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')

# 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')

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.

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

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 .

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