Generating a long-term (2003−2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)
- DOI
- 10.1016/j.scitotenv.2022.157747
- Published
- 2022-11
- Container
- Science of The Total Environment
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.scitotenv.2022.157747,
title = {Generating a long-term (2003−2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)},
author = {Yi Xiao and Yuan Wang and Qiangqiang Yuan and Jiang He and Liangpei Zhang},
year = {2022},
journal = {Science of The Total Environment},
doi = {10.1016/j.scitotenv.2022.157747},
url = {https://doi.org/10.1016/j.scitotenv.2022.157747}
}RIS
TY - JOUR TI - Generating a long-term (2003−2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS) AU - Yi Xiao AU - Yuan Wang AU - Qiangqiang Yuan AU - Jiang He AU - Liangpei Zhang PY - 2022 JO - Science of The Total Environment DO - 10.1016/j.scitotenv.2022.157747 UR - https://doi.org/10.1016/j.scitotenv.2022.157747 ER -
APA
Xiao, Y., Wang, Y., Yuan, Q., He, J., & Zhang, L. (2022). Generating a 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. https://doi.org/10.1016/j.scitotenv.2022.157747
Source records
- crossref · retrieved 2026-09-25T19:40:35.891Z