Hourly PWV Dataset Derived from GNSS Observations in China

Abstract

The rapid variation of atmospheric water vapor is important for a regional hydrologic cycle and climate change. However, it is rarely investigated in China, due to the lack of a precipitable water vapor (PWV) dataset with high temporal resolution. Therefore, this study focuses on the generation of an hourly PWV dataset using Global Navigation Satellite System (GNSS) observations derived from the Crustal Movement Observation Network of China. The zenith total delay parameters estimated by GAMIT/GLOBK software are used and validated with an average root mean square (RMS) error of 4–5 mm. The pressure (P) and temperature (T) parameters used to calculate the zenith hydrostatic delay (ZHD) and weighted average temperature of atmospheric water vapor (Tm) are derived from the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting (ECMWF ERA5) products. The values of P and T at the GNSS stations are obtained by interpolation in the horizontal and vertical directions using empirical formulas. Tm is calculated at the GNSS stations using the improved global pressure and temperature 2 wet (IGPT2w) model in China with an RMS of 3.32 K. The interpolated P and T are validated by interpolating the grid-based ERA5 data into radiosonde stations. The average RMS and bias of P and T in China are 2.71/−1.11 hPa and 1.88/−0.51 K, respectively. Therefore, the error in PWV with a theoretical RMS of 1.85 mm over the period of 2011–2017 in China can be obtained. Finally, the hourly PWV dataset in China is generated and the practical accuracy of the generated PWV dataset is validated using the corresponding AERONET and radiosonde data at specific stations. Numerical results reveal that the average RMS values of the PWV dataset in the four geographical regions of China are less than 3 mm. A case analysis of the PWV diurnal variations as a response to the EI Niño event of 2015–2016 is performed. Results indicate the capability of the hourly PWV dataset of monitoring the rapid water vapor changes in China.

Evaluation of Hourly PWV Products Derived From ERA5 and MERRA‐2 Over the Tibetan Plateau Using Ground‐Based GNSS Observations by Two Enhanced Models

“…Similar investigations were later carried out over China and East Africa for ERA5 by Zhao et al. (2020) and Ssenyunzi et al. (2020), respectively.…”

“…Therefore, it is essential to unify the PWV values at different heights to reduce the impact of these height differences. Previous studies (Leckner et al., 1978; Li et al., 2020; Wang et al., 2020; H. Zhang et al., 2019; Zhao et al., 2020) have used the widely common empirical correction function of PWV (E‐PWVC model) to unify the PWV values of stations at different heights. However, due to the significant seasonal variations in PWV over the TP (Zhang et al., 2013), systematic errors are generated when using such a simple correction formula to adjust the PWV.…”

“…A comprehensive assessment of ERA5 and MERRA‐2 PWV products has yet to be fully explored at the hourly scale over the TP with the help of numerous and reliable ground‐based GNSS observations. Moreover, in previous studies, several widely common T m models and a PWV vertical adjustment method are adopted in the comparison between reanalysis data and GNSS observations (Wang et al., 2020; H. Zhang et al., 2019; Zhao et al., 2020). However, due to the substantially undulating terrain and complex climate conditions of the TP, the uncertainties of reanalysis data set evaluation would increase when using such models in the TP.…”

High-Precision GNSS PWV and Its Variation Characteristics in China Based on Individual Station Meteorological Data

“…We tested our model with radiosonde data from 2011 to 2019, and the results showed that our model has the best accuracy. The spatial distribution characteristics of the PWV are consistent with other studies in different years [39,40]. There are bimodal characteristics of the PWV in Southern China, and the formation mechanism of the bimodal characteristics remains to be further studied.…”

Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China

“…PWV is greatly affected in the vertical direction, and the PWVs at different heights must be unified to reduce the impact of height differences on the PWV comparisons. The empirical correction model of PWV is used as follows (Zhang et al, 2019a;Zhao et al, 2019b):…”