code. After untarring the 10 files, this directory should contain catalog.csv along with sub-directories train/ and test/ filled with .nc files for each year in the dataset.

Setup

Basic python requirements are listed in requirements/basic.txt.

The tornet package can then installed into your environment by running

pip install .

In this repo. To do ML with TorNet, additional installs may be necessary depending on library of choice. See e.g., requirements/tensorflow.txt, requirements/torch.txt and/or requirements/jax.txt.

Please note that we did not exhaustively test all combinations of operating systems, data loaders, deep learning frameworks, and GPU usage. If you are using the latest version of keras, then I recommend you follow setup instructions on the keras webpage https://keras.io/getting_started/. Feel free to describe any issues you are having under the issues tab.

Conda

If using conda

conda create -n tornet-{backend} python=3.10
conda activate tornet-{backend}
pip install -r requirements/{backend}.txt

Replace {backend} with tensorflow, torch or jax.

Loading and visualizing TorNet

Start with notebooks/DataLoaders.ipynb to get an overview on loading and visualizing the dataset.

To run inference on TorNet samples using a pretrained model, look at notebooks/VisualizeSamples.ipynb.

Train CNN baseline model

Multiple backend support with Keras 3

The model uses Keras 3 which supports multiple backends. The environment variable KERAS_BACKEND can be used to choose the backend.

export KERAS_BACKEND=tensorflow
# export KERAS_BACKEND=torch
# export KERAS_BACKEND=jax

The following trains the CNN baseline model described in the paper using tensorflow. If you run this out-of-the-box, it will run very slowly because it uses the basic dataloader. Read the DataLoader notebook for tips on how to optimize the data loader.

# Set path to dataset
export TORNET_ROOT=/path/to/tornet     

# Run training
python scripts/tornado_detection/train_tornado_keras.py scripts/tornado_detection/config/params.json

Evaluate trained model

To evaluate this model on the test set, run

# Set path to dataset
export TORNET_ROOT=/path/to/tornet  

# Evaluate trained model
python scripts/tornado_detection/test_tornado_keras.py

This will compute and print various metrics computed on the test set. Note that this script will attempt to download pretrained weights from huggingface, so ensure there is internet connectivity. Alternatively, manually download the pretrained yourself and provide with --model_path

Disclosure

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.
© 2024 Massachusetts Institute of Technology.
The software/firmware is provided to you on an As-Is basis
Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.