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| DOI | 10.3390/su16051934 |
| Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning | |
| 发表日期 | 2024 |
| EISSN | 2071-1050 |
| 起始页码 | 16 |
| 结束页码 | 5 |
| 卷号 | 16期号:5 |
| 英文摘要 | High-resolution air temperature distribution data are of crucial significance for studying climate change and agriculture in the Yellow River Basin. Obtaining accurate and high-resolution air temperature data has been a persistent challenge in research. This study selected the Yellow River Basin as its research area and assessed multiple variables, including the land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope, aspect, longitude, and latitude. We constructed three downscaling models, namely, ET, XGBoost, and LightGBM, and applied a stacking ensemble learning algorithm to integrate these three models. Through this approach, ERA5-Land reanalysis air temperature data were successfully downscaled from a spatial resolution of 0.1 degrees to 1 km, and the downscaled results were validated using observed data from meteorological stations. The results indicate that the stacking ensemble model significantly outperforms the three independent machine learning models. The integrated model, combined with the selected set of multiple variables, provides a feasible approach for downsizing ERA5 air temperature data. The stacking ensemble model not only effectively enhances the spatial resolution of ERA5 reanalysis air temperature data but also improves downscaled results to a certain extent. The downscaled air temperature data exhibit richer spatial texture information, better revealing spatial variations in air temperature within the same land class. This research outcome provides robust technical support for obtaining high-resolution air temperature data in meteorologically sparse or topographically complex regions, contributing significantly to climate, ecosystem, and sustainable development research. |
| 英文关键词 | the Yellow River Basin; ERA5-Land; downscaling; air temperature; stacking ensemble learning |
| 语种 | 英语 |
| WOS研究方向 | Science & Technology - Other Topics ; Environmental Sciences & Ecology |
| WOS类目 | Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies |
| WOS记录号 | WOS:001182935800001 |
| 来源期刊 | SUSTAINABILITY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/288875 |
| 作者单位 | Northwest Normal University - China; Lanzhou Jiaotong University |
| 推荐引用方式 GB/T 7714 | . Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning[J],2024,16(5). |
| APA | (2024).Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning.SUSTAINABILITY,16(5). |
| MLA | "Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning".SUSTAINABILITY 16.5(2024). |
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