Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China.
Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China,; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China,.
Sci Total Environ. 2022 Sep 1;837:155887. doi: 10.1016/j.scitotenv.2022.155887. Epub 2022 May 12.
Air temperature (Ta) data obtained from meteorological stations were spatially discontinuous. Some satellite data have 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 °C, which were 0.57-0.96 °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 °C when Ta is extremely low and as low as -3.09 °C when Ta is extremely high. Compared with the method in the literature, the EPC method can reduce the MAE by 1.41 °C, root mean square error by 1.49 °C, and bias by 1.61 °C of the estimated extreme Ta. Additionally, the EPC method produced satisfactory accuracy (MAEs <0.9 °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.
气象站获得的气温 (Ta) 数据在空间上是不连续的。一些卫星数据具有完整的空间覆盖范围，并且与 Ta 有很强的相关性（例如海拔和地表温度）。因此，可以利用实地 Ta 和卫星数据来绘制 Ta。然而，这种方法在估计极端 Ta 时可能会有很大的偏差。在这项研究中，提出了一种结合 Cubist 机器学习算法的误差预测和校正 (EPC) 方法，以提高对极端 Ta 的估计。将 EPC 方法的准确性与 2003 年至 2012 年在中国东部研究中广泛使用的方法进行了比较。EPC 方法估计的日平均气温的平均绝对误差 (MAE) 范围为 0.75-1.01°C，比文献中的方法低 0.57-0.96°C。两种方法估计的 Ta 的偏差接近零。然而，当 Ta 极低时，偏差可达 7.10°C，当 Ta 极高时，偏差可达-3.09°C。与文献中的方法相比，EPC 方法可以将估计的极端 Ta 的 MAE 降低 1.41°C，均方根误差降低 1.49°C，偏差降低 1.61°C。此外，EPC 方法产生了令人满意的估计热和冷浪幅度的精度（MAE<0.9°C）。最后，开发了 2003 年至 2012 年中国东部 1 公里分辨率的日 Ta 图，这将是多个研究领域有用的数据。
