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

Abstract

Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (Tm) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

Investigation of Antarctic Precipitable Water Vapor Variability and Trend from 18 Year (2001 to 2018) Data of Four Reanalyses Based on Radiosonde and GNSS Observations

“…Potential factors contributing to the bias may be the antenna phase center variation at GNSS stations, which directly impacts the GNSS PWV estimates [56], or uncertainty in radiosonde observations [57] or the position offsets between the radiosonde and GNSS stations [58]. In terms of RMS error, the overall mean RMS error at the five sites is 1.12 mm with the range of 0.94-1.68 mm, which shows high accuracy and is consistent with the findings of previous studies [12,15]. Therefore, the GNSS-derived PWV could be used as a reference to assess the PWV estimates derived from reanalyses.…”

“…The PWV observations obtained from this category are measured by the sensors and have the advantage of high accuracy. Among them, ground-based GNSS can detect the signal zenith delay and further estimate the PWV with its long-term stability, weather independent and high temporal resolution compared to other PWV measurements such as radiosondes and satellite remote sensing [15][16][17]. However, since the sensors from radiosondes and satellites are of high cost and poor performance under harsh climate conditions, and since the observations are often sparsely distributed and incomplete, it is difficult to estimate accurate PWV variability and trend from the observational data.…”

Estimation of IRNSS-retrieved integrated precipitable water over a few locations in India

“…Studies found good agreement between the radiosonde-retrieved and GNSS-retrieved PW(Huang et al 2021), and GPS-retrieved PW(Choy et al 2015) Jaiswal et al (2021). found a very good agreement between the GPS-based IPWV and that retrieved from the radiosonde over Bangalore.4 ConclusionsThe solar cycle controls IPWV, as evident from TSI vs. IPWV relation.…”

Accuracy Evaluation and Analysis of GNSS Tropospheric Delay Inversion from Meteorological Reanalysis Data

“…The current uncertainty of ZTD inversion and modeling is an irregular periodic and stochastic variation in tropospheric delay. Previous studies have shown that the stochastic variation in ZTD is related to the atmospheric turbulence in the area where the station is located [42], and the large increase or decrease in ZTD over a short period of time is often the result of the influence of strong convective weather; it is also difficult to describe such drastic changes with traditional models [43,44]. Figure 6 shows the ZTD time series for the BJFS and WUH2 stations in 2020 using the three types of reanalysis data and the ZTD change under storm conditions.…”