GPS PPP‐derived precipitable water vapor retrieval based on T_m/P_s from multiple sources of meteorological data sets in China

Abstract

Weighted mean temperature (Tm) and pressure (Ps) are two parameters of great relevance to precipitable water vapor (PWV) retrieval from global positioning system (GPS) data. However, information about the Tm and Ps cannot be available for those GPS stations that are not colocated with meteorological sensors. To investigate the optimal GPS‐PWV retrieval method for China, two enhanced Tm models, GM‐Tm (temperature dependent) and GH‐Tm (temperature independent), are developed. Additionally, the potentials of the Ps data from the two reanalysis data sets, the National Centers for Environmental Prediction (NCEP)‐Department of Energy (DOE) Reanalysis II (NCEP II) and ERA‐Interim, and from the empirical model GPT2w for GPS‐PWV retrieval are investigated over China. To evaluate the performances of multisources Tm and Ps data for GPS‐PWV retrieval, GPS data (2011–2013) collected from 22 stations of the Crustal Movement Observation Network of China (CMONOC) were processed by using the precise point positioning (PPP) technique, estimating the zenith tropospheric delay (ZTD) so as to be subsequently converted to GPS‐PWV. The retrieved GPS‐PWVs are compared with their counterparts derived from NCEP II and radiosonde data over China. The results show that (1) the GM‐Tm model consistently shows the highest accuracy (with root mean square error of 2.3 K), and the GH‐Tm model should be selected when temperature observations are not available, and that (2) the performances of Ps from NCEP II and ERA‐Interim differ marginally for GPS‐PWV retrieval, and significant seasonal variations are found in the agreement between the GPS‐PWVs and the PWVs derived from NCEP II and radiosonde data over China.

Development and Assessment of the Atmospheric Pressure Vertical Correction Model With ERA‐Interim and Radiosonde Data

“…In most cases, the meteorological observation instruments and the GNSS receiving antennas are not at the same heights. In addition, the NWP model can only provide data at the height of the grid point, and the empirical model can only provide data at the reference level of the model (Wang et al, 2016;Zhang et al, 2016). Both of the grid point height and the reference level are usually different with the height of the GNSS receiving antenna.…”

“…Currently, there are mainly two types of the methods. The first method employs the T v at the target height to conduct the correction (Böhm et al, 2014;Lagler et al, 2013;Yao et al, 2015;Zhang et al, 2016). For convenience, this method is named as the T v -based model in this paper.…”

Constructing Precipitable Water Vapor Map from Regional GNSS Network Observations without Collocated Meteorological Data

“…Due to its high quality and global coverage, ERA-Interim reanalysis has been exploited in various fields, e.g. GNSS meteorology (Wang et al, 2017;Zhang et al, 2017) and climate change research (Chen and Liu, 2016b;Lu et al, 2015). ERA-Interim reanalysis provides pressure, temperature, humidity and many other meteorological variables at 37 isobaric levels from 1000 hPa to 1 hPa with a 6 h interval.…”

“…However, a large number of GNSS stations have been deployed for positioning purposes and not equipped with collocated meteorological sensors. In this case, one may use pressure derived from a global atmospheric reanalysis (Dee et al, 2011;Zhang et al, 2017) or interpolated from nearby 20 meteorological observations (Alshawaf et al, 2015;Musa et al, 2011;Wang et al, 2007) or predicted by a blind model (Böhm et al, 2015;Wang et al, 2017). For , since the temperature and humidity profiles are very difficult to obtain, particularly in a near-real-time mode, has to be calculated from a model.…”

“…For , since the temperature and humidity profiles are very difficult to obtain, particularly in a near-real-time mode, has to be calculated from a model. An empirical model dependent on surface temperature ( ) (Bevis et al, 1994) or a blind model developed from atmospheric reanalysis products (Böhm et al, 2015;Yao et al, 2013;Zhang et al, 2017) is often employed. 25…”