| 顾及垂直递减率函数的中国区域大气加权平均温度模型 |
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| 引用本文： | 黄良珂,彭华,刘立龙,李琛,康传利,谢劭峰. 顾及垂直递减率函数的中国区域大气加权平均温度模型[J]. 测绘学报, 2020, 49(4): 432-442. DOI: 10.11947/j.AGCS.2020.20190168 |
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| 作者姓名： | 黄良珂 彭华 刘立龙 李琛 康传利 谢劭峰 |
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| 作者单位： | 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 |
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| 基金项目： | 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 |
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| 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1...
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| 关 键 词： | CTm模型 垂直递减率函数 GNSS大气水汽 中国区域 |
| 收稿时间： | 2019-05-10 |
| 修稿时间： | 2019-11-27 |
An empirical atmospheric weighted mean temperature model considering the lapse rate function for China |
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| HUANG Liangke,PENG Hua,LIU Lilong,LI Chen,KANG Chuanli,XIE Shaofeng. An empirical atmospheric weighted mean temperature model considering the lapse rate function for China[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(4): 432-442. DOI: 10.11947/j.AGCS.2020.20190168 |
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| Authors: | HUANG Liangke PENG Hua LIU Lilong LI Chen KANG Chuanli XIE Shaofeng |
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| Affiliation: | 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 |
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| 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. |
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| Keywords: | CTm model lapse rate function GNSS precipitable water vapor China area |
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