An improved global Tm stratification model for GNSS-PWV retrieval

Abstract

The atmospheric weighted mean temperature (Tm) is a key parameter for converting tropospheric wet delay (ZWD) into precipitable water vapor (PWV) and plays a vital role in Global Navigation Satellite System (GNSS) meteorology. Existing Tm models suffer from limitations such as the lack of consideration of full periodicity, poor accuracy at high altitudes, and the adoption of single-resolution grid data for modeling. Hence, an improved global Tm stratification model is developed based on ERA5 hourly reanalysis data from 2015 to 2019, called IGTmS, which takes seasonal and intraday variations into consideration and adopts a stratification mode to improve the vertical accuracy of Tm estimation. The accuracy and applicability of the IGTmS model are evaluated using Tm data at 417 radiosonde stations in 2020. The results show that the mean bias and RMS of Tm for the IGTmS model are −0.34 K and 3.53 K, and the accuracy is improved by approximately 7.8 %, 4.6 % and 2.5 % compared with that of GPT3, GTm-III and GTrop models on a global scale, respectively. IGTmS model can achieve the mean σPWV and σPWV/PWV values of 0.38 mm and 1.3 %, respectively, which shows optimal performance among the four models, especially in the Antarctic and Tibetan Plateau regions. Furthermore, three resolutions of the IGTmS models have been developed and all show high accuracy compared to GPT3 models. Users can choose the suitable model according to the desired accuracy and resolution. The developed stratification model IGTmS can effectively weaken the influence of large height difference on Tm estimation and can obtain high-accuracy and stable Tm over the globe and provide data support for real-time GNSS PWV retrieval.