A Forecast-Then-Retrieve Framework for Short-Term Forecasting of Downward Solar Radiation Using Geostationary Satellite Data
文献类型：期刊论文
| 作者 | Zhao, Ziwei1,3; Liu, Ronggao2; Zhang, Xuezhen1,3; Zhu, Mengyao1; Tao, Zexing1; Wu, Maowei1; Chen, Jiewei1; Xu, Duanyang1; Ge, Quansheng1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:4112518 |
| 关键词 | Forecasting Accuracy Clouds Atmospheric modeling Spatial resolution Predictive models Land surface Green energy Geostationary satellites Fossil fuels Climate change Globalization Downward shortwave radiation (DSR) forecasting Himawari-8 satellite Level 1B (L1B) radiance prediction multimodal neural networks spatiotemporal deep learning |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3630152 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | To mitigate global climate change, the replacement of conventional coal-fired power generation with clean energy sources such as photovoltaic (PV) has become a key strategy. However, solar power output is highly variable because it depends on the amount of sunlight reaching the ground, referred to as downward shortwave radiation (DSR). Accurately forecasting DSR in the short term is, therefore, critical for the stable integration of large-scale PV systems into urban power grids. Existing methods typically adopt a retrieve-then-forecast paradigm (first deriving physical products, and then forecasting them), which performs poorly under rapidly varying atmospheric conditions. We propose a new forecast-then-retrieve framework for short-term DSR prediction based on geostationary satellite observations. Unlike conventional approaches, our method first forecasts the full-spectrum Level 1B (L1B) radiances from Himawari-8 for the next 3 h, and then retrieves DSR values from the predicted radiances. To support this framework, we design AtmoNet, a multimodal network that takes the past 3 h of Himawari-8 L1B radiances as input and captures spatiotemporal patterns of reflectance, water vapor, and longwave emission. Experiments show that this approach improves DSR prediction accuracy by up to 6.8% in the 1-3-h forecast window. Moreover, in direct L1B forecasting tasks, AtmoNet outperforms leading models, including the state-of-the-art Swin Transformer, particularly in capturing complex, moisture-driven phenomena such as localized convection and evaporation. By enabling accurate and scalable DSR forecasting, this work supports the stable integration of renewable energy into power grids and has the potential to contribute to global efforts to reduce carbon emissions. |
| URL标识 | 查看原文 |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001627694300005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219503]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhu, Mengyao; Ge, Quansheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhao, Ziwei,Liu, Ronggao,Zhang, Xuezhen,et al. A Forecast-Then-Retrieve Framework for Short-Term Forecasting of Downward Solar Radiation Using Geostationary Satellite Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:4112518. |
| APA | Zhao, Ziwei.,Liu, Ronggao.,Zhang, Xuezhen.,Zhu, Mengyao.,Tao, Zexing.,...&Ge, Quansheng.(2025).A Forecast-Then-Retrieve Framework for Short-Term Forecasting of Downward Solar Radiation Using Geostationary Satellite Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,4112518. |
| MLA | Zhao, Ziwei,et al."A Forecast-Then-Retrieve Framework for Short-Term Forecasting of Downward Solar Radiation Using Geostationary Satellite Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):4112518. |
入库方式： OAI收割
来源：地理科学与资源研究所
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