Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications

Precipitable water vapor (PWV) can be assimilated into a numerical weather model (NWM) to improve the prediction accuracy of numerical weather prediction. In this study, taking GNSS data for the Beijing Fangshan station (BJFS) as an example, based on the method of Pearson correlation coefficient combined with quantitative analysis, GNSS datasets are used to study the relationships between GNSS-derived PWV (GNSS PWV__Met) and its influencing factors, including the internal influencing factors zenith troposphere delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and surface temperature (T_s), and the external influencing factor haze (mainly PM_2.5). Firstly, based on the strong correlation between PWV__Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182–212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. Secondly, based on hourly data of BJFS in 2016, the correlation between PWV__Met and ZTD, ZWD, ZHD, pressure (P) and T_s is analyzed, and then, with the rate of ZTD variation as the main factor, ZTD variation as auxiliary factor, the prediction success rate is 88.24% from hourly data of precipitation event for DOYs 183–213 in Beijing. The experiment indicates that ZTD can help forecast short-term precipitation. Thirdly, based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall, and relatively continuous data in the past three years, the correlation between GNSS PWV_Met/ZTD and PM_2.5 hourly series is analyzed. The results of the experiments suggests that GNSS ZTD should be considered to assist in haze monitoring. So in the absence of radiosonde stations and meteorological elements, ZTDs on retrieval of GNSS stations have more application value in short-term forecast.     

Real-time zenith tropospheric delays in support of numerical weather prediction applications

The Geodetic Observatory Pecný (GOP) routinely estimates near real-time zenith total delays (ZTD) from GPS permanent stations for assimilation in numerical weather prediction (NWP) models more than 12 years. Besides European regional, global and GPS and GLONASS solutions, we have recently developed real-time estimates aimed at supporting NWP nowcasting or severe weather event monitoring. While all previous solutions are based on data batch processing in a network mode, the real-time solution exploits real-time global orbits and clocks from the International GNSS Service (IGS) and Precise Point Positioning (PPP) processing strategy. New application G-Nut/Tefnut has been developed and real-time ZTDs have been continuously processed in the nine-month demonstration campaign (February–October, 2013) for selected 36 European and global stations. Resulting ZTDs can be characterized by mean standard deviations of 6–10 mm, but still remaining large biases up to 20 mm due to missing precise models in the software. These results fulfilled threshold requirements for the operational NWP nowcasting (i.e. 30 mm in ZTD). Since remaining ZTD biases can be effectively eliminated using the bias-reduction procedure prior to the assimilation, results are approaching the target requirements in terms of relative accuracy (i.e. 6 mm in ZTD). Real-time strategy and software are under the development and we foresee further improvements in reducing biases and in optimizing the accuracy within required timeliness. The real-time products from the International GNSS Service were found accurate and stable for supporting PPP-based tropospheric estimates for the NWP nowcasting.     

Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data

Global Navigation Satellite Systems (GNSS) are emerging as possible tools for remote sensing high-resolution atmospheric water vapour that improves weather forecasting through numerical weather prediction models. Nowadays, the GNSS-derived tropospheric zenith total delay (ZTD), comprising zenith dry delay (ZDD) and zenith wet delay (ZWD), is achievable with sub-centimetre accuracy. However, if no representative near-site meteorological information is available, the quality of the ZDD derived from tropospheric models is degraded, leading to inaccurate estimation of the water vapour component ZWD as difference between ZTD and ZDD. On the basis of freely accessible regional surface meteorological data, this paper proposes a height-dependent linear correction model for a priori ZDD. By applying the ordinary least-squares estimation (OLSE), bootstrapping (BOOT), and leave-one-out cross-validation (CROS) methods, the model parameters are estimated and analysed with respect to outlier detection. The model validation is carried out using GNSS stations with near-site meteorological measurements. The results verify the efficiency of the proposed ZDD correction model, showing a significant reduction in the mean bias from several centimetres to about 5 mm. The OLSE method enables a fast computation, while the CROS procedure allows for outlier detection. All the three methods produce consistent results after outlier elimination, which improves the regression quality by about 20% and the model accuracy by up to 30%.     

High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere

Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/?0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Niño event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.     

GGTm-Ts: A global grid model of weighted mean temperature (Tm) based on surface temperature (Ts) with two modes

With the development of Global Navigation Satellite System (GNSS), the detection of precipitable water vapor (PWV) using the GNSS atmospheric sounding technique becomes a research interest in GNSS meteorology. In the conversion of zenith tropospheric delay (ZTD) to PWV, the weighted mean temperature (Tm) plays a crucial role. Generally, the Tm estimated by the linear regression models based on surface temperature (Ts) cannot meet the requirement for global use, and the accuracy of Tm derived from the empirical models is limited. In this study, a new Tm model, named GGTm-Ts model, was developed using the global geodetic observing system (GGOS) atmosphere Tm data and European Centre for Medium-Range Weather Forecasts (ECMWF) data from 2011 to 2015. Resting upon a global 2.5°*2° grid of coefficients of Tm-Ts linear function, the new model can provide Tm at any site in two modes, one for the case with measured Ts provided, i.e., the accurate mode, the other for the case that Ts provided by a subroutine, i.e., the normal mode. The performance of GGTm-Ts model was assessed against the Bevis formula, GPT2w and GPT2wh model using different data sources in 2016-the GGOS atmosphere and radiosonde data. The results show that the GGTm-Ts model in accurate mode achieves best performance with an improvement of 46.9 %/15.3 %, 37.8 %/19.5 % and 34.4 %/14.2 % over other three models in the GGOS atmosphere/radiosonde comparison. For the normal mode, the GGTm-Ts model outperforms the GPT2w model and achieves equivalence results with the GPT2wh model. Moreover, the impact of Tm on GNSS-PWV was analyzed to validate the performance of the GGTm-Ts model.     

An inter-comparison of zenith tropospheric delays derived from DORIS and GPS data

Doppler Orbitography Radiopositioning Integrated by Satellite (DORIS) and Global Positioning System (GPS) techniques are similarly affected by propagation delays in the neutral atmosphere (troposphere) and hence make use of similar data processing strategies for reducing this effect. We compare Zenith Tropospheric Delays (ZTDs) estimated from 52 DORIS and GPS station pairs co-located at 35 sites over the 2005–2008 period. We find an overall systematic negative mean bias of −4 mm and a median bias of −2 mm, with a large site-to-site scatter and especially stronger biases over South America, potentially linked to remaining problems related to the South Atlantic Anomaly (SAA) in the current DORIS data processing. The standard deviation of ZTD differences is in the range 4–12 mm over the globe (8 mm on average), with larger values located in the southern hemisphere. The spatial variability of differences is consistent with previous work but remains largely unexplained. DORIS is shown to be much less sensitive to instrumental changes than GPS (only the switch from Alcatel to Starec antenna at Toulouse is detected as an offset of −4 mm in the ZTD time series). On the opposite, discontinuities and spurious annual signals are found in the GPS ZTD solutions. A discontinuity of +5 mm is found on 5 November 2006, linked to the switch from relative to absolute GPS antenna models used in the data processing. The use of modified GPS antennas (e.g. at GODE) or improved antenna models is shown to reduce the spurious annual signal (e.g. from 5 mm to 2 mm at METS). Overall, the agreement between both techniques is good, though DORIS shows a significantly larger random scatter. The high stability and good spatial and temporal coverage make DORIS a potential candidate technique for meteorology and climate studies as long as reasonable time averaging can be applied (e.g. differences are reduced from 8.6 to 2.4 mm with 5-day averages) and no real-time application is considered. This technique could be considered as a potential contributor to Global Geodetic Observing System (GGOS) for climatology.     

Effects of physical correlations on long-distance GPS positioning and zenith tropospheric delay estimates

The global positioning system (GPS) has become an essential tool for the high precision navigation and positioning. The quality of GPS positioning results mainly depends on the model’s formulations regarding GPS observations, including both a functional model, which describes the mathematical relationships between the GPS measurements and unknown parameters, and a stochastic model, which reflects the physical properties of the measurements. Over the past two decades, the functional models for GPS measurements have been investigated in considerable detail. However, the stochastic models of GPS observation data are simplified, assuming that all the GPS measurements have the same variance and are statistically independent. Such assumptions are unrealistic. Although a few studies of GPS stochastic models were performed, they are restricted to short baselines and short time session lengths. In this paper, the stochastic modeling for GPS long-baseline and zenith tropospheric delay (ZTD) estimates with a 24-h session is investigated using the residual-based and standard stochastic models. Results show that using the different stochastic modelling methods, the total differences can reach as much as 3–6 mm in the baseline component, especially in the height component, and 10 mm in the ZTD estimation. Any misspecification in the stochastic models will result in unreliable GPS baseline and ZTD estimations. Using the residual-based stochastic model, not only the precision of GPS baseline and ZTD estimation is obviously improved, but also the baseline and ZTD estimations are closer to the reference value.     

A priori zenith wet delays in the analysis of VLBI CONT sessions

Tropospheric delay is one of the major sources of error in VLBI (Very Long Baseline Interferometry) analysis. The principal component of this error can be accurately computed through reliable surface pressure data —hydrostatic delay— yet there is also a small but volatile component —wet delay— which is difficult to be modelled a priori. In VLBI analysis, troposphere delay is typically modelled in the theoretical delays using Zenith Hydrostatic Delays (ZHD) and a dry mapping function. Zenith Wet Delay (ZWD) is not modelled but estimated in the analysis process. This work studies inter alia the impact of including external GNSS estimates to model a priori ZWD in VLBI analysis, as well as other models of a priori ZWD.In a first stage, two different sources of GNSS troposphere products are compared to VLBI troposphere estimates in a period of 5 years. The solution with the best agreement to VLBI results is injected in the VLBI analysis as a priori ZWD value and is compared to other options to model a priori ZWD. The dataset used for this empirical analysis consists of the six CONT campaigns.It has been found that modelling a priori ZWD has no significant impact either on baseline length and coordinates repeatabilities. Nevertheless, modelling a priori ZWD can change the magnitude of the estimated coordinates a few millimeters in the up component with respect to the non-modelling approach. In addition, the influence of a priori ZWD on Earth Orientation Parameters (EOP) and troposphere estimates —Zenith Total Delays (ZTD) and gradients—has also been analysed, resulting in a small but significant impact on both geodetic products.     

星载GNSS确定GEO卫星轨道的积分滤波方法

采用星载全球导航卫星系统（GNSS）确定地球静止轨道（GEO）,以解决目前应用星载全球定位系统(GPS)时导航卫星可见性差的问题。以风云卫星为例,分析了未来的GNSS相对于GEO卫星的可见性,针对GEO轨道上导航接收机采样间隔较长的问题,综合轨道积分和卡尔曼滤波方法的优点,提出了确定GEO卫星轨道的积分滤波方法。并利用STK软件仿真产生所需数据,用MATLAB对提出的算法编程并进行仿真验证,结果表明,提出的方法性能优越,定轨精度较高。     

GNSS water vapour tomography – Expected improvements by combining GPS,GLONASS and Galileo observations

The German Research Centre for Geosciences (GFZ) operates a GNSS water vapour tomography system using about 350 German GNSS stations. The GNSS data processing at the GFZ works in near real-time and provides zenith total delays, integrated water vapour and slant delay data operationally. This large data set of more than 50,000 slant delays per hour is used to reconstruct spatially resolved humidity fields by means of tomographic techniques. It can be expected that additional observations from the future Galileo system provide more information with improved quality. A simulation study covering 12 h at 14 July 2009 was therefore started to estimate the impact of GPS, Galileo and GLONASS data on the GNSS tomography. It is shown that the spatial coverage of the atmosphere with slant paths is highly improved by combining observations from two or three satellite systems. Equally important for a reliable tomographic reconstruction is the distribution of slant path intersections as they are required to locate the integrated delay information. The number of intersection points can be increased by a factor of 4 or 8 if two or three systems are combined and their distribution will cover larger regions of the atmosphere. The combined data sets can be used to increase the spatiotemporal resolution of the reconstructed humidity fields up to 30 km horizontally, 300 m vertically and 15 min. The reconstruction quality could not be improved considerably using the currently available techniques.     

DORIS and GPS monitoring of the Gavdos calibration site in Crete

Due to its specific geographical location as well as its geodetic equipment (DORIS, GNSS, microwave transponder and tide gauges), the Gavdos station in Crete, Greece is one of the very few sites around the world used for satellite altimetry calibration. To investigate the quality of the Gavdos geodetic coordinates and velocities, we analyzed and compared here DORIS and GPS-derived results obtained during several years of observations. The DORIS solution is the latest ignwd11 solution at IGN, expressed in ITRF2008, while the GPS solution was obtained using the GAMIT software package. Current results show that 1–2 mm/yr agreement can be obtained for 3-D velocity, showing a good agreement with current geophysical models. In particular, the agreement obtained for the vertical velocity is around 0.3–0.4 mm/yr, depending on the terrestrial reference frame. As a by-product of these geodetic GPS and DORIS results, Zenith Tropospheric Delays (ZTDs) estimations were also compared in 2010 between these two techniques, and compared to ECMWF values, showing a 6.6 mm agreement in dispersion without any significant difference between GPS and DORIS (with a 97.6% correlation), but with a 13–14 mm agreement in dispersion when comparing to ECMWF model (with only about 90% correlation for both techniques). These tropospheric delay estimations could also provide an external calibration of the tropospheric correction used for the geophysical data of satellite altimetry missions.     

Retrieval of the change of precipitable water vapor with zenith tropospheric delay in the Chinese mainland

As a preliminary step for assessing the impact of global positioning system (GPS) refractive delay data in numerical weather prediction (NWP) models, the GPS zenith tropospheric delays (ZTD) are analyzed from 28 permanent GPS sites in the Chinese mainland. The objectives are to estimate the GPS ZTD and their variability in this area. The differences between radiosonde precipitable water vapor (PWV) and GPS PWV have a standard deviation of 4 mm in delay, a bias of 0.24 mm in delay, and a correlation coefficient of 0.94. The correlation between GPS ZTD and radiosonde PWV amounts to 0.89, indicating that the variety of tropospheric zenith delay can reflect the change of precipitable water vapor. The good agreement also guarantees that the information provided by GPS will benefit the NWP models. The time series of GPS ZTD, which were derived continuously from 2002 to 2004, are used to analyze the change of precipitable water vapor in Chinese mainland. It shows that the general trend of GPS ZTD is diminishing from the south-east coastland to the north-west inland, which is in accordance with the distribution of Chinese annual amount of rainfall. The temporal distribution of GPS ZTD in the Chinese mainland is that the GPS ZTD reaches maximum in summer, and it reaches minimum in winter. The long term differences between the observational data sources require further study before GPS derived data become useful for climate studies.     

Orbit determination performances using single- and double-differenced methods: SAC-C and KOMPSAT-2

In this paper, Global Positioning System-based (GPS) Orbit Determination (OD) for the KOrea-Multi-Purpose-SATellite (KOMPSAT)-2 using single- and double-differenced methods is studied. The requirement of KOMPSAT-2 orbit accuracy is to allow 1 m positioning error to generate 1-m panchromatic images. KOMPSAT-2 OD is computed using real on-board GPS data. However, the local time of the KOMPSAT-2 GPS receiver is not synchronized with the zero fractional seconds of the GPS time internally, and it continuously drifts according to the pseudorange epochs. In order to resolve this problem, an OD based on single-differenced GPS data from the KOMPSAT-2 uses the tagged time of the GPS receiver, and the accuracy of the OD result is assessed using the overlapping orbit solution between two adjacent days. The clock error of the GPS satellites in the KOMPSAT-2 single-differenced method is corrected using International GNSS Service (IGS) clock information at 5-min intervals. KOMPSAT-2 OD using both double- and single-differenced methods satisfies the requirement of 1-m accuracy in overlapping three dimensional orbit solutions. The results of the SAC-C OD compared with JPL’s POE (Precise Orbit Ephemeris) are also illustrated to demonstrate the implementation of the single- and double-differenced methods using a satellite that has independent orbit information available for validation.     

The impact on tsunami detection from space using GNSS-reflectometry when combining GPS with GLONASS and Galileo

The devastating Sumatra tsunami in 2004 demonstrated the need for a tsunami early warning system in the Indian Ocean. Such a system has been installed within the German-Indonesian Tsunami Early Warning System (GITEWS) project. Tsunamis are a global phenomenon and for global observations satellites are predestined. Within the GITEWS project a feasibility study on a future tsunami detection system from space has therefore been carried out. The Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative way of using GNSS signals for remote sensing. It uses ocean reflected GNSS signals for sea surface altimetry. With a dedicated Low Earth Orbit (LEO) constellation of satellites equipped with GNSS-R receivers, densely spaced sea surface height measurements could be established to detect tsunamis. Some general considerations on the geometry between LEO and GNSS are made in this simulation study. It exemplary analyzes the detection performance of a GNSS-R constellation at 900 km altitude and 60° inclination angle when applied to the Sumatra tsunami as it occurred in 2004. GPS is assumed as signal source and the combination with GLONASS and Galileo signals is investigated. It can be demonstrated, that the combination of GPS and Galileo is advantageous for constellations with few satellites while the combination with GLONASS is preferable for constellations with many satellites. If all three GNSS are combined, the best detection performance can be expected for all scenarios considered. In this case an 18 satellite constellation will detect the Sumatra tsunami within 17 min with certainty, while it takes 53 min if only GPS is considered.     

一种地基GNSS接收机差分码偏差估算方法

GNSS不同频点间的码伪距作差会引入信号的差分码偏差(DCB),包括GNSS卫星及地面接收机的DCB.本文提出一种地基GNSS接收机差分码偏差参数估算方法,首先由电离层文件参数作线性插值,计算出电离层延迟误差.之后对IGS站观测文件进行加权最小二乘法估计,得到GPS卫星和地面GNSS接收机的L1C频点和L2P频点间码偏...     

GNSS-R open-loop difference phase altimetry: Results from a bridge experiment

The paper explores a method to obtain accurate lake surface heights using measurements of the Global Navigation Satellite System (GNSS) carrier phase reflected from the lake surface. The method is referred to as Global Navigation Satellite System-Reflection (GNSS-R) open-loop difference phase altimetry method. It consists of two key technologies: one is the open-loop tracking method to track the GNSS-R signals, where the direct GNSS signal’s frequency is used as a reference frequency to obtain the carrier phases of the GNSS-R signals; the other key technology is time difference phase altimetry method to invert the lake surface heights using two or more carrier phases of GNSS-R signals received simultaneously. A validation experiment is carried out on the SANYING bridge over GUANTING lake using a GNSS-R receiver developed by the Center for Space Science and Applied Research (CSSAR), processing the data with GNSS-R open-loop difference phase altimetry method. The lake surface height results are consistent with the height results of GPS dual-frequency differential positioning altimetry. The results show that we can achieve centimeter level height in one minute average, by using 11 minutes carrier phase data of three GNSS-R signals received simultaneously.     

一种自适应GNSS弱信号载噪比估计方法

全球导航卫星系统（GNSS）信号的载噪比（CNR）是衡量接收机工作性能的一个重要参数。为了准确得到载噪比估计值,推导并分析了2种常用的GNSS信号载噪比估计方法（方差求和法（VSM）、窄带宽带功率比值法（PRM））,并同时提出一种基于渐消因子容积卡尔曼滤波的自适应载噪比估计方法,比较了3种方法在通常的信号环境下和弱信号环境下的载噪比估计能力。结果显示:在信号较弱环境或信号受到遮挡产生突变等情况时,VSM方法与PRM方法均会产生较大的误差,而自适应载噪比估计方法能准确估计出信号的载噪比。      

高动态GPS/BD2组合导航信号模拟器关键技术研究

高动态组合导航信号模拟器是高动态组合导航接收机设计验证的高效平台。本文给出了高动态GPS/BD2组合导航信号模拟器关键部分架构和实现方案,重点阐述了高动态信号的产生方法、提高模拟器运算效率的三次样条插值算法、反SINC滤波器补偿方法和保证模拟器通道一致性措施。对这一信号模拟器所产生的GNSS信号进行了验证。     

Near real-time PPP-based monitoring of the ionosphere using dual-frequency GPS/BDS/Galileo data

Ionosphere delay is very important to GNSS observations, since it is one of the main error sources which have to be mitigated even eliminated in order to determine reliable and precise positions. The ionosphere is a dispersive medium to radio signal, so the value of the group delay or phase advance of GNSS radio signal depends on the signal frequency. Ground-based GNSS stations have been used for ionosphere monitoring and modeling for a long time. In this paper we will introduce a novel approach suitable for single-receiver operation based on the precise point positioning (PPP) technique. One of the main characteristic is that only carrier-phase observations are used to avoid particular effects of pseudorange observations. The technique consists of introducing ionosphere ambiguity parameters obtained from PPP filter into the geometry-free combination of observations to estimate ionospheric delays. Observational data from stations that are capable of tracking the GPS/BDS/GALILEO from the International GNSS Service (IGS) Multi-GNSS Experiments (MGEX) network are processed. For the purpose of performance validation, ionospheric delays series derived from the novel approach are compared with the global ionospheric map (GIM) from Ionospheric Associate Analysis Centers (IAACs). The results are encouraging and offer potential solutions to the near real-time ionosphere monitoring.     

The verification of GNSS tropospheric tomography model in a mountainous area

The GNSS (Global Navigation Satellite System) has not been developed as a meteorological data source provider, but with a careful and sophisticated processing strategy it might be used as one. The term GNSS tomography refers to the usage of the ray traced GNSS signal as scanning rays in the tomographic model input. The model is divided into a number of voxels. The system is inverted and value of refractivity is obtained. Typically, as in the most of the inverse processing, there is a problem of the undetermined system and as a consequence the cofactor matrix is close to singular. To avoid singularity additional conditions or constrains should be added to the system. Here, additional parameters are derived with the help of the air flow analysis in the Sudety mountains (south-west region of Poland), and special Slant Wet Delay (SWD) trimming procedure. The flow’s synthetic parameters like the Bruint-Väisälä frequency and the Froude number are determined. This way the type of the flow is recognized and the analysis of the impact of orographic barrier has been quantified. The SWDs from the GNSS observations were tested against, SWD from raytracing through the COAMPS model field. The modified GNSS tomography model was tested for the real GNSS observations delivered from the GNSS network Karkonosze located in the Sudety mountains and compared with the COAMPS model. The solution shows a considerable improvement in comparison with plain tomographic model results.