期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 837, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2022.155887
关键词
Extreme air temperature; Land surface temperature; Remote sensing; Machine learning; Heat wave; East China
资金
- National Natural Science Foundation of China [41771360, 41975044, 41801021]
- Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences (Wuhan) [CUGL170401, CUGCJ1704]
- Fundamental Research Funds for National Universities, China University of Geosciences (Wuhan)
In this study, a method using error prediction and correction was proposed to improve the estimation of extreme temperatures. The accuracy of the method was compared with a widely used method in previous studies, and the results showed that it reduced the errors in extreme temperature estimation and produced satisfactory estimates of heat and cold wave magnitudes.
complete spatial coverage and strong relationships with Ta (e.g., elevation and land surface temperature). Therefore, Ta can be mapped using in situ Ta and satellite data. However, this method may have a large bias when estimating the extreme Ta. In this study, the error prediction and correction (EPC) method, incorporating Cubist machine learning algorithm, was proposed to improve the estimation of extreme Ta. The accuracy of the EPC method was compared with that of the widely used method in previous studies in east China from 2003 to 2012. The mean absolute errors (MAEs) of the estimated daily Ta using the EPC method ranged from 0.75-1.01 degrees C, which were 0.57-0.96 degrees C lower than that of the method in the literature. The biases of the estimated Ta obtained using the two methods were close to zero. However, the biases can be as high as 7.10 degrees C when Ta is extremely low and as low as -3.09 degrees C when Ta is extremely high. Compared with the method in the literature, the EPC method can reduce the MAE by 1.41 degrees C, root mean square error by 1.49 degrees C, and bias by 1.61 degrees C of the estimated extreme Ta. Additionally, the EPC method produced satisfactory accuracy (MAEs <0.9 degrees C) of the estimated heat and cold wave magnitudes. Finally, a 1 km resolution daily Ta map in east China from 2003 to 2012 was developed, which will be useful data in multiple research fields.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据