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<a href="url"><img src="assets/CRA5LOGO.svg" align="center"></a>

Paper:CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer

Introduction and get started

CRA5 dataset now is available at OneDrive

CRA5 is a extreme compressed weather dataset of the most popular ERA5 reanalysis dataset. The repository also includes compression models, forecasting model for researchers to conduct portable weather and climate research.

CRA5 currently provides:

• A customized variaitional transformer (VAEformer) for climate data compression
• A dataset CRA5 less than 1 TiB, but contains the same information with 400+ TiB ERA5 dataset. Covering houly ERA5 from year 1979 to 2023.
• A pre-trained Auto-Encoder on the climate/weather data to support some potential weather research.

Note: Multi-GPU support is now experimental.

Installation

CRA5 supports python 3.8+ and PyTorch 1.7+.

conda create --name cra5 python=3.10 -y 
conda activate cra5

Please install cra5 from source:

A C++17 compiler, a recent version of pip (19.0+), and common python packages are also required (see setup.py for the full list).

To get started locally and install the development version of CRA5, run the following commands in a virtual environment:

git clone https://github.com/taohan10200/CRA5
cd CRA5

pip install -U pip && pip install -e .

Test

python test.py

Usages

Using with API:

Supporting functions like: Compression / decompression / latents representation / feature visulization / reconstructed visulization

# We build a downloader to help use download the original ERA5 netcdf files for testing.

# data/ERA5/2024/2024-06-01T00:00:00_pressure.nc (513MiB) and data/ERA5/2024/2024-06-01T00:00:00_single.nc (18MiB) 
from cra5.api.era5_downloader import era5_downloader
ERA5_data = era5_downloader('./cra5/api/era5_config.py') #specify the dataset config for what we want to download
data = ERA5_data.get_form_timestamp(time_stamp="2024-06-01T00:00:00",
                                    local_root='./data/ERA5')

# After getting the ERA5 data ready, you can explore the compression.
from cra5.api import cra5_api
cra5_API = cra5_api()

####=======================compression functions=====================
# Return a continuous latent y for ERA5 data at 2024-06-01T00:00:00
y = cra5_API.encode_to_latent(time_stamp="2024-06-01T00:00:00") 

# Return the the arithmetic coded binary stream of y 
bin_stream = cra5_API.latent_to_bin(y=y)  

# Or if you want to directly compress and save the binary stream to a folder
cra5_API.encode_era5_as_bin(time_stamp="2024-06-01T00:00:00", save_root='./data/cra5')  


####=======================decompression functions=====================
# Starting from the bin_stream, you can decode the binary file to the quantized latent.
y_hat = cra5_API.bin_to_latent(bin_path="./data/CRA5/2024/2024-06-01T00:00:00.bin")  # Decoding from binary can only get the quantized latent.

# Return the normalized cra5 data
normlized_x_hat = cra5_API.latent_to_reconstruction(y_hat=y_hat) 


# If you have saveed  or downloaded the binary file, then you can directly restore the binary file into reconstruction.
normlized_x_hat = cra5_API.decode_from_bin("2024-06-01T00:00:00", return_format='normalized') # Return the normalized cra5 data
x_hat = cra5_API.decode_from_bin("2024-06-01T00:00:00", return_format='de_normalized') # Return the de-normalized cra5 data

# Show some channels of the latent
cra5_API.show_latent(
	latent=y_hat.squeeze(0).cpu().numpy(), 
	time_stamp="2024-06-01T00:00:00", 
	show_channels=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150],
    save_path = './data/vis')

<a href="url"><img src="assets/2024-06-01T00_latent.png" align="center"></a>

# show some variables for the constructed data
cra5_API.show_image(
	reconstruct_data=x_hat.cpu().numpy(), 
	time_stamp="2024-06-01T00:00:00", 
	show_variables=['z_500', 'q_500', 'u_500', 'v_500', 't_500', 'w_500'],
    save_path = './data/vis')

<a href="url"><img src="assets/2024-06-01T00.png" align="center"></a>

Or using with the pre-trained model

import os 
import torch
from cra5.models.compressai.zoo import vaeformer_pretrained
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(device)
net = vaeformer_pretrained(quality=268, pretrained=True).eval().to(device)
input_data_norm = torch.rand(1,268, 721,1440).to(device) #This is a proxy weather data. It actually should be a 

print(x.shape)
with torch.no_grad():
    out_net = net.compress(x) 
    
print(out_net)

Features

1. CRA5 dataset is a product of the VAEformer applied in the atmospheric science. We explore this to facilitate the research in weather and climate.

• Train the large data-driven numerical weather forecasting models with our CRA5

Note: For researches who do not have enough disk space to store the 300 TiB+ ERA5 dataset, but have interests to train a large weather forecasting model, like FengWu-GHR, this research can help you save it into less than 1 TiB disk.

Our preliminary attemp has proven that the CRA5 dataset can train the very very similar NWP model compared with the original ERA5 dataset. Also, with this dataset, you can easily train a Nature published forecasting model, like Pangu-Weather.

<a href="url"><img src="assets/rmse_acc_bias_activity.png" align="center"></a>

2. VAEformer is a powerful compression model, we hope it can be extended to other domains, like image and video compression.

<a href="url"><img src="assets/MSE_supp_new.png" align="center"></a>

3 VAEformer is based on the Auto-Encoder-Decoder, we provide a pretrained VAE for the weather research, you can use our VAEformer to get the latents for downstream research, like diffusion-based or other generation-based forecasting methods.

• Using it as a Auto-Encoder-Decoder

Note: For people who are intersted in diffusion-based or other generation-based forecasting methods, we can provide an Auto Encoder and decoder for the weather research, you can use our VAEformer to get the latents for downstream research.

License

CompressAI is licensed under the BSD 3-Clause Clear License

Contributing

We welcome feedback and contributions. Please open a GitHub issue to report bugs, request enhancements or if you have any questions.

Before contributing, please read the CONTRIBUTING.md file.

Authors

• Tao Han (hantao10200@gmail.com)
• Zhenghao Chen.

Citation

If you use this project, please cite the relevant original publications for the models and datasets, and cite this project as:

@article{han2024cra5extremecompressionera5,
      title={CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer}, 
      author={Tao Han and Zhenghao Chen and Song Guo and Wanghan Xu and Lei Bai},
      year={2024},
      eprint={2405.03376},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2405.03376}, 
}

For any work related to the forecasting models, please cite

@article{han2024fengwughr,
title={FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting}, 
author={Tao Han and Song Guo and Fenghua Ling and Kang Chen and Junchao Gong and Jingjia Luo and Junxia Gu and Kan Dai and Wanli Ouyang and Lei Bai},
year={2024},
eprint={2402.00059},
archivePrefix={arXiv},
primaryClass={cs.LG}
}

The weather variabls supported in CRA5 and their numerical error

CRA5 contains a total of 268 variables, including 7 pressure-level variables from the ERA5 pressure level archive and 9 surface variables .

Related links

• CompressAI Library: https://github.com/InterDigitalInc/CompressAI