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DOI10.3390/rs16030442
A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net
Wang, Liwen; Li, Qian; Peng, Xuan; Lv, Qi
发表日期2024
EISSN2072-4292
起始页码16
结束页码3
卷号16期号:3
英文摘要Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often cannot accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherently can capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.
英文关键词temporal downscaling; U-Net; flow regularization; residual blocks; ERA5
语种英语
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:001160089700001
来源期刊REMOTE SENSING
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/291267
作者单位National University of Defense Technology - China
推荐引用方式
GB/T 7714
Wang, Liwen,Li, Qian,Peng, Xuan,et al. A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net[J],2024,16(3).
APA Wang, Liwen,Li, Qian,Peng, Xuan,&Lv, Qi.(2024).A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net.REMOTE SENSING,16(3).
MLA Wang, Liwen,et al."A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net".REMOTE SENSING 16.3(2024).
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