Published June 16, 2024
| Version v1
Dataset
Open
High Spatiotemporal Resolution Estimation of Global Surface CO Concentrations Using a Deep Learning Model
Authors/Creators
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1.
Hong Kong University of Science and Technology
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2.
Chinese University of Hong Kong
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3.
University of Hong Kong
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4.
Chinese Academy of Sciences
Description
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.