DeepCAMS (STOTEN 2022)

📖Paper | 🖼️PDF

PyTorch and MATLAB codes for "Generating long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)", Science of The Total Environment (STOTEN), 2022.

Authors: Yi Xiao, Yuan Wang, Qiangqiang Yuan*, Jiang He, and Liangpei Zhang
Wuhan University

Abstract

Generating a long-term high-spatiotemporal resolution global PM2.5 dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003–2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). Hence, this paper developed a deep learning-based framework (DeepCAMS) to downscale CAMS PM2.5 product on the spatiotemporal dimension for resolution enhancement. The nonlinear statistical downscaling from low-resolution (LR) to high-resolution (HR) data can be learned from the high-quality (0.25°, hourly) but short-term (2018–2020) Goddard Earth Observing System composition forecast (GEOS-CF) system PM2.5 product. Compared to the conventional spatiotemporal interpolation methods, simulation validations on GEOS-CF demonstrate that DeepCAMS is capable of producing accurate temporal variations with an improvement of Root-Mean-Squared Error (RMSE) of 0.84 (4.46 to 5.30) ug/m3 and spatial details with an improvement of Mean Absolute Error (MAE) of 0.16 (0.34 to 0.50) ug/m3. The real validations on CAMS reflect convincing spatial consistency and temporal continuity at both regional and global scales. Furthermore, the proposed dataset is validated with OpenAQ air quality data from 2017 to 2019, and the in-situ validations illustrate that the DeepCAMS maintains the consistent precision (R: 0.597) as the original CAMS (R: 0.593) while tripling the spatiotemporal resolution.

🌱2003-2020 Global Hourly 0.25° PM2.5 Dadaset🌱

Please download from Zenodo:

The overall two-stage flowchart

Environment

• CUDA 10.0
• pytorch 1.x

Model Training

1) For spatial downscaling

Download the LR-HR paired Geos-CF from Google Drive

2) For temporal downscaling

Download the hourly Geos-CF from Google Drive

3) Two-Stage Training

python /T-SR/my_train.py
python /S-SR/main_3x.py

Test

python T-SR/test.py
python S-SR/demo_3x.py

Temporal downscaling results

Spatial Downscaling results

In-situ Validation

More details can be found in our paper!

Contact

If you have any questions or suggestions, feel free to contact me. 😊
Email: [email protected]; [email protected]

Citation

If you find our work helpful in your research, please consider citing it. Many thanks for your support! 😊

@article{xiao2022deepcams,
  title={Generating a long-term (2003- 2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)},
  author={Xiao, Yi and Wang, Yuan and Yuan, Qiangqiang and He, Jiang and Zhang, Liangpei},
  journal={Science of The Total Environment},
  volume={848},
  pages={157747},
  year={2022},
  publisher={Elsevier}
}

Acknowledgement

Our work is built upon XVFI and ABPN.
Thanks to the author for these awesome works!