Enhanced Zenith Tropospheric Delay Forecasting Using a Hybrid GRU-LSTM Deep Learning Model
文献类型：期刊论文
| 作者 | Li, Ming-Shuai2,3,4; Li, Yu5; Wang, Na2,3,4; Cui, Lang2,3,4; Zhang, Ming2,4; Li, Jian1,2; Duan, Xue-Feng1,2 |
| 刊名 | RESEARCH IN ASTRONOMY AND ASTROPHYSICS
 |
| 出版日期 | 2025-10-01 |
| 卷号 | 25期号:10页码:104002 |
| 关键词 | atmospheric effects methods: data analysis site testing |
| ISSN号 | 1674-4527 |
| DOI | 10.1088/1674-4527/adf70f |
| 产权排序 | 1 |
| 英文摘要 | Accurate estimation of Zenith Tropospheric Delay (ZTD) is essential for mitigating atmospheric effects in radio astronomical observations and improving the retrieval of precipitable water vapor (PWV). In this study, we first analyze the periodic characteristics of ZTD at the NanShan Radio Telescope site using Fourier transform, revealing its dominant seasonal variations, and then investigate the correlation between ZTD and local meteorological parameters, to better understand atmospheric influences on tropospheric delay. Based on these analyses, we propose a hybrid deep learning Gated Recurrent Units-Long Short-Term Memory model, incorporating meteorological parameters as external inputs to enhance ZTD forecasting accuracy. Experimental results demonstrate that the proposed approach achieves a Root Mean Squared Error of 7.97 mm and a correlation coefficient R of 96%, significantly outperforming traditional empirical models and standalone deep learning architectures. These findings indicate that the model effectively captures both short-term dynamics and long-term dependencies in ZTD variations. The improved ZTD predictions not only contribute to reducing atmospheric errors in radio astronomical observations but also provide a more reliable basis for PWV retrieval and forecasting. This study highlights the potential of deep learning in tropospheric delay modeling, offering advancements in both atmospheric science and geodetic applications. |
| WOS关键词 | TIME-SERIES |
| 资助项目 | CAS Light of West China Program[2021-XBQNXZ-030] ; CAS Light of West China Program[2021-XBQNXZ-005] ; Xinjiang Key Laboratory of Radio Astrophysics[2023D04064] ; National Key R&D Program of China[2024YFA1611503] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001571586100001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | CAS Light of West China Program ; Xinjiang Key Laboratory of Radio Astrophysics ; National Key R&D Program of China |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/8135]  |
| 专题 | 微波接收机技术实验室 |
| 通讯作者 | Li, Ming-Shuai |
| 作者单位 | 1.Xinjiang Key Lab Microwave Technol, Urumqi 830011, Peoples R China 2.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 3.Chinese Acad Sci, Key Lab Radio Astron & Technol, Beijing 100101, Peoples R China 4.Xinjiang Key Lab Radio Astrophys, Urumqi 830011, Peoples R China 5.China Earthquake Networks Ctr, Beijing 100045, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Ming-Shuai,Li, Yu,Wang, Na,et al. Enhanced Zenith Tropospheric Delay Forecasting Using a Hybrid GRU-LSTM Deep Learning Model[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2025,25(10):104002. |
| APA | Li, Ming-Shuai.,Li, Yu.,Wang, Na.,Cui, Lang.,Zhang, Ming.,...&Duan, Xue-Feng.(2025).Enhanced Zenith Tropospheric Delay Forecasting Using a Hybrid GRU-LSTM Deep Learning Model.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,25(10),104002. |
| MLA | Li, Ming-Shuai,et al."Enhanced Zenith Tropospheric Delay Forecasting Using a Hybrid GRU-LSTM Deep Learning Model".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 25.10(2025):104002. |
入库方式： OAI收割
来源：新疆天文台
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