TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region
Abstract Wind downscaling is crucial for refining coarse‐scale wind estimates, improving local‐scale predictions, and supporting various applications like risk assessment and planning. Dynamic downscaling models demand extensive computational resources and time, leading to a shift toward more effici...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2024GL112124 |
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| _version_ | 1850126653485744128 |
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| author | Jie Lian Sirong Huang Jiahao Shao Peiyan Chen Shengming Tang Yi Lu Hui Yu |
| author_facet | Jie Lian Sirong Huang Jiahao Shao Peiyan Chen Shengming Tang Yi Lu Hui Yu |
| author_sort | Jie Lian |
| collection | DOAJ |
| description | Abstract Wind downscaling is crucial for refining coarse‐scale wind estimates, improving local‐scale predictions, and supporting various applications like risk assessment and planning. Dynamic downscaling models demand extensive computational resources and time, leading to a shift toward more efficient statistical downscaling, whereas it often overlooks inter‐variable and inter‐station spatial correlations. Addressing this, we propose TerraWind, a deep learning‐based downscaling method for complex terrain regions. TerraWind enhances accuracy by incorporating topographic factors and inter‐station linkages, capturing wind field interactions with terrain at multiple scales. Experimental results in Eastern China demonstrate that TerraWind reduces wind speed Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by an average of 42.6% and 33.3%, respectively, compared to three interpolation methods (bicubic, bilinear, and Inverse Distance Weighting). Furthermore, TerraWind achieves an average reduction of 35.3% in wind speed MAE and 25.6% in wind speed RMSE compared to four deep learning models (Wind‐Topo, DeepCAMS, RCM‐emulator, and Uformer). |
| format | Article |
| id | doaj-art-e1db8867007244b0bc7c80cdc23ce87e |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-e1db8867007244b0bc7c80cdc23ce87e2025-08-20T02:33:52ZengWileyGeophysical Research Letters0094-82761944-80072024-12-015123n/an/a10.1029/2024GL112124TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain RegionJie Lian0Sirong Huang1Jiahao Shao2Peiyan Chen3Shengming Tang4Yi Lu5Hui Yu6Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai ChinaShanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai ChinaShanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai ChinaShanghai Typhoon Institute China Meteorological Administration Shanghai ChinaShanghai Typhoon Institute China Meteorological Administration Shanghai ChinaShanghai Typhoon Institute China Meteorological Administration Shanghai ChinaShanghai Typhoon Institute China Meteorological Administration Shanghai ChinaAbstract Wind downscaling is crucial for refining coarse‐scale wind estimates, improving local‐scale predictions, and supporting various applications like risk assessment and planning. Dynamic downscaling models demand extensive computational resources and time, leading to a shift toward more efficient statistical downscaling, whereas it often overlooks inter‐variable and inter‐station spatial correlations. Addressing this, we propose TerraWind, a deep learning‐based downscaling method for complex terrain regions. TerraWind enhances accuracy by incorporating topographic factors and inter‐station linkages, capturing wind field interactions with terrain at multiple scales. Experimental results in Eastern China demonstrate that TerraWind reduces wind speed Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by an average of 42.6% and 33.3%, respectively, compared to three interpolation methods (bicubic, bilinear, and Inverse Distance Weighting). Furthermore, TerraWind achieves an average reduction of 35.3% in wind speed MAE and 25.6% in wind speed RMSE compared to four deep learning models (Wind‐Topo, DeepCAMS, RCM‐emulator, and Uformer).https://doi.org/10.1029/2024GL112124 |
| spellingShingle | Jie Lian Sirong Huang Jiahao Shao Peiyan Chen Shengming Tang Yi Lu Hui Yu TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region Geophysical Research Letters |
| title | TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region |
| title_full | TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region |
| title_fullStr | TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region |
| title_full_unstemmed | TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region |
| title_short | TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region |
| title_sort | terrawind a deep learning based near surface winds downscaling model for complex terrain region |
| url | https://doi.org/10.1029/2024GL112124 |
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