ClimateNets: Using AI to Fight Climate Change

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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

In this project, we focus on short-term extreme precipitation forecasting using deep neural networks, including convolutions and transformers. In particular, we propose a self-attention augmented convolution mechanism for extreme precipitation forecasting, systematically combining attention scores with traditional convolutions to enrich feature data and reduce the expected errors of the results.

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Built With

• Keras
• TensorFlow
• Pandas
• Xarray
• Python

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Getting Started

Follow the following instructions and install a few software packages. Then Create a conda env and start installation of library dependencies. Download the required datasets from the link below before training models.

Prerequisites

Install a few software packages before you get started.

• conda env

  conda env update --file ./environment.yml --prune [--debug]

Installation

1. Clone the repo

   git clone https://github.com/weichen-huang/climatenets.git

2. Install python dependency packages

   conda env update --file ./environment.yml --prune [--debug]

3. Download the following datasets -

   PRISM - https://ftp.prism.oregonstate.edu/daily/ppt/
   NCEP/NCAR Reanalysis - https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html
   Global Historical Climatology Network daily (GHCNd) - https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily

4. Training and evaluating models

   python train.py

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Usage

TODO add more details

For more examples, please refer to the Documentation

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Roadmap

• Feature 1
• Feature 2
• Feature 3
  • Nested Feature

See the open issues for a full list of proposed features (and known issues).

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Contributing

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1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Weichen Huang - @twitter_handle - weichen.huang.2022@gmail.com

Project Link: https://github.com/weichen-huang/climatenets

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Acknowledgments

TODO add more details

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