GNSS大气加权平均温度经验模型精化方法的建立和分析  

Establishment and analysis of a refinement method for the GNSS empirical weighted mean temperature model

作　　者：杨飞 郭际明[2] 陈明[3] 章迪[2] YANG Fei;GUO Jiming;CHEN Ming;ZHANG Di(College of Geoscience and Surveying Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China;School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China;National Geomatics Center of China,Beijing 100830,China)

机构地区：[1]中国矿业大学(北京)地球科学与测绘工程学院,北京100083 [2]武汉大学测绘学院,湖北武汉430079 [3]国家基础地理信息中心,北京100830

出　　处：《测绘学报》2022年第11期2339-2345,共7页Acta Geodaetica et Cartographica Sinica

基　　金：北京市自然科学基金(8224093);中国博士后科学基金(2021M703510);中央高校基本科研业务费专项(2021XJDC01);煤炭资源与安全开采国家重点试验室开放基金(SKLCRSM21KFA08);地球空间环境与大地测量教育部重点试验室测绘基础研究基金(19-02-08);国家自然科学基金(41804038,42204022)。

摘　　要：加权平均温度T_(m)作为对流层湿延迟转换为大气可降水量的关键参数,在GNSS气象学研究中发挥着重要作用。T_(m)经验模型的构建,可以通过将测站位置和时间信息作为输入参数快速获取T_(m)估值,但其精度往往受限,尤其在某些局部区域。本文提出了一种T_(m)经验模型精化方法,引入了地表气温数据,通过最小二乘快速获取精化系数,达到T_(m)的误差补偿作用。基于我国及邻近区域180个探空测站2011—2015年的数据,本文构建了基于GPT3的精化模型,并对其进行分析。数值结果表明,与Bevis模型、区域线性模型和GPT3模型相比,本文提出的精化模型估计T_(m)的精度分别提高了16.2%、13.5%和21.1%。另外,基于GPT3的精化模型估计T_(m)表现出最优的时空分布结果,显著提高了高纬度地区T_(m)估计精度,有效解决了GPT3模型只能表现T_(m)季节性变化的缺陷。本文方法计算公式简便,可以快速推广至任意T_(m)经验模型,具有较高的使用价值。The weighted mean temperature(T_(m)) as a key parameter for the conversion of tropospheric wet delay to precipitable water vapor,plays an important role in the field of GNSS meteorology.Several empirical T_(m) models were established,which can provide T_(m) estimates by using the location and time information of the site as input parameters.However,the accuracy of these models is often limited,especially in some local areas.This paper proposed a refinement method for the empirical models,which introduced surface temperature,obtained the refined coefficient by using least squares and achieved the error compensation effect for estimating T_(m).Based on the 2011—2015 data of 180 radiosonde sites in China and its nearby regions,this paper carried out the establishment and analysis of the GPT3 refined model.Numerical results show that the GPT3 refined model outperformed the other three models,and improved the T_(m) accuracy by 16.2%,13.5% and 21.1%compared with the Bevis model,regional linear model and GPT3 model,respectively.In addition,the T_(m) estimated by the GPT3 refined model appeared the best spatio-temporal distribution,which significantly improved the accuracy of T_(m) estimated by other models in high latitudes,and effectively solved the defect that the GPT3 model can only describe the seasonal variation of T_(m).The formula of the proposed method is simple,which can be quickly extended to any empirical T_(m) model.

关 键 词：GNSS气象学 加权平均温度 GPT3模型 大气可降水量 

分 类 号：P228[天文地球—大地测量学与测量工程]