Extending a model for water vapor sounding by ground-based GNSS in the vertical direction. (November 2018)

• Record Type: Journal Article
• Authors: Yao, Yibin; Sun, Zhangyu; Xu, Chaoqian; Xu, Xingyu; Kong, Jian
• Is Part Of: Journal of atmospheric and solar-terrestrial physics. Volume 179(2018)
• Page Start: 358
• Page End: 366
• Publication Date: 2018-11
• Subjects: GNSS -- PWV -- Tm -- Vertical variation -- Vertical correction model; Geophysics -- Periodicals; Atmospheric physics -- Periodicals; Géophysique -- Périodiques; Météorologie physique -- Périodiques; Electronic journals; 551.51
• DOI: 10.1016/j.jastp.2018.08.016
• Languages: English
• ISSNs: 1364-6826

Abstract

Abstract: Weighted mean temperature ( T m ) is a crucial parameter for retrieving precipitable water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology. The difference between the height of the GNSS receiver and the height of the derived T m may cause large uncertainty in the value of the retrieved PWV. In this study, we analyze the vertical variation of T m on a global scale and build a new vertical correction model using the operational reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF). Another dataset from ECMWF and the radiosonde data derived from 678 globally distributed stations are used to independently validate the performance of the newly built model and demonstrate its superiority compared with the existing vertical correction method. The results show that the lapse rate of T m has geographic and seasonal variations, and the T m has large nonlinear variation along the vertical direction in the high-latitude regions, especially in the polar regions. A performance improvement of 15%–35% compared with the existing vertical correction method is achievable for the newly built model when tested with the ECMWF data, and the improvement is 10%–25% when tested with the radiosonde data. Highlights: The vertical variation in weighted mean temperature is investigated on a global scale. A global empirical model for vertical correction of weighted mean temperature is built. The performance of vertical correction methods for weighted mean temperature is comprehensively evaluated.