气候变化领域集成服务门户(CCPortal): Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning

CCPortal

DOI	10.3390/su16051934
	Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning
	Zhang, Yuna; Li, Jing; Liu, Deren
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/288876
作者单位	Northwest Normal University - China; Lanzhou Jiaotong University
推荐引用方式 GB/T 7714	Zhang, Yuna,Li, Jing,Liu, Deren. Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning[J],2024,16(5).
APA	Zhang, Yuna,Li, Jing,&Liu, Deren.(2024).Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning.SUSTAINABILITY,16(5).
MLA	Zhang, Yuna,et al."Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning".SUSTAINABILITY 16.5(2024).