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license: cc-by-4.0
---
<!--  -->
<a href="url"><img src="assets/CRA5LOGO.svg" align="center"></a>
[](https://github.com/InterDigitalInc/CompressAI/blob/master/LICENSE)
[](https://pypi.org/project/compressai/)
[](https://pypi.org/project/cra5/#files)
<div align="center">
[](https://www.nature.com/articles/s43247-025-02903-z)
### [Climate science data can be compressed efficiently by dual-stage extreme compression with a variational auto-encoder transformer](https://www.nature.com/articles/s43247-025-02903-z)
</div>
# Introduction and get started
### CRA5 dataset now is available at [OneDrive](https://hkustconnect-my.sharepoint.com/:f:/g/personal/thanad_connect_ust_hk/EjGupNV_IcZKre1PdN65dFUB2sBOHX3MCH4cycBtcSm2yA?e=GAqS1J)
## Paper Summary
We introduce **VAEformer**, a variational autoencoder transformer designed for the extreme compression of climate data. Addressing the storage challenges of massive datasets like ERA5, VAEformer utilizes a low-complexity transformer with variance inference to achieve high compression efficiency.
We successfully compressed the **226 TB ERA5 dataset into the 0.7 TB CRA5 dataset**, achieving a **>300x compression ratio**. Despite this extreme reduction, CRA5 retains high scientific utility; global weather forecasting models trained on CRA5 achieve accuracy comparable to those trained on the original data, significantly lowering the barrier for AI-based meteorological research.
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](https://docs.python.org/3.6/library/venv.html):
```bash
git clone https://github.com/taohan10200/CRA5
cd CRA5
pip install -U pip && pip install -e .
```
<!-- For a custom installation, you can also run one of the following commands:
* `pip install -e '.[dev]'`: install the packages required for development (testing, linting, docs)
* `pip install -e '.[tutorials]'`: install the packages required for the tutorials (notebooks)
* `pip install -e '.[all]'`: install all the optional packages -->
<!-- ## Documentation -->
<!-- * [Installation](https://interdigitalinc.github.io/CompressAI/installation.html)
* [CompressAI API](https://interdigitalinc.github.io/CompressAI/)
* [Training your own model](https://interdigitalinc.github.io/CompressAI/tutorials/tutorial_train.html)
* [List of available models (model zoo)](https://interdigitalinc.github.io/CompressAI/zoo.html) -->
# Test
```
python test.py
```
# Usages
## Using with API:
> Supporting functions like: Compression / decompression / latents representation / feature visulization / reconstructed visulization
### Step 1: Download from Hugging Face
We provide a simple way to download the data using `huggingface_hub`.
```python
from huggingface_hub import hf_hub_download
# Download CRA5 binary file for a specific timestamp (e.g., 2022-01-01T00:00:00)
local_bin_path = hf_hub_download(
repo_id="taohan10200/CRA5-Dataset",
repo_type="dataset",
filename="2022/2022-01-01T00:00:00.bin",
local_dir="./data/CRA5"
)
print(f"Downloaded to: {local_bin_path}")
```
### Step 2: Use CRA5 API for Decompression and Visualization
```python
# 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>
```python
# 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
```python
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](https://arxiv.org/abs/2402.00059), 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](https://www.nature.com/articles/s41586-023-06185-3).
<!--  -->
<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.
<!-- Script and notebook examples can be found in the `examples/` directory.
To encode/decode images with the provided pre-trained models, run the
`codec.py` example:
```bash
python3 examples/codec.py --help
```
An examplary training script with a rate-distortion loss is provided in
`examples/train.py`. You can replace the model used in the training script
with your own model implemented within CompressAI, and then run the script for a
simple training pipeline:
```bash
python3 examples/train.py -d /path/to/my/image/dataset/ --epochs 300 -lr 1e-4 --batch-size 16 --cuda --save
```
> **Note:** the training example uses a custom [ImageFolder](https://interdigitalinc.github.io/CompressAI/datasets.html#imagefolder) structure.
A jupyter notebook illustrating the usage of a pre-trained model for learned image
compression is also provided in the `examples` directory:
```bash
pip install -U ipython jupyter ipywidgets matplotlib
jupyter notebook examples/
``` -->
<!-- ### Evaluation
To evaluate a trained model on your own dataset, CompressAI provides an
evaluation script:
```bash
python3 -m compressai.utils.eval_model checkpoint /path/to/images/folder/ -a $ARCH -p $MODEL_CHECKPOINT...
```
To evaluate provided pre-trained models:
```bash
python3 -m compressai.utils.eval_model pretrained /path/to/images/folder/ -a $ARCH -q $QUALITY_LEVELS...
```
To plot results from bench/eval_model simulations (requires matplotlib by default):
```bash
python3 -m compressai.utils.plot --help -->
<!-- ``` -->
<!-- To evaluate traditional codecs:
```bash
python3 -m compressai.utils.bench --help
python3 -m compressai.utils.bench bpg --help
python3 -m compressai.utils.bench vtm --help
```
For video, similar tests can be run, CompressAI only includes ssf2020 for now:
```bash
python3 -m compressai.utils.video.eval_model checkpoint /path/to/video/folder/ -a ssf2020 -p $MODEL_CHECKPOINT...
python3 -m compressai.utils.video.eval_model pretrained /path/to/video/folder/ -a ssf2020 -q $QUALITY_LEVELS...
python3 -m compressai.utils.video.bench x265 --help
python3 -m compressai.utils.video.bench VTM --help
python3 -m compressai.utils.video.plot --help
``` -->
<!-- ## Tests
Run tests with `pytest`:
```bash
pytest -sx --cov=compressai --cov-append --cov-report term-missing tests
```
Slow tests can be skipped with the `-m "not slow"` option. -->
## 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](mailto: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](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) and 9 [surface variables](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview) .
| Variable | channel | error | Variable | channel | error | Variable | channel | error |Variable | channel | error |Variable | channel | error |
|--------|---------|-----------|--------|---------|-----------|--------|---------|-----------|--------|---------|-----------|--------|---------|-----------|
| geopotential | z_1000 | 9.386 | specific_humidity | q_1000 | 0.00033 | u_component_of_wind | u_1000 | 0.416 |v_component_of_wind | v_1000 | 0.411 | temperature | t_1000 | 0.405 |
| geopotential | z_975 | 7.857 | specific_humidity | q_975 | 0.00032 | u_component_of_wind | u_975 | 0.448 |v_component_of_wind | v_975 | 0.442 | temperature | t_975 | 0.380 |
| geopotential | z_950 | 6.802 | specific_humidity | q_950 | 0.00035 | u_component_of_wind | u_950 | 0.491 |v_component_of_wind | v_950 | 0.479 | temperature | t_950 | 0.352 |
| geopotential | z_925 | 6.088 | specific_humidity | q_925 | 0.00037 | u_component_of_wind | u_925 | 0.520 |v_component_of_wind | v_925 | 0.505 | temperature | t_925 | 0.333 |
| geopotential | z_900 | 5.575 | specific_humidity | q_900 | 0.00036 | u_component_of_wind | u_900 | 0.518 |v_component_of_wind | v_900 | 0.503 | temperature | t_900 | 0.321 |
| geopotential | z_875 | 5.259 | specific_humidity | q_875 | 0.00035 | u_component_of_wind | u_875 | 0.517 |v_component_of_wind | v_875 | 0.503 | temperature | t_875 | 0.309 |
| geopotential | z_850 | 5.061 | specific_humidity | q_850 | 0.00034 | u_component_of_wind | u_850 | 0.508 |v_component_of_wind | v_850 | 0.493 | temperature | t_850 | 0.294 |
| geopotential | z_825 | 4.941 | specific_humidity | q_825 | 0.00031 | u_component_of_wind | u_825 | 0.496 |v_component_of_wind | v_825 | 0.481 | temperature | t_825 | 0.276 |
| geopotential | z_800 | 4.897 | specific_humidity | q_800 | 0.00029 | u_component_of_wind | u_800 | 0.487 |v_component_of_wind | v_800 | 0.472 | temperature | t_800 | 0.259 |
| geopotential | z_775 | 4.947 | specific_humidity | q_775 | 0.00027 | u_component_of_wind | u_775 | 0.486 |v_component_of_wind | v_775 | 0.468 | temperature | t_775 | 0.250 |
| geopotential | z_750 | 5.120 | specific_humidity | q_750 | 0.00029 | u_component_of_wind | u_750 | 0.545 |v_component_of_wind | v_750 | 0.524 | temperature | t_750 | 0.250 |
| geopotential | z_700 | 5.593 | specific_humidity | q_700 | 0.00029 | u_component_of_wind | u_700 | 0.638 |v_component_of_wind | v_700 | 0.607 | temperature | t_700 | 0.242 |
| geopotential | z_650 | 5.810 | specific_humidity | q_650 | 0.00025 | u_component_of_wind | u_650 | 0.634 |v_component_of_wind | v_650 | 0.610 | temperature | t_700 | 0.242 |
| geopotential | z_600 | 5.882 | specific_humidity | q_600 | 0.00020 | u_component_of_wind | u_600 | 0.633 |v_component_of_wind | v_600 | 0.597 | temperature | t_650 | 0.240 |
| geopotential | z_550 | 5.958 | specific_humidity | q_550 | 0.00018 | u_component_of_wind | u_550 | 0.668 |v_component_of_wind | v_550 | 0.616 | temperature | t_600 | 0.222 |
| geopotential | z_500 | 6.098 | specific_humidity | q_500 | 0.00014 | u_component_of_wind | u_500 | 0.676 |v_component_of_wind | v_500 | 0.603 | temperature | t_550 | 0.201 |
| geopotential | z_450 | 6.408 | specific_humidity | q_450 | 0.00010 | u_component_of_wind | u_450 | 0.699 |v_component_of_wind | v_450 | 0.649 | temperature | t_500 | 0.185 |
| geopotential | z_400 | 6.851 | specific_humidity | q_400 | 0.00007 | u_component_of_wind | u_400 | 0.733 |v_component_of_wind | v_400 | 0.686 | temperature | t_450 | 0.185 |
| geopotential | z_350 | 7.366 | specific_humidity | q_350 | 0.00004 | u_component_of_wind | u_350 | 0.760 |v_component_of_wind | v_350 | 0.704 | temperature | t_400 | 0.179 |
| geopotential | z_300 | 8.324 | specific_humidity | q_300 | 0.00002 | u_component_of_wind | u_300 | 0.744 |v_component_of_wind | v_300 | 0.704 | temperature | t_350 | 0.170 |
| geopotential | z_250 | 8.100 | specific_humidity | q_250 | 0.00001 | u_component_of_wind | u_250 | 0.765 |v_component_of_wind | v_250 | 0.701 | temperature | t_300 | 0.160 |
| geopotential | z_225 | 7.698 | specific_humidity | q_225 | 0.00001 | u_component_of_wind | u_225 | 0.722 |v_component_of_wind | v_225 | 0.642 | temperature | t_250 | 0.166 |
| geopotential | z_200 | 7.900 | specific_humidity | q_200 | 0.00000 | u_component_of_wind | u_200 | 0.646 |v_component_of_wind | v_200 | 0.563 | temperature | t_225 | 0.169 |
| geopotential | z_175 | 8.059 | specific_humidity | q_175 | 0.00000 | u_component_of_wind | u_175 | 0.565 |v_component_of_wind | v_175 | 0.509 | temperature | t_200 | 0.158 |
| geopotential | z_150 | 8.928 | specific_humidity | q_150 | 0.00000 | u_component_of_wind | u_150 | 0.525 |v_component_of_wind | v_150 | 0.458 | temperature | t_150 | 0.149 |
| geopotential | z_125 | 10.813 | specific_humidity | q_125 | 0.00000 | u_component_of_wind | u_125 | 0.479 |v_component_of_wind | v_125 | 0.417 | temperature | t_125 | 0.158 |
| geopotential | z_100 | 15.956 | specific_humidity | q_100 | 0.00000 | u_component_of_wind | u_100 | 0.447 |v_component_of_wind | v_100 | 0.373 | temperature | t_100 | 0.178 |
| geopotential | z_70 | 11.158 | specific_humidity | q_70 | 0.00000 | u_component_of_wind | u_70 | 0.360 |v_component_of_wind | v_70 | 0.275 | temperature | t_70 | 0.155 |
| geopotential | z_50 | 11.962 | specific_humidity | q_50 | 0.00000 | u_component_of_wind | u_50 | 0.356 |v_component_of_wind | v_50 | 0.242 | temperature | t_50 | 0.158 |
| geopotential | z_30 | 13.317 | specific_humidity | q_30 | 0.00000 | u_component_of_wind | u_30 | 0.348 |v_component_of_wind | v_30 | 0.221 | temperature | t_30 | 0.153 |
| geopotential | z_20 | 16.538 | specific_humidity | q_20 | 0.00000 | u_component_of_wind | u_20 | 0.361 |v_component_of_wind | v_20 | 0.229 | temperature | t_20 | 0.161 |
| geopotential | z_10 | 19.751 | specific_humidity | q_10 | 0.00000 | u_component_of_wind | u_10 | 0.350 |v_component_of_wind | v_10 | 0.232 | temperature | t_10 | 0.166 |
| geopotential | z_7 | 20.925 | specific_humidity | q_7 | 0.00000 | u_component_of_wind | u_7 | 0.315 |v_component_of_wind | v_7 | 0.225 | temperature | t_7 | 0.161 |
| geopotential | z_5 | 20.825 | specific_humidity | q_5 | 0.00000 | u_component_of_wind | u_5 | 0.307 |v_component_of_wind | v_5 | 0.212 | temperature | t_5 | 0.160 |
| geopotential | z_3 | 24.529 | specific_humidity | q_3 | 0.00000 | u_component_of_wind | u_3 | 0.333 |v_component_of_wind | v_3 | 0.246 | temperature | t_3 | 0.194 |
| geopotential | z_2 | 28.055 | specific_humidity | q_2 | 0.00000 | u_component_of_wind | u_2 | 0.338 |v_component_of_wind | v_2 | 0.239 | temperature | t_2 | 0.184 |
| geopotential | z_1 | 27.987 | specific_humidity | q_1 | 0.00000 | u_component_of_wind | u_1 | 0.363 |v_component_of_wind | v_1 | 0.245 | temperature | t_1 | 0.182 |
|--------|---------|-----------|--------|---------|-----------|--------|---------|-----------|--------|---------|-----------|--------|---------|-----------|
| relative_humidity | r_1000 | 3.073 | vertical_velocity w_1000 | 0.059 || 10m_v_component_of_wind | v10 | 0.367 |
| relative_humidity | r_975 | 3.192 | vertical_velocity w_975 | 0.067 || 10m_u_component_of_wind | u10 | 0.379 |
| relative_humidity | r_950 | 3.588 | vertical_velocity w_950 | 0.078 || 100m_v_component_of_wind | v100 | 0.435 |
| relative_humidity | r_925 | 3.877 | vertical_velocity w_925 | 0.086 || 100m_u_component_of_wind | u100 | 0.445 |
| relative_humidity | r_900 | 3.982 | vertical_velocity w_900 | 0.090 || 2m_temperature | t2m | 0.720 |
| relative_humidity | r_875 | 4.011 | vertical_velocity w_875 | 0.092 || total_cloud_cover | tcc | 0.146 |
| relative_humidity | r_850 | 3.933 | vertical_velocity w_850 | 0.093 || surface_pressure | sp | 480.222
| relative_humidity | r_825 | 3.789 | vertical_velocity w_825 | 0.094 || total_precipitation | tp1h | 0.264 |
| relative_humidity | r_800 | 3.555 | vertical_velocity w_800 | 0.096 || mean_sea_level_pressure | msl | 12.685 |
| relative_humidity | r_775 | 3.449 | vertical_velocity w_775 | 0.099 |
| relative_humidity | r_750 | 3.816 | vertical_velocity w_750 | 0.102 |
| relative_humidity | r_700 | 4.265 | vertical_velocity w_700 | 0.110 |
| relative_humidity | r_650 | 4.223 | vertical_velocity w_650 | 0.114 |
| relative_humidity | r_600 | 4.183 | vertical_velocity w_600 | 0.112 |
| relative_humidity | r_550 | 4.411 | vertical_velocity w_550 | 0.106 |
| relative_humidity | r_500 | 4.409 | vertical_velocity w_500 | 0.101 |
| relative_humidity | r_450 | 4.675 | vertical_velocity w_450 | 0.096 |
| relative_humidity | r_400 | 4.831 | vertical_velocity w_400 | 0.091 |
| relative_humidity | r_350 | 4.932 | vertical_velocity w_350 | 0.084 |
| relative_humidity | r_300 | 5.151 | vertical_velocity w_300 | 0.075 |
| relative_humidity | r_250 | 5.134 | vertical_velocity w_250 | 0.056 |
| relative_humidity | r_225 | 4.682 | vertical_velocity w_225 | 0.046 |
| relative_humidity | r_200 | 3.899 | vertical_velocity w_200 | 0.039 |
| relative_humidity | r_175 | 3.063 | vertical_velocity w_175 | 0.034 |
| relative_humidity | r_150 | 2.508 | vertical_velocity w_150 | 0.029 |
| relative_humidity | r_125 | 2.123 | vertical_velocity w_125 | 0.024 |
| relative_humidity | r_100 | 1.844 | vertical_velocity w_100 | 0.018 |
| relative_humidity | r_70 | 0.487 | vertical_velocity w_70 | 0.010 |
| relative_humidity | r_50 | 0.151 | vertical_velocity w_50 | 0.007 |
| relative_humidity | r_30 | 0.097 | vertical_velocity w_30 | 0.005 |
| relative_humidity | r_20 | 0.083 | vertical_velocity w_20 | 0.003 |
| relative_humidity | r_10 | 0.033 | vertical_velocity w_10 | 0.002 |
| relative_humidity | r_7 | 0.016 | vertical_velocity w_7 | 0.001 |
| relative_humidity | r_5 | 0.008 | vertical_velocity w_5 | 0.001 |
| relative_humidity | r_3 | 0.003 | vertical_velocity w_3 | 0.001 |
| relative_humidity | r_2 | 0.001 | vertical_velocity w_2 | 0.000 |
| relative_humidity | r_1 | 0.000 | vertical_velocity w_1 | 0.000 |
## Related links
* CompressAI Library: https://github.com/InterDigitalInc/CompressAI
## Citation
If you find this work useful for your research, please cite our paper:
```bibtex
@article{han2025climate,
title={Climate science data can be compressed efficiently by dual-stage extreme compression with a variational auto-encoder transformer},
author={Han, Tao and Chen, Zhenghao and Guo, Song and Xu, Wanghan and Ouyang, Wanli and Bai, Lei},
journal={Communications Earth \& Environment},
volume={6},
number={1},
pages={955},
year={2025},
publisher={Nature Portfolio},
doi={10.1038/s43247-025-02903-z},
url={https://www.nature.com/articles/s43247-025-02903-z}
}
``` |