Articles | Volume 19, issue 12
https://doi.org/10.5194/amt-19-4313-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-4313-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jun 2026
Research article |
| 30 Jun 2026
| 30 Jun 2026
Enhanced methane monitoring: a globally harmonized daily 0.1° XCH4 through machine learning-based fusion of GOSAT, GOSAT-2, and TROPOMI
Jebun Naher Keya
Department of Civil, Urban, Earth, & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
Yejin Kim
Department of Civil, Urban, Earth, & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
Hyunyoung Choi
Department of Civil, Urban, Earth, & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
Department of Civil, Urban, Earth, & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
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Daehyeon Han, Jungho Im, Yeji Shin, and Juhyun Lee
Geosci. Model Dev., 16, 5895–5914, https://doi.org/10.5194/gmd-16-5895-2023, https://doi.org/10.5194/gmd-16-5895-2023, 2023
Short summary
Short summary
To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep learning (DL), we carried out a comprehensive evaluation and analysis. We compared four key factors: DL model, length of the input sequence, loss function, and ensemble approach. Generally, U-Net outperformed ConvLSTM. Loss function and ensemble showed potential for improving performance when they synergized well. The length of the input sequence did not significantly affect the results.
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Short summary
Monitoring atmospheric methane is essential, yet current satellite observations are limited by measurement errors and incomplete coverage. This study combines three satellite missions using machine learning to generate a daily global 0.1° XCH4 dataset for 2020–2023. The resulting dataset improves coverage in data-sparse regions and reveals intensifying methane concentrations over South Asia, East Asia, and Central Africa, providing a valuable resource for enhanced regional methane monitoring.
Monitoring atmospheric methane is essential, yet current satellite observations are limited by...
