Issue |
E3S Web Conf.
Volume 520, 2024
4th International Conference on Environment Resources and Energy Engineering (ICEREE 2024)
|
|
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Article Number | 02010 | |
Number of page(s) | 5 | |
Section | Carbon Emission Control and Waste Resource Utilization | |
DOI | https://doi.org/10.1051/e3sconf/202452002010 | |
Published online | 03 May 2024 |
Estimation Method of Covariance Matrix in Atmospheric Inversion of CO2 Emissions
1 School of Computer and Artificial Intelligence, Zhengzhou University, 450001 Zhengzhou, China
2 Zhengzhou Institute of Metrology, 450001 Zhengzhou, China
3 National Institute of Metrology, 100029 Beijing, China
a aakagi@qq.com
b reng@nim.ac.cn
c 469600057@qq.com
d dkl1439@163.com
e* Corresponding author’s email: elinhong@nim.ac.cn
Atmospheric inversion of CO2 Emissions is based on the correction of prior carbon dioxide flux inventories using concentration monitoring data and atmospheric transport models to obtain posterior carbon dioxide flux. In atmospheric inversion studies, fixed covariance functions are commonly used to generate covariance matrices, and the hyperparameters in the covariance functions are empirically estimated. In this study, we design and implement an ideal experiment based on meteorological data from the central urban area of Zhengzhou, using WRF-STILT to generate sensitivity matrices and construct real carbon emission inventories and prior inventories. Based on the real carbon emission inventories and sensitivity matrices of monitoring stations, simulated observation concentration values are generated. Firstly, based on the observed concentration values, sensitivity matrices of monitoring stations, prior inventories, and constructed covariance matrices, the values of hyperparameters are determined based on maximum marginal likelihood estimation. Then, the influence of different prior covariance functions on the inversion results is tested, and it is found that the prior covariance matrix generated by the balgovind covariance function is most suitable for the experimental data.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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