Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine
Abstract Land Surface Temperature (LST) is widely recognized as a sensitive indicator of climate change, and it plays a significant role in ecological research. The ERA5-Land LST dataset, developed and managed by the European Centre for Medium-Range Weather Forecasts (ECMWF), is extensively used for...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83944-w |
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| author | Shiyu Li Hong Wan Qun Yu Xinyuan Wang |
| author_facet | Shiyu Li Hong Wan Qun Yu Xinyuan Wang |
| author_sort | Shiyu Li |
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| description | Abstract Land Surface Temperature (LST) is widely recognized as a sensitive indicator of climate change, and it plays a significant role in ecological research. The ERA5-Land LST dataset, developed and managed by the European Centre for Medium-Range Weather Forecasts (ECMWF), is extensively used for global or regional LST studies. However, its fine-scale application is limited by its low spatial resolution. Therefore, to improve the spatial resolution of ERA5-Land LST data, this study proposes an Attention Mechanism U-Net (AMUN) method, which combines data acquisition and preprocessing on the Google Earth Engine (GEE) cloud computing platform, to downscale the hourly monthly mean reanalysis LST data of ERA5-Land across China’s territory from 0.1° to 0.01°. This method comprehensively considers the relationship between the LST and surface features, organically combining multiple deep learning modules, includes the Global Multi-Factor Cross-Attention (GMFCA) module, the Feature Fusion Residual Dense Block (FFRDB) connection module, and the U-Net module. In addition, the Bayesian global optimization algorithm is used to select the optimal hyperparameters of the network in order to enhance the predictive performance of the model. Finally, the downscaling accuracy of the network was evaluated through simulated data experiments and real data experiments and compared with the Random Forest (RF) method. The results show that the network proposed in this study outperforms the RF method, with RMSE reduced by approximately 32–51%. The downscaling method proposed in this study can effectively improve the accuracy of ERA5-Land LST downscaling, providing new insights for LST downscaling research. |
| format | Article |
| id | doaj-art-8f723e56a05640e98fe0e0bff483af1c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-8f723e56a05640e98fe0e0bff483af1c2025-01-05T12:14:45ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-83944-wDownscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth EngineShiyu Li0Hong Wan1Qun Yu2Xinyuan Wang3College of Information Science and Engineering, Shandong Agricultural UniversityCollege of Information Science and Engineering, Shandong Agricultural UniversityCollege of Information Science and Engineering, Shandong Agricultural UniversityAerospace Information Research Institute, Chinese Academy of SciencesAbstract Land Surface Temperature (LST) is widely recognized as a sensitive indicator of climate change, and it plays a significant role in ecological research. The ERA5-Land LST dataset, developed and managed by the European Centre for Medium-Range Weather Forecasts (ECMWF), is extensively used for global or regional LST studies. However, its fine-scale application is limited by its low spatial resolution. Therefore, to improve the spatial resolution of ERA5-Land LST data, this study proposes an Attention Mechanism U-Net (AMUN) method, which combines data acquisition and preprocessing on the Google Earth Engine (GEE) cloud computing platform, to downscale the hourly monthly mean reanalysis LST data of ERA5-Land across China’s territory from 0.1° to 0.01°. This method comprehensively considers the relationship between the LST and surface features, organically combining multiple deep learning modules, includes the Global Multi-Factor Cross-Attention (GMFCA) module, the Feature Fusion Residual Dense Block (FFRDB) connection module, and the U-Net module. In addition, the Bayesian global optimization algorithm is used to select the optimal hyperparameters of the network in order to enhance the predictive performance of the model. Finally, the downscaling accuracy of the network was evaluated through simulated data experiments and real data experiments and compared with the Random Forest (RF) method. The results show that the network proposed in this study outperforms the RF method, with RMSE reduced by approximately 32–51%. The downscaling method proposed in this study can effectively improve the accuracy of ERA5-Land LST downscaling, providing new insights for LST downscaling research.https://doi.org/10.1038/s41598-024-83944-wLand surface temperature (LST)ERA5-landDownscalingGoogle Earth Engine (GEE)Deep learningAttention mechanism |
| spellingShingle | Shiyu Li Hong Wan Qun Yu Xinyuan Wang Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine Scientific Reports Land surface temperature (LST) ERA5-land Downscaling Google Earth Engine (GEE) Deep learning Attention mechanism |
| title | Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine |
| title_full | Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine |
| title_fullStr | Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine |
| title_full_unstemmed | Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine |
| title_short | Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine |
| title_sort | downscaling of era5 reanalysis land surface temperature based on attention mechanism and google earth engine |
| topic | Land surface temperature (LST) ERA5-land Downscaling Google Earth Engine (GEE) Deep learning Attention mechanism |
| url | https://doi.org/10.1038/s41598-024-83944-w |
| work_keys_str_mv | AT shiyuli downscalingofera5reanalysislandsurfacetemperaturebasedonattentionmechanismandgoogleearthengine AT hongwan downscalingofera5reanalysislandsurfacetemperaturebasedonattentionmechanismandgoogleearthengine AT qunyu downscalingofera5reanalysislandsurfacetemperaturebasedonattentionmechanismandgoogleearthengine AT xinyuanwang downscalingofera5reanalysislandsurfacetemperaturebasedonattentionmechanismandgoogleearthengine |