A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling
Abstract High‐resolution wind fields has always been the goal of refined meteorological forecasting. Using advanced deep learning algorithms for wind downscaling is an effective approach to achieve this goal. However, the lack of physical process understanding in deep learning algorithms results in...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
|
| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024JH000147 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849432434313003008 |
|---|---|
| author | Anboyu Guo Qiyang Liu Jiuke Wang Yuanyong Gao Lixin Wei Xiaojiang Song Chi Zhang Kai Liu Shuping Ma Jiu Jiang |
| author_facet | Anboyu Guo Qiyang Liu Jiuke Wang Yuanyong Gao Lixin Wei Xiaojiang Song Chi Zhang Kai Liu Shuping Ma Jiu Jiang |
| author_sort | Anboyu Guo |
| collection | DOAJ |
| description | Abstract High‐resolution wind fields has always been the goal of refined meteorological forecasting. Using advanced deep learning algorithms for wind downscaling is an effective approach to achieve this goal. However, the lack of physical process understanding in deep learning algorithms results in the inability to accurately reconstruct fine‐scale structures after downscaling. In this study, we propose a Terrain‐Constraint Wind Downscaling Model (TCWDM), a lightweight deep learning model consisting of a downscaling module and a terrain‐constraint module. By combining low‐resolution wind with high‐resolution terrain data, the model achieves a tenfold downscaling of spatial wind fields and reconstructs the detailed structure of the wind. Due to the incorporation of an attention mechanism, multi‐feature inputs, and the terrain‐constraint module, TCWDM demonstrates superior downscaling performance. Compared to traditional interpolation methods and other deep learning models, the mean absolute error is reduced by up to 49%. The terrain‐constraint module, in particular, contributes most significantly to the model's performance, especially in complex terrains, where it enables greater optimization of downscaling results. Furthermore, due to the lightweight model structure and a specific fine‐tuning strategy, TCWDM can deliver significantly better downscaling results at a lower cost across different regions, offering potential for broader applications. |
| format | Article |
| id | doaj-art-5bb09888e22b4191a93163288e7e43b3 |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-5bb09888e22b4191a93163288e7e43b32025-08-20T03:27:22ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2024JH000147A Lightweight Terrain‐Constraint Model for Wind Spatial DownscalingAnboyu Guo0Qiyang Liu1Jiuke Wang2Yuanyong Gao3Lixin Wei4Xiaojiang Song5Chi Zhang6Kai Liu7Shuping Ma8Jiu Jiang9National Marine Environment Forecasting Center Beijing ChinaEast China Electric Power Design Institute of China Power Engineering Consulting Group Corporation Shanghai ChinaSchool of Artificial Intelligence Sun Yat sen University Zhuhai ChinaNational Marine Environment Forecasting Center Beijing ChinaNational Marine Environment Forecasting Center Beijing ChinaNational Marine Environment Forecasting Center Beijing ChinaNational Marine Environment Forecasting Center Beijing ChinaNational Marine Environment Forecasting Center Beijing ChinaDepartment of Civil and Environmental Engineering National University of Singapore Singapore SingaporeCollege of Geography and Remote Sensing Sciences Xinjiang University Urumqi ChinaAbstract High‐resolution wind fields has always been the goal of refined meteorological forecasting. Using advanced deep learning algorithms for wind downscaling is an effective approach to achieve this goal. However, the lack of physical process understanding in deep learning algorithms results in the inability to accurately reconstruct fine‐scale structures after downscaling. In this study, we propose a Terrain‐Constraint Wind Downscaling Model (TCWDM), a lightweight deep learning model consisting of a downscaling module and a terrain‐constraint module. By combining low‐resolution wind with high‐resolution terrain data, the model achieves a tenfold downscaling of spatial wind fields and reconstructs the detailed structure of the wind. Due to the incorporation of an attention mechanism, multi‐feature inputs, and the terrain‐constraint module, TCWDM demonstrates superior downscaling performance. Compared to traditional interpolation methods and other deep learning models, the mean absolute error is reduced by up to 49%. The terrain‐constraint module, in particular, contributes most significantly to the model's performance, especially in complex terrains, where it enables greater optimization of downscaling results. Furthermore, due to the lightweight model structure and a specific fine‐tuning strategy, TCWDM can deliver significantly better downscaling results at a lower cost across different regions, offering potential for broader applications.https://doi.org/10.1029/2024JH000147wind field downscalinglightweight modelcomplex terrainterrain‐constraint |
| spellingShingle | Anboyu Guo Qiyang Liu Jiuke Wang Yuanyong Gao Lixin Wei Xiaojiang Song Chi Zhang Kai Liu Shuping Ma Jiu Jiang A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling Journal of Geophysical Research: Machine Learning and Computation wind field downscaling lightweight model complex terrain terrain‐constraint |
| title | A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling |
| title_full | A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling |
| title_fullStr | A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling |
| title_full_unstemmed | A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling |
| title_short | A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling |
| title_sort | lightweight terrain constraint model for wind spatial downscaling |
| topic | wind field downscaling lightweight model complex terrain terrain‐constraint |
| url | https://doi.org/10.1029/2024JH000147 |
| work_keys_str_mv | AT anboyuguo alightweightterrainconstraintmodelforwindspatialdownscaling AT qiyangliu alightweightterrainconstraintmodelforwindspatialdownscaling AT jiukewang alightweightterrainconstraintmodelforwindspatialdownscaling AT yuanyonggao alightweightterrainconstraintmodelforwindspatialdownscaling AT lixinwei alightweightterrainconstraintmodelforwindspatialdownscaling AT xiaojiangsong alightweightterrainconstraintmodelforwindspatialdownscaling AT chizhang alightweightterrainconstraintmodelforwindspatialdownscaling AT kailiu alightweightterrainconstraintmodelforwindspatialdownscaling AT shupingma alightweightterrainconstraintmodelforwindspatialdownscaling AT jiujiang alightweightterrainconstraintmodelforwindspatialdownscaling AT anboyuguo lightweightterrainconstraintmodelforwindspatialdownscaling AT qiyangliu lightweightterrainconstraintmodelforwindspatialdownscaling AT jiukewang lightweightterrainconstraintmodelforwindspatialdownscaling AT yuanyonggao lightweightterrainconstraintmodelforwindspatialdownscaling AT lixinwei lightweightterrainconstraintmodelforwindspatialdownscaling AT xiaojiangsong lightweightterrainconstraintmodelforwindspatialdownscaling AT chizhang lightweightterrainconstraintmodelforwindspatialdownscaling AT kailiu lightweightterrainconstraintmodelforwindspatialdownscaling AT shupingma lightweightterrainconstraintmodelforwindspatialdownscaling AT jiujiang lightweightterrainconstraintmodelforwindspatialdownscaling |