Articles | Volume 16, issue 20
https://doi.org/10.5194/gmd-16-5895-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-5895-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
20 Oct 2023
Model evaluation paper |
| 20 Oct 2023
| 20 Oct 2023
Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning
Daehyeon Han
Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea
Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea
Yeji Shin
National Institute of Meteorological Sciences, Korea Meteorological Administration, Jeju-do, 63568, South Korea
Market Intelligence Team, CJ CheilJedang Corporation, Seoul, 04560, South Korea
Juhyun Lee
Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea
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EGUsphere, https://doi.org/10.5194/egusphere-2026-1034, https://doi.org/10.5194/egusphere-2026-1034, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
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.
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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.
To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep...
