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- Title
Generating Daily High-Resolution Regional XCO 2 by Deep Neural Network and Multi-Source Data.
- Authors
Tian, Wenjie; Zhang, Lili; Yu, Tao; Yao, Dong; Zhang, Wenhao; Wang, Chunmei
- Abstract
CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people's understanding of CO2 spatiotemporal variations, supporting carbon-related research.
- Subjects
CHINA; ARTIFICIAL neural networks; CLIMATE change; GREENHOUSE gases; STRIPES
- Publication
Atmosphere, 2024, Vol 15, Issue 8, p985
- ISSN
2073-4433
- Publication type
Article
- DOI
10.3390/atmos15080985