High Spatiotemporal Resolution Estimation of Global Surface CO Concentrations Using a Deep Learning Model
A high-performance Convolutional Neural Network (CNN)-based Residual Network (ResNet) was developed for estimating daily worldwide CO concentrations at a high spatial resolution of 0.07 ° from June 2018 to May 2021, using the global TROPOMI Total Column of atmospheric CO (TCCO) product and reanalysis datasets. The proposed framework achieved a desirable estimation accuracy, with R -values (correlation coefficients) of 0.90 and 0.96 for daily and monthly predictions, respectively. The daily surface CO concentration dataset from our study is potentially useful for further relevant sustainable studies.
Resource information
- Data purpose
- Research dataset
- Format
- zip
- Original repository
- 11806178
- DOI
- 10.5281/zenodo.11806178
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- Not specified
- Last record update
- Nov 5, 2026
About this resource
A high-performance Convolutional Neural Network (CNN)-based Residual Network (ResNet) was developed for estimating daily worldwide CO concentrations at a high spatial resolution of 0.07 ° from June 2018 to May 2021, using the global TROPOMI Total Column of atmospheric CO (TCCO) product and reanalysis datasets. The proposed framework achieved a desirable estimation accuracy, with R -values (correlation coefficients) of 0.90 and 0.96 for daily and monthly predictions, respectively. The daily surface CO concentration dataset from our study is potentially useful for further relevant sustainable studies.