Zenith Tropospheric Delay Research Articles

The second reprocessing campaigns of historical observations in the GNSS Data Analysis Centre of MAO NAS of Ukraine

The second reprocessing campaign of historical observations of GNSS satellites at permanent stations located in Ukraine and in the Eastern Europe for GPS weeks 935–1708 (December 7, 1997 – October 6, 2012) was carried out in the GNSS Data Analysis Centre of the Main Astronomical Observatory NAS of Ukraine with using products updated in IGS repro2 and EPN-Repro2 campaigns – precise ephemerides of GPS and GLONASS satellites, coordinates and velocities of reference permanent GNSS stations, etc. The observations was analyzed with the Bernese GNSS Software ver. 5.2 according to the requirements of the EUREF Permanent GNSS Network (EPN), that were valid at that time. In total, observations on 72 GNSS stations, including 48 Ukrainian stations belonging to the following operators of GNSS networks: MAO NAS of Ukraine, Research Institute of Geodesy and Cartography, TNT TPI company (TNT TPI GNSS Network), PJSC System Solutions (System.NET), Lviv Polytechnic National University, UNAVCO, Inc. (USA), were processed. The IGb08 reference frame was realized by applying No-Net-Translation conditions on the coordinates of the IGS Reference Frame stations. As result, the stations’ coordinates in the IGb08 reference frame and the zenith tropospheric delays for all stations were estimated. The mean repeatabilities for components of stations’ coordinates for all weeks (the characteristics of the precision of the received daily and weekly solutions) are in the following ranges: for north and east components – from 0.6 mm to 1.6 mm (average values are 1.02 mm and 0.94 mm respectively), for height component – from 2.2 mm to 5.2 mm (average value is 3.36 mm) with the outlier of 5.79 mm for GPS week 943. The coordinates of the permanent GNSS stations for one weekly solution are presented.

The Second Reprocessing Campaign of Historical Observations in the GNSS Data Analysis Centre of MAO NAS of Ukraine

The second reprocessing campaign of historical observations of GNSS satellites at permanent stations located in Ukraine and in the Eastern Europe for GPS weeks 935–1708 (December 7, 1997 – October 6, 2012) was carried out in the GNSS Data Analysis Centre of the Main Astronomical Observatory NAS of Ukraine with using products updated in IGS repro2 and EPN-Repro2 campaigns – precise ephemerides of GPS and GLONASS satellites, coordinates and velocities of reference permanent GNSS stations, etc. The observations was analyzed with the Bernese GNSS Software ver. 5.2 according to the requirements of the EUREF Permanent GNSS Network (EPN), that were valid at that time. In total, observations on 72 GNSS stations, including 48 Ukrainian stations belonging to the following operators of GNSS networks: MAO NAS of Ukraine, Research Institute of Geodesy and Cartography, TNT TPI company (TNT TPI GNSS Network), PJSC System Solutions (System.NET), Lviv Polytechnic National University, UNAVCO, Inc. (USA), were processed. The IGb08 reference frame was realized by applying No-Net-Translation conditions on the coordinates of the IGS Reference Frame stations. As result, the stations’ coordinates in the IGb08 reference frame and the zenith tropospheric delays for all stations were estimated. The mean repeatabilities for components of stations’ coordinates for all weeks (the characteristics of the precision of the received daily and weekly solutions) are in the following ranges: for north and east components – from 0.6 mm to 1.6 mm (average values are 1.02 mm and 0.94 mm respectively), for height component – from 2.2 mm to 5.2 mm (average value is 3.36 mm) with the outlier of 5.79 mm for GPS week 943. The coordinates of the permanent GNSS stations for one weekly solution are presented.

Regional modeling of tropospheric delay considering vertically and horizontally separation of station for regional augmented PPP

Considering the height and horizontal separations between stations, we propose a method to regionally model the tropospheric delay. The zenith tropospheric delay (ZTD) is separated into vertically-correlated and horizontally-correlated components, which are to be regionally modeled respectively. A more accurate ZTD is thereby interpolated at the rover station, and enhanced ZTD constraints can be provided for precise point positioning (PPP). A new constraint of the ZTD interpolation error is proposed and assessed. The results show that the linear correlation between ZTD after separation and the height of the station are further enhanced, with the mean correlation coefficient reaching up to 0.99. The RMS value of the ZTD obtained from undifferenced and uncombined PPP with fixed station coordinates is less than 6.5 mm. The new method is compared with conventional modeling methods and the results show that the new zenith tropospheric delay modeling method is superior to other methods, yielding a RMS value within 1 cm. Simulated kinematic positioning experiments for five different elevation cutoff angles from 10° to 50° are performed to evaluate the performance of the new method. Experimental results show that the new method can effectively improve the convergence and positioning accuracy. The systematic deviations of the vertical component for the higher cutoff angle solution can be significantly improved by the new method.

An advanced residual error model for tropospheric delay estimation

Global navigation satellite systems (GNSS) are widely used for safety-of-life positioning applications. Such applications require high integrity, availability, and continuity of the positioning service. Integrity is assessed by the definition of a protection level, which is an estimation of the maximum positioning error at extremely low probability levels. The emergence of multi-frequency civilian signals and the availability of satellite-based augmentation systems improve the modeling of ionospheric disturbances considerably. As a result, in many applications the tropospheric delay tends to become one of the limiting factors of positioning—especially at low elevation angles. The currently adopted integrity concepts employ a global constant to model the variance of the residual tropospheric delay error. We introduce a new approach to derive residual tropospheric delay error models using the extreme value analysis technique. Seventeen years of global numerical weather model fields are analyzed, and new residual error models are derived for some recently developed tropospheric delay models. Our approach provides models that consider both the geographical location and the seasonal variation of meteorological parameters. Our models are validated with a 17-year-long time series of zenith tropospheric delay estimates as provided by the International GNSS Service. The results show that the developed models are still conservative, while the maximal residual error of the tropospheric delay is still improved by 39–55%. This improvement yields higher service availability and continuity in safety-of-life applications of GNSS.

GNSS Meteorology for Disastrous Rainfalls in 2017–2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients

We studied disastrous heavy rainfall episodes in 2017-2019 summer in SW Japan, especially in the Kyushu region using tropospheric delay data from the dense Global Satellite Navigation System network GEONET (GNSS Earth Observation Network). This region often suffers from extremely heavy rains associated with stationary fronts during summer. In this study, we first analyze behaviors of water vapor on July 6, 2018, using tropospheric parameters obtained from the database at University of Nevada Reno. The data set includes tropospheric delay gradient vectors (G), as well as zenith tropospheric delays, estimated every 5 minutes. At first, we interpolated G to obtain those at grid points and calculated their convergence, similar to the quantity proposed by Shoji (2013) as water vapor concentration index. We obtained zenith wet delay from ZTD by removing zenith hydrostatic delay. The raw ZWD values do not really reflect the wetness of the atmosphere above the GNSS station because they largely depend on the station altitudes. To study the dynamics of water vapor before heavy rains, we estimated ZWD converted to the values at sea-level. In the inversion scheme, we used G at all GEONET stations and ZWD data at low-altitude ( 50 mm/hour) events occurred, i.e., July 5, 2017, July 6, 2018, and August 27, 2019. Next, we performed high time resolution analysis (every 5 minutes) on the days of heavy rain. The results suggest that both WVC and sea-level ZWD go up prior to the onset of the rain, and ZWD decreases rapidly once the heavy rain started.

A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model.

Tropospheric delay is a major error source that affects the initialization and re-initialization speed of the Global Navigation Satellite System’s (GNSS) medium-/long-range baseline in Network Real-Time Kinematic (NRTK) positioning. Fusing the meteorological data from the Numerical Weather Prediction (NWP) model to estimate the zenith tropospheric delay (ZTD) is one of the current research hotspots. However, research has shown that the ZTD derived from NWP models is still not accurate enough for high-precision GNSS positioning applications without the estimation of the residual tropospheric delay. To date, General Regression Neural Network (GRNN) has been applied in many fields. It has a high learning speed and simple structure, and can approximate any function with arbitrary precision. In this study, we developed a regional NWP tropospheric delay inversion method based on a GRNN model to improve the accuracy of the tropospheric delay derived from the NWP model. The accuracy of the tropospheric delays derived from reanalysis data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the US National Centers for Environmental Prediction (NCEP) was assessed through comparisons with the results of the International GPS Service (IGS). The variation characteristics of the residual of the ZTD inverted by NWP data were analyzed considering the factors of temperature, humidity, latitude, and season. To evaluate the performance of this new method, the National Center Atmospheric Research (NCAR) troposphere data of 650 stations in Japan in 2005 were collected as a reference to compare the accuracy of the ZTD before and after using the new method. The experimental results showed that the GRNN model has obvious advantages in fitting the NWP ZTD residual. The mean residual and the root mean square deviation (RMSD) of the ZTD inverted using the method of this study were 9.5 mm and 12.7 mm, respectively, showing reductions of 20.8% and 19.1%, respectively, as compared to the standard NWP model. For long-range baseline (155 km and 207 km), the corrected NWP-constrained RTK showed a reduction of over 43% in the initialization time compared with the standard RTK, and showed a reduction of over 24% in the initialization time compared with the standard NWP-constrained RTK.

Improving the accuracy of real-time precipitable water vapour using country-wide meteorological model with precise point positioning in Thailand

ABSTRACT Atmospheric sensing by Global Navigation Satellite System (GNSS) signals has been exclusively studied for over two decades. This study derives the real-time Precipitable Water Vapour (PWV) by taking 1 month GNSS observation data from 11 stations. A Country-wide Pressure and Temperature (CPT) model is applied to precisely determine PWV from Zenith Tropospheric Delay (ZTD). It shows that the computed ZTD RMSE obtained from most stations is less than 15 mm, meeting a threshold of 15 mm input to its Numerical Weather Prediction (NWP) equation while the average RMSE of PWV is less than 3 mm; suitable for weather nowcasting.

Evaluation of Spatio‐Temporal Characteristics of Different Zenith Tropospheric Delay Models in Antarctica

AbstractZenith tropospheric delay (ZTD) is one of the main error sources in space geodetic techniques, such as Global Positioning System (GPS) and satellite altimetry. To qualitatively and quantitatively determine the most suitable model for Antarctica, we analyze the accuracy and applicability of nine models (UNB3m, EGNOS, GPT2 + Saastamoinen, GPT2w + Saastamoinen, GPT3 + Saastamoinen, GPT2 + Hopfield, GPT2w + Hopfield, GPT3 + Hopfield, and IGGtropSH) in Antarctica using 8 years of GPS‐derived ZTD time series from 65 stations. The results show that the GPT2/2w/3 + SAAS models are better than the other six models, with a bias of 0.2, −0.22, and −0.29 cm and root mean square (RMS) of 2.33, 2.31, and 2.36 cm. Based on the decimeter bias and RMS, the UNB3m model and EGNOS model present the worst performance in Antarctica. There are regional characteristics of bias and RMS in the nine models. The GPT2/2w/3 + SAAS models have the smallest regional deviation, and the bias and RMS between subregions (Antarctic Peninsula, Amundsen Sea Embayment, Ross Ice Shelf, Inland area of West Antarctica, Filchner‐Ronne Ice Shelf, and coastal East Antarctica) are all at the 0.2 and 0.7 cm levels, respectively. The GPT2/2w/3 + HOP models have the largest regional deviation, with regional bias and RMS at the levels of 8 and 6 cm, respectively. Our results suggest that the uncertainty of ice sheet elevation derived from satellite altimetry may be partly caused by the spatial‐related bias and error in the ZTD corrections. The bias and RMS of six GPT combined models and the IGGtropSH model present limited seasonal changes, indicating that these models can simulate the seasonal characteristics of ZTD better. The time series of the bias and RMS values of the EGNOS and UNB3m models show obvious seasonal characteristics, which may contaminate the annual ice sheet elevation by approximately 5 cm if used as ZTD corrections.

PPP models and performances from single- to quad-frequency BDS observations

Nowadays, China BeiDou Navigation Satellite System (BDS) has been developed well and provided global services with highly precise positioning, navigation and timing (PNT) as well as unique short-message communication, particularly global system (BDS-3) with higher precision multi-frequency signals. The precise point positioning (PPP) can provide the precise position, receiver clock, and zenith tropospheric delay (ZTD) with a stand-alone receiver compared to the traditional double differenced relative positioning mode, which has been widely used in PNT, geodesy, meteorology and so on. However, it has a lot of challenges for multi-frequency BDS PPP with different strategies and more unknown parameters. In this paper, the detailed PPP models using the single-, dual-, triple-, and quad-frequency BDS observations are presented and evaluated. Firstly, BDS system and PPP method are introduced. Secondly, the stochastic models of time delay bias in BDS-2/BDS-3 PPP including the neglection, random constant, random walk and white noise are presented. Then, three single-frequency, four dual-frequency, four triple-frequency and four quad-frequency BDS PPP models are provided. Finally, the BDS PPP models progress and performances including theoretical comparison of the models, positioning performances, precise time and frequency transfer, ZTD, inter-frequency bias (IFB) and differential code bias (DCB) are presented and evaluated as well as future challenges. The results show that the multi-frequency BDS observations will greatly improve the PPP performances.

High Quality Zenith Tropospheric Delay Estimation Using a Low-Cost Dual-Frequency Receiver and Relative Antenna Calibration

The recent release of consumer-grade dual-frequency receivers sparked scientific interest into use of these cost-efficient devices for high precision positioning and tropospheric delay estimations. Previous analyses with low-cost single-frequency receivers showed promising results for the estimation of Zenith Tropospheric Delays (ZTDs). However, their application is limited by the need to account for the ionospheric delay. In this paper we investigate the potential of a low-cost dual-frequency receiver (U-blox ZED-F9P) in combination with a range of different quality antennas. We show that the receiver itself is very well capable of achieving high-quality ZTD estimations. The limiting factor is the quality of the receiving antenna. To improve the applicability of mass-market antennas, a relative antenna calibration is performed, and new absolute Antenna Exchange Format (ANTEX) entries are created using a geodetic antenna as base. The performance of ZTD estimation with the tested antennas is evaluated, with and without antenna Phase Center Variation (PCV) corrections, using Precise Point Positioning (PPP). Without applying PCVs for the low-cost antennas, the Root Mean Square Errors (RMSE) of the estimated ZTDs are between 15 mm and 24 mm. Using the newly generated PCVs, the RMSE is reduced significantly to about 4 mm, a level that is excellent for meteorological applications. The standard U-blox ANN-MB-00 patch antenna, with a circular ground plane, after correcting the phase pattern yields comparable results (0.47 mm bias and 4.02 mm RMSE) to those from geodetic quality antennas, providing an all-round low-cost solution. The relative antenna calibration method presented in this paper opens the way for wide-spread application of low-cost receiver and antennas.

Comparison of tropospheric parameters from Meteodrone measurements with GNSS estimates from ground-based stations

This study presents a comparison of tropospheric parameters, namely zenith tropospheric delay and total refractivity calculated using meteorological measurements from a Meteodrone and Global Navigation Satellite Systems (GNSS) stations. A Meteodrone is a unique Unmanned Aerial Vehicle (UAV) originally designed for automatic measurements of meteorological parameters in vertical profiles (up to 3 km above ground) for the assimilation into numerical weather prediction models. One profile measured with both ascending and descending drone takes approximately 20 min, and there are multiple flights possible during one night. The measurements for this campaign have been conducted in March 2019, in Marbach, Switzerland. We have collected pressure, temperature and humidity profiles from nine flights. From the measured meteorological parameters, we have calculated the refractivity and zenith tropospheric delays (ZTDs). The parameters are compared with models calculated from 32 neighboring GNSS stations interpolated using the in-house developed least-squares collocation software COMEDIE (COllocation of MEteorological Data for Interpretation and Estimation of tropospheric path delays). The comparisons between the Meteodrones and the GNSS-derived models show a good agreement with an average absolute bias of 2.4 mm with 1.5 mm standard deviation for ZTD and 2.7 ppm with 1.7 ppm standard deviation for the comparison of the refractivity. The bias is a result of local changes of meteorological conditions captured by the drone, but not by the interpolation model.

Modeling and Forecasting the GPS Zenith Troposphere Delay in West Antarctica Based on Different Blind Source Separation Methods and Deep Learning.

Tropospheric delay is an important error source in global positioning systems (GPS), and the water vapor retrieved from the tropospheric delay is widely used in meteorological research such as climate analysis and weather forecasting. Most zenith tropospheric delay (ZTD) models are presently used as positioning corrections, and few models are used for the estimation of water vapor, especially in Antarctica. Through two blind source separation algorithms (principal component analysis (PCA) and independent component analysis (ICA)), a back-propagation (BP) neural network and a deep learning technique (long short-term memory (LSTM) network), we establish an hourly high-accuracy ZTD model for GPS meteorology using the GPS-ZTD from 52 GPS stations in West Antarctica. Our results show that under the condition in which the principal components (PCs) and independent components (ICs) remain fixed after decomposition, the mean accuracy of the models for West Antarctica using PCA or ICA are better than 10 mm. Compared with the ZTDs from the nonmodeling stations, the mean root mean square (RMS) of the PCA and ICA models are 9.3 and 8.9 mm, respectively, and the correlation coefficients between the GPS-ZTD and model-ZTDs all exceed 90%. The accuracy of the ICA model is slightly higher than that of the PCA model, and the ICs of the ICA model show more consistent spatial responses. The six-hour forecast is the best among the forecast results, with a mean correlation coefficient of 90.6% and a mean RMS of 7.2 mm using GPS-ZTD. The long-term forecast result is significantly inaccurate, as the correlation coefficient between the 24-h forecast and GPS-ZTD is only 63.2%. Generally modest results have been achieved (HSS ≤ 0.38). Furthermore, the forecast accuracy in coastal areas is lower than that in inland areas. Our study confirms that the combined use of ICA and deep learning in ZTD modeling can effectively restore the original signals, and short-term forecasting can be effectively used in GPS meteorology. However, further development of the technology is necessary.

MG-APP: an open-source software for multi-GNSS precise point positioning and application analysis

To meet the demands of research and precise point positioning (PPP) in a multi-GNSS environment, we developed a GNSS data processing software named multi-GNSS automatic precise positioning software (MG-APP). MG-APP is an open-source software that can be run on Windows/Linux/UNIX and other operating systems. It can simultaneously process GPS/GLONASS/BDS/Galileo observations using a Kalman filter or a square root information filter (SRIF). Compared to the Kalman filter, the SRIF has better numerical stability and maintains stable convergence even with a significant round-off error. MG-APP has a comprehensive and friendly graphical user interface that conveniently allows the user to select models and set parameters. It also contains several types of tropospheric and estimation models that make it easy to analyze the impact of different models and parameters on PPP data processing. After the data processing finishes, zenith tropospheric delays, receiver clock offsets, satellite ambiguity parameters, observation residuals, and other results will be saved into files. Users can further analyze the solution results and construct graphs easily.

Assessment of multi-frequency global navigation satellite system precise point positioning models using GPS, BeiDou, GLONASS, Galileo and QZSS

The development of a global navigation satellite system (GNSS) brings the benefit of positioning, navigation and timing (PNT) services with three or even more available frequency signals. This paper developed five-system multi-frequency precise point positioning (PPP) models based on mathematical and stochastic models. Static positioning performances were evaluated and analyzed with multi-GNSS experiment (MGEX) network datasets and a vehicle-borne kinematic experiment was conducted to verify the kinematic PPP performances. In addition, the receiver clock, zenith tropospheric delay (ZTD), inter-frequency bias (IFB) and differential code bias (DCB) estimates were discussed. Results show that the triple-frequency PPP performances perform slightly better than the dual-frequency solutions, apart from the GPS-related PPP models based on a single ionosphere-free (IF) combined measurement. By introducing the external ionospheric products, the mean convergence time is reduced. For instance, the mean convergence time of ionosphere-constrained (IC) multi-frequency PPP is reduced by 7.4% from 35.7 to 33.1 min and by 19.0% from 7.8 to 6.3 min, for Galileo-only and five-constellation solutions, respectively, compared with dual-frequency IF PPP models. Similarly, the kinematic PPP can also achieve improved performances with more frequency signals and multi-GNSS observations.

A New Global Tropospheric Delay Model Considering the Spatiotemporal Variation Characteristics of ZTD With Altitude Coefficient

AbstractTropospheric delay error is independent of the signal's frequency and has strong spatiotemporal variation. It is one of the most severe error sources of satellite navigation and spatial measurement. In view of the limitation of global zenith tropospheric delay (GZTD) model considering the altitude coefficient β as a constant and ignoring its spatiotemporal variation, this paper analyzes the spatiotemporal distribution of zenith tropospheric delay (ZTD) with altitude variation coefficient β based on the meteorological reanalysis data provided by the European Centre for Medium‐Range Weather Forecasting from 2011 to 2015. The global altitude coefficient β model is established by using the trigonometric function and seventh‐order spherical harmonics function model, and then the global tropospheric delay model R_GZTD (reconstruction GZTD) is reconstructed by using Gbeta (Global beta β) model. The R_GZTD model gives high‐accuracy global distribution of tropospheric delays without meteorological parameters. The results show that the internal accuracy of the R_GZTD model is 3.22 cm, which has a good fitting effect. This paper uses the tropospheric delay products in 2016–2017 provided by the International GNSS Service, the tropospheric delay calculated by the European Centre for Medium‐Range Weather Forecasting reanalysis data, and the radiosonde ZTD data in 2016–2017 as external compliance check data. The results show that the accuracy of the R_GZTD model is better than that of the GZTD model, UNB3m model, and the global pressure and temperature 2 wet model in the global and regional scope. Especially in areas with a higher altitude, the correction effect of the R_GZTD model is more significant. The root‐mean‐square error is 8.5% smaller than that of the GZTD model in the range of 0–500 m, 14.6% smaller in the range of 500–1,000 m, 12.5% smaller in the range of 1,000–2,000 m, and 16.8% smaller in the range above 2,000 m. The accuracy with the increasement of height is due to the fact that the R_GZTD model takes account of the spatiotemporal variation of β.

Assessment of the Algorithm for Single Frequency Precise Point Positioning at Different Latitudes and with Distinct Magnetic Storm Conditions

This paper analyzes the convergence time and the root mean square (RMS) error of single frequency (SF) precise point positioning (PPP) in the ionospheric-constrained (TIC1) and troposphere- and ionospheric-constrained (TIC2) conditions, when the stations are at a low latitude, mid-latitude, and high latitude area during the period of a magnetic storm (MS) and a non-magnetic storm (NMS). In this paper, 375 IGS (international global navigation satellite system (GNSS) service) stations were selected from all over the world for 30 days in September 2017. The 24 hour daily observations were split for each station into 8 data sets of 3 hours each, so that a total of 90,000 tests were carried out. After statistical analysis, it was concluded that: during the MS period, the percentage of TIC2 shortened compared to the TIC1 convergence time, and it was by at least 3.9%, 3.0%, and 9.3% when the station was at global, low latitude, and high latitude areas, respectively. According to the statistical analysis, during the NMS period the convergence time was shortened about at least 12.8%, 11.0%, and 30.0% with respect to the stations in the MS period at global, low, and high latitude areas, respectively. If the station was located in the mid-latitude region, the convergence time was not shortened in some modes. The ionospheric activity in the mid-latitude region was less than that in the low-latitude region, while there were more stations in the mid-latitude region, and the precision of the global ionospheric maps (GIMs) and zenith tropospheric delay (ZTD) products were also slightly higher. Overall, TIC1 and TIC2 have a greater impact on convergence time, but have less impact on positioning accuracy, and only have a greater impact in different environments during the MS and NMS periods.

Performance of real-time undifferenced precise positioning assisted by remote IGS multi-GNSS stations

The heavy reliance of real-time precise point positioning (RTPPP) on external satellite clock products may lead to discontinuity or even failure in time-critical applications. We present an alternative approach of real-time undifferenced precise positioning (RUP) that, by combining satellite clock estimation and precise point positioning based on the extended Kalman filter, is independent of external satellite clock corrections. The approach is evaluated in simulated real time with the assistance of a variable number of IGS multi-GNSS stations located between 1359.7 and 4852.5 km from the users. The results show that even with a single auxiliary IGS station, RUP is still feasible and able to retain centimeter-level positioning accuracy. Typically, with three auxiliary IGS stations about 2000–3000 km away, an accuracy of about 2 cm in the horizontal and 5 cm in the vertical can be achieved. The performance of RUP is comparable to that of PPP using 5-s satellite clock products and notably exhibits superior short-term precision in dealing with high-rate (1 Hz) GPS/GLONASS observations. The addition of GLONASS observations reduces the convergence time by 56.9% and improves the 3-D position accuracy by 31.8% while increasing the processing latency by a factor of about 1.6. Employing three IGS stations over 2400 km away from the epicenter, RUP is applied for the rapid determination of coseismic displacements and waveforms for the 2016 Kaikoura earthquake, yielding highly consistent results compared to those obtained from post-processed PPP in the global reference frame. We also explore its potential in facilitating real-time online services in terms of real-time precise positioning, zenith tropospheric delay retrieving, and satellite clock estimation.

The retrieval of atmosphereric water vapor based on Beidou Ultra-rapid precise ephemeris using different mapping functions

The atmospheric mapping function is an important model to convert the total tropospheric delay on the slant path of satellite signal into the zenith tropospheric delay. It is important to choose the appropriate mapping function to improve the accuracy of water vapor estimation. Beidou observation data in different seasons from 6 MGEX stations were processed using GAMIT 10.7 software. Three mapping functions (NMF/GMF/VMF1) were used in the estimating the atmospheric precipitable water vapor (PWV) based on Beidou Ultra-rapid precise ephemeris. The results of PWV/BDS were compared with the PWV/GPS. The experiments showed that the average bias between PWV/BDS and PWV/GPS is about 1-5mm. The GMF mapping function is slightly better than NMF and VMF1 in the retrieval of atmosphereric water vapor.

Development of Global Tropospheric Empirical Correction Model with High Temporal Resolution

The accuracy of global tropospheric empirical models depends on the model expression and the modeling data sources. Although the current temporal resolution of available models is usually one day, it is anticipated that this will be improved in the future. To achieve compatibility with future high temporal-resolution data sources, this study develops a new global tropospheric correction model, the Wuhan-University Global Tropospheric Empirical Model (WGTEM). Evaluation of WGTEM model expression determines that it has better precision than other models, and this is attributed to its ability to consider diurnal variations in meteorological parameters and the double-peak daily variation in air pressure, which are not concerned in other models. The external accuracy of the WGTEM was evaluated after modeling with the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-Interim products, and results show its accuracy exceeds that of the current ITG model and its Zenith Tropospheric Delay (ZTD) performance is also superior.

Evaluation of Zenith Tropospheric Delay Derived from ERA5 Data over China Using GNSS Observations

The latest reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF), ERA5, can provide atmospheric data for calculating Zenith Tropospheric Delay (ZTD) with hourly temporal resolution, which is a key factor in Global Navigation Satellite System (GNSS) high-precision application. This paper is aimed at evaluating the performance of ZTD derived from ERA5 reanalysis data over China using 219 GNSS stations of the Crustal Movement Observation Network of China (CMONOC) covering the period from 2015 to 2016. The site-specific hourly ZTD at these stations is obtained by integration method and Saastamoinen model method on ERA5 pressure-level and surface-level reanalysis data with the temporal resolution of 1 h and the spatial resolution of 0.25° × 0.25°. Firstly, the atmospheric temperature and pressure that derived from ERA5 are compared with temperature and pressure obtained from meteorological sensors available at 193 GNSS stations. The biases are 2.31 °C and 1.26 mbar implying the accuracy and feasibility of ERA5 pressure and temperature for calculating ZTD over China. Secondly, the performance of ERA5 ZTD is systematically evaluated using ZTD from 219 GNSS sites. The average bias and Root Mean Square (RMS) of ERA5 pressure-level ZTD at all test stations in integration method are approximately 2.97 mm and 11.49 mm respectively, and those of ERA5 surface-level ZTD in model method are 7.97 mm, 39.25 mm, which indicates that ERA5 pressure-level ZTD has a higher accuracy over China. Further analysis indicates that the accuracies of ZTD derived from ERA5 pressure-level and surface-level data are approximately 13.8% and 10.9% higher than those from of ERA-Interim pressure-level and surface-level data. Moreover, ERA5 is able to accurately capture the short-term (hourly) variation of ZTD, which further indicates the better performance of ERA5. Thirdly, the temporal and spatial variation characteristics of ERA5 ZTD accuracy are further analyzed over China. The results show that the ZTD in the southern region has the lower accuracy compared with that in the northern region over China due to the influence of latitude and altitude. Furthermore, it is found that the ERA5 ZTD over China has obvious seasonality, with higher accuracy in winter and lower accuracy in summer.