中国南部地区大气加权平均温度模型精化研究

针对中国南部地区地势西高东低、沿海与内陆存在差异等情况，分析中国南部地区T_m与地面温度、测站高度、季节变化以及纬度的关系，利用中国南部地区19个探空站2015~2017年的探空数据，在Bevis公式的基础上建立只考虑地面温度的线性模型（T_m-SC1模型）和与地面温度、高程、季节变化以及纬度有关的新T_m模型（T_m-SC2模型）。以2018年的探空数据为参考值，对T_m-SC1模型和T_m-SC2模型进行精度验证，并与广泛使用的Bevis公式和GPT3模型进行精度比较。结果表明，T_m-SC1模型的年均偏差和均方根误差（RMS）分别为0.76 K和2.57 K，相比Bevis模型和GPT3模型，其精度（RMS值）分别提高13.8%和2.2%；T_m-SC2模型的年均偏差和均方根误差（RMS）分别为-0.10 K和1.64 K，相比Bevis模型和GPT3模型其精度（RMS值）分别提高44.9%和37.6%。T_m-SC2模型用于GNSS水汽计算导致的理论RMS误差和相对误差分别为0.16 mm和0.43%。因此，T_m-SC2模型更适用于中国南部地区的GNSS水汽探测以及气象研究。

Refinement of Atmospheric Weighted Mean Temperature Model for Southern China

In southern China the terrain is high in the west and low in the east, and there are differences between the coast and the inland. Using 19 sounding stations in southern China from 2015-2017, we analyze the relationship between T_m and station height, ground temperature, seasonal variation and latitude. For the annual sounding data, we establish a linear model (T_m-SC1) that only considers ground temperature and a newT_m model (T_m-SC2) related to ground temperature, elevation, seasonal changes, and latitude on the basis of the Bevis formula. Using the sounding data in 2018 as a reference, we analyze the accuracy of the models. We compare the accuracy with the widely used Bevis formula and GPT3 model. The results show that the average annual deviation and root mean square error (RMS) of theT_m-SC1 model are 0.76 K and 2.57 K, respectively. Compared with the Bevis model and the GPT3 model, the accuracy (RMS value) is increased by 13.8% and 2.2%, respectively. The annual average deviation and root mean square error (RMS) of theT_m-SC2 model are -0.10 K and 1.64 K, respectively. Compared with the Bevis model and the GPT3 model, the accuracy (RMS value) is increased by 44.9% and 37.6%, respectively. The theoretical RMS error and relative error of water vapor calculation caused by theT_m-SC2 model used in GNSS water vapor calculation are 0.16 mm and 0.43%, respectively. Therefore, theT_m-SC2 model is more suitable for GNSS water vapor detection and meteorological research in southern China.