| 区域大气加权平均温度模型构建与适用性分析 |
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| 引用本文： | 王朋远,李飞,郑南山.区域大气加权平均温度模型构建与适用性分析[J].全球定位系统,2018,43(3):82-87. |
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| 作者姓名： | 王朋远 李飞 郑南山 |
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| 作者单位： | 1.珠海市测绘院,广东 珠海 519015 |
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| 摘 要： | 加权平均温度作为GNSS水汽反演的重要参数,直接影响大气可降水量的反演精度,而建立区域化加权平均温度模型有助于提高水汽反演精度。利用香港探空站2012-2015年数据资料,在分析加权平均温度与地面气象要素关系的基础上,运用最小二乘原理探究最优回归方程系数,回归建立了区域加权平均温度的单因素模型和多因素模型。结果表明:多因素模型精度高于单因素模型,但并不显著,Bevis经验公式应用于香港区域时不满足精度要求;对模型精度和适用性进行了分析比较,表明文中建立的模型精度较高,能更好满足水汽遥感高精度的要求。
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| 关 键 词： | GNSS水汽反演 加权平均温度 单因素模型 多因素模型 |
Construction and Applicability Analysis of Regional Atmospheric Weighted Average Temperature Model |
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| Affiliation: | 1.Zhuhai Institute of Surveying and Mapping, Zhuhai?519015, ChinaThe Architectural Engineering College,Guangdong Polytechnic of?Science and Technology, Zhuhai?519090,ChinaSchool of Environment Science and Spatial Informatics,China University of Mining &Technology, Xuzhou 221116, China |
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| Abstract: | As an important parameter in atmospheric water vapor inversion by GNSS, weighted average temperature directly affects the inversion accuracy of atmospheric Precipitable Water Vapor (PWV), and establishment of a localization-weighted average temperature model is helpful to improve water vapor inversion accuracy. Based on the analysis of the relationship between the weighted average temperature and the surface meteorological elements by using Hong Kong sounding station’s data from 2012 to 2015, single-factor model and muti-factor model of the regional weighted average temperature are established with the least square principle which is used to explore the optimal regression equation coefficients. The results show that the accuracy of the multi-factor model is higher than that of the single-factor model, but it is not significant. The Bevis empirical formula does not meet the accuracy requirements when applied to the Hong Kong region; the accuracy and applicability of the model are analyzed and compared, indicating that the accuracy of the model established in this paper is high, and it can better meet the high-precision requirements of water vapor remote sensing. |
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