In precipitable water vapor (PWV) retrievals from Global Navigation Satellite System (GNSS) data, the two essential parameters, namely, surface pressure () and weighted mean temperature (), are often not available due to the lack of collocated meteorological sensors or improper data retention. Hence, this study presents a comprehensive assessment of the GNSS PWV retrieval using alternative and data from the European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis 5, National Centers for Environmental Prediction Final (NCEP FNL) Analysis, and NCEP Global Forecast System (GFS) products. The assessment was based on 691 globally distributed GNSS stations over the entire year of 2019. The zenith hydrostatic delay (ZHD) and integrated from the three types of numerical weather prediction (NWP) atmospheric profiles achieve varying accuracies in ranges of 2.4–3.0 mm and 1.1–1.5 K, respectively. PWVs estimated using ZHD and integrated from the NWP profiles obtain accuracies of about 1.6–2.0 mm. These PWVs are slightly better than those using ZHD and calculated by empirical models with surface pressure and temperature from the NWP datasets. The assessment of PWVs with the global pressure and temperature 2 wet model yields a root mean square (RMS) error of 3.73 mm. The relative RMS decreases from 30%‐40% at high latitudes (70–80°S/N) to ∼5% around the equator. The monthly variations of relative RMS show that (a) low‐latitude regions outperform the high‐latitude regions, and (b) winter months have significantly worse performance than other months in both hemispheres.

The geomagnetic storm, which is an abnormal space weather phenomenon, can sometimes severely affect GPS signal propagation, thereby impacting the performance of GPS precise point positioning (PPP). However, the investigation of GPS PPP accuracy over the global scale under different geomagnetic storm conditions is very limited. This paper for the first time presents the performance of GPS dual-frequency (DF) and single-frequency (SF) PPP under moderate, intense, and super storms conditions during solar cycle 24 using a large data set collected from about 500 international GNSS services (IGS) stations. The global root mean square (RMS) maps of GPS PPP results show that stations with degraded performance are mainly distributed at high-latitude, and the degradation level generally depends on the storm intensity. The three-dimensional (3D) RMS of GPS DF PPP for high-latitude during moderate, intense, and super storms are 0.393 m, 0.680 m and 1.051 m, respectively, with respect to only 0.163 m on quiet day. RMS errors of mid- and low-latitudes show less dependence on the storm intensities, with values less than 0.320 m, compared to 0.153 m on quiet day. Compared with DF PPP, the performance of GPS SF PPP is inferior regardless of quiet or disturbed conditions. The degraded performance of GPS positioning during geomagnetic storms is attributed to the increased ionospheric disturbances, which have been confirmed by our global rate of TEC index (ROTI) maps. Ionospheric disturbances not only lead to the deteriorated ionospheric correction but also to the frequent cycle-slip occurrence. Statistical results show that, compared with that on quiet day, the increased cycle-slip occurrence are 13.04%, 56.52%, and 69.57% under moderate, intense, and super storms conditions, respectively.

Surface pressure (P_s) and weighted mean temperature (T_m) are two necessary variables for the accurate retrieval of precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) zenith total delay (ZTD) estimates. The lack of P_s or T_m information is a concern for those GNSS sites that are not collocated with meteorological sensors. This paper investigates an alternative method of inferring accurate P_s and T_m at the GNSS station using nearby synoptic observations. P_s and T_m obtained at the nearby synoptic sites are interpolated onto the location of the GNSS station by performing both vertical and horizontal adjustments, in which the parameters involved in P_s and T_m calculation are estimated from ERA-Interim reanalysis profiles. In addition, we present a method of constructing high-quality PWV maps through vertical reduction and horizontal interpolation of the retrieved GNSS PWVs. To evaluate the performances of the P_s and T_m retrieval, and the PWV map construction, GNSS data collected from 58 stations of the Hunan GNSS network and synoptic observations from 20 nearby sites in 2015 were processed to extract the PWV so as to subsequently generate the PWV maps. The retrieved P_s and T_m and constructed PWV maps were assessed by the results derived from radiosonde and the ERA-Interim reanalysis. The results show that (1) accuracies of P_s and T_m derived by synoptic interpolation are within the range of 1.7–3.0 hPa and 2.5–3.0 K, respectively, which are much better than the GPT2w model; (2) the constructed PWV maps have good agreements with radiosonde and ERA-Interim reanalysis data with the overall accuracy being better than 3 mm; and (3) PWV maps can well reveal the moisture advection, transportation and convergence during heavy rainfall.

GNSS ionospheric tomography technique is capable to reconstruct the high-quality 3D ionospheric electron density (IED) images with a relatively low cost. We present a new parameterized approach for refining the voxel-based ionospheric tomography modeling. This approach is different to most other voxel-based techniques as they assume a homogeneous IED distribution in each voxel that is unreasonable for the tomography modeling. In this method, IED of any point within a voxel is determined via vertically exponential interpolation and horizontally inverse distance weighted interpolation from the IED values at the eight corners of that voxel. The parameterized tomography is tested with real data collected over the period of June 1–30, 2015 from 45 GPS stations in south China. The superiority of the new parameterized method is verified by comparison with the traditional nonparametric method. The new parameterized method outperforms the traditional method by 12%, 10%, 5% and 2% for vertical resolutions of 25 km, 50 km, 75 km and 100 km, respectively, in the self-consistency validation by GPS data. Such improvements are 20%, 24%, 22% and 16%, respectively, when assessed by the Swarm in situ IEDs. In terms of the vertical layer discretization, configurations using the resolution of 25 km generally performs better than the other three vertical resolutions. Overall, the parameterized method using a vertical resolution of 25 km achieves the best performance from the comprehensive comparisons with ionospheric data derived by GPS, ionosonde and Swarm satellites.

Precipitable water vapor (PWV) products from the second generation of China’s geostationary meteorological satellite Fengyun-4A (FY-4A) have the advantage of high spatiotemporal resolution and can play an increasingly important role in the study of atmosphere and climate. Using the radiosonde, the global navigation satellite system (GNSS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 (ERA5) reanalysis data, this study presented a comprehensive evaluation of PWV products from FY-4A for a one-year period from January 2019 to January 2020. Results indicated that FY-4A PWV data have a good agreement with radiosonde and GNSS measured ones with the same correlation coefficient of 0.976, and the root mean square errors (RMSEs) are 3.95 and 3.73 mm, respectively. Compared with the radiosonde and GNSS, the FY-4A Advanced Geostationary Radiation Imager (AGRI) was found to underestimate the water vapor during humid conditions when the PWV is greater than 50 mm. The magnitude of underestimation increases with the growth in water vapor content. In terms of spatial variability, the RMSE of FY-4A PWV decreases with the increase in latitude, while the relative RMSE (R-RMSE) displays an opposite pattern. RMSE from the comparison between FY-4A and ERA5 PWV varies from 0 to 6 mm depending upon the location. Statistics showed that 55.08%, 59.79%, and 83.13% RMSE values are less than 4 mm in the evaluation by radiosonde, GNSS, and ERA5, respectively. The seasonal and diurnal variations of RMSE showed that: 1) summer exhibited larger RMSE than winter and 2) daytime obtained slightly worse performance than nighttime.