顾及垂直递减率函数的中国区域大气加权平均温度模型

大气加权平均温度(Tm)是全球导航卫星系统(GNSS)水汽监测的关键参数。针对中国区域地形起伏较大的特点,本文构建了顾及精细季节变化的Tm垂直递减率函数模型,在此基础上,利用2007—2014年的Global Geodetic Observing System(GGOS)atmosphere格网数据建立了中国区域的Tm格网新模型(简称为CTm模型)。以2015年GGOS格网数据和无线电探空资料为参考值,对CTm模型进行精度检验,并与常用的Bevis公式和GPT2w模型进行比较分析。结果表明:①以GGOS格网数据为参考值,CTm模型的年均偏差和均方根误差(RMS)分别为-0.52 K和3.28 K,相比于GPT2w-5和GPT2w-1模型,精度(RMS值)分别提高了27%和13%;②以探空数据为参考值,CTm模型的年均偏差和RMS误差分别为0.26 K和3.75 K,相对于GPT2w-5和GPT2w-1模型,精度分别提高了21%和16%,尤其在中国西部地区,CTm模型表现出更为显著的优势。此外,将CTm模型用于GNSS水汽计算,其引起的水汽计算RMS误差和相对误差分别为0.29 mm和1.36%。CTm模型不需要实测气象参数,因此,在中国区域的GNSS实时高精度水汽探测中具有重要的应用。

An empirical atmospheric weighted mean temperature model considering the lapse rate function for China

The atmospheric weighted mean temperature, T_m, plays an important role in the process of retrieving precipitable water vapor from Global Navigation Satellite System (GNSS) signals. Aiming at the characteristics of complex terrain in China,we develop a T_m lapse rate function that considering sophisticated seasonal variations, and then a new grid T_m model for China, named as CT_m, is established using gridded T_m data over an 8-year period from 2007 to 2014 provided by the global geodetic observing system (GGOS) atmosphere.Both gridded T_m data and radiosonde profiles from 2015 are treated as reference values to assess the performance of CT_m. The results are compared with the Bevis formula and the GPT2w model. The results show that the CT_m with the annual bias and RMS error of -0.52 K and 3.28 K when compared with gridded T_m data, respectively. In terms of RMS,the CT_m has improved by approximately 27% and 13% against GPT2w-5 and GPT2w-1, respectively. While the CT_m has the annual bias and RMS error of 0.26 K and 3.75 K against radiosonde data, and which has improved by approximately 21% and 16% against GPT2w-5 and GPT2w-1, respectively,especially in Western China, where the significant performance was observed for CT_m. Besides, the CT_m has RMS_PWV and RMS_PWV/PWV values of 0.29 mm and 1.36% when used to estimate GNSS-PWV. The CT_m model does not require any in situ meteorological parameters, thus, which has potential application for high-precision real-time GNSS-PWV retrieving in China.