测绘学报 ›› 2020, Vol. 49 ›› Issue (4): 432-442.doi: 10.11947/j.AGCS.2020.20190168

• 大地测量学与导航 • 上一篇    下一篇

顾及垂直递减率函数的中国区域大气加权平均温度模型

黄良珂1,2,3, 彭华1,2, 刘立龙1,2, 李琛1,2, 康传利1,2, 谢劭峰1,2   

  1. 1. 桂林理工大学测绘地理信息学院, 广西 桂林 541004;
    2. 广西空间信息与测绘重点实验室, 广西 桂林 541004;
    3. 武汉大学卫星导航定位技术研究中心, 湖北 武汉 430079
  • 收稿日期:2019-05-10 修回日期:2019-11-27 发布日期:2020-04-17
  • 通讯作者: 刘立龙 E-mail:hn_liulilong@163.com
  • 作者简介:黄良珂(1986-),男,博士生,副教授,研究方向为GNSS气象学。E-mail:lkhuang666@163.com
  • 基金资助:
    国家自然科学基金(41704027;41664002;41864002);广西自然科学基金(2017GXNSFBA198139;2018GXNSFAA281182;2017GXNSFDA198016);广西“八桂学者”岗位专项

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

HUANG Liangke1,2,3, PENG Hua1,2, LIU Lilong1,2, LI Chen1,2, KANG Chuanli1,2, XIE Shaofeng1,2   

  1. 1. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China;
    2. Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China;
    3. GNSS Research Center, Wuhan University, Wuhan 430079, China
  • Received:2019-05-10 Revised:2019-11-27 Published:2020-04-17
  • Supported by:
    The National Natural Foundation of China (Nos. 41704027;41664002;41864002);The Guangxi Natural Science Foundation of China (Nos. 2017GXNSFBA198139;2018GXNSFAA281182;2017GXNSFDA198016);The “Ba Gui Scholars” Program of the Provincial Government of Guangxi

摘要: 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。

关键词: CTm模型, 垂直递减率函数, GNSS大气水汽, 中国区域

Abstract: The atmospheric weighted mean temperature, Tm, 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 Tm lapse rate function that considering sophisticated seasonal variations, and then a new grid Tm model for China, named as CTm, is established using gridded Tm data over an 8-year period from 2007 to 2014 provided by the global geodetic observing system (GGOS) atmosphere.Both gridded Tm data and radiosonde profiles from 2015 are treated as reference values to assess the performance of CTm. The results are compared with the Bevis formula and the GPT2w model. The results show that the CTm with the annual bias and RMS error of -0.52 K and 3.28 K when compared with gridded Tm data, respectively. In terms of RMS,the CTm has improved by approximately 27% and 13% against GPT2w-5 and GPT2w-1, respectively. While the CTm 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 CTm. Besides, the CTm has RMSPWV and RMSPWV/PWV values of 0.29 mm and 1.36% when used to estimate GNSS-PWV. The CTm model does not require any in situ meteorological parameters, thus, which has potential application for high-precision real-time GNSS-PWV retrieving in China.

Key words: CTm model, lapse rate function, GNSS precipitable water vapor, China area

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