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.     

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%.     

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.     

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.     

Establishment of atmospheric weighted mean temperature model in the polar regions

Due to the special geographical location and extreme climate environment, the polar regions (Antarctic and Arctic) have an important impact on global climate change. Atmospheric weighted mean temperature (Tm) is a crucial parameter in the retrieval of precipitable water vapor (PWV) from the zenith wet delay (ZWD) of ground-based Global Navigation Satellite System (GNSS) signal propagation. In this paper, the correlation between weighted mean temperature and surface temperature (Ts) is studied firstly. It is shown that the correlation coefficients between Tm and Ts are 0.93 in the Antarctic and 0.94 in the Arctic. The linear regression Tm model and quadratic function Tm model of the Antarctic and the Arctic are established respectively using the radiosonde profiles of 12 stations in the Antarctic and 58 stations in the Arctic from 2008 to 2015. The accuracies of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model which is a state-of-the-art global Tm model are verified using the radiosonde profiles from 2016 to 2018 in the Antarctic and Arctic. Root Mean Square (RMS) errors of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model in the Antarctic are 3.07 K, 2.87 K and 4.32 K respectively, and those in the Arctic are 3.53 K, 3.38 K and 4.82 K, which indicates that the quadratic function Tm model has a higher accuracy compared to linear regression Tm model, and the accuracies of the two regional Tm models are better than that of GPT2w Tm model in the polar regions. In order to better evaluate the accuracy of Tm in the PWV retrieval, the PWV values of radiosondes are used for comparisons as the reference value. The RMS errors of PWV derived from the two Tm models are similar for 1.28 mm in the Antarctic and 1 mm in the Arctic respectively. In addition, the spatial and temporal variation characteristics of Tm are analyzed in the polar regions by spectral analysis of Tm data using fast Fourier transform. The results show that the Tm has obvious seasonality and annual periodicity in the polar regions, and the maximum difference between warm season and cold season is about 63 K. After comparing and analyzing the influences of latitude, longitude and elevation on the Tm in the polar regions, it is found that latitude and elevation have a greater influence on the Tm than the longitude. As the latitude and elevation increase, the Tm decreases, and vice versa in the polar regions.     

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.     

Comparison of satellite orbit ephemerides for use in GPS meteorology

This paper discusses GPS (Global Position System) meteorology. The research presented is based on a comparison of values of precipitable water vapour PWV, based on GPS measurements using final and predicted ephemerides of satellite orbits. We analysed recent year’s improvement in predicting ephemerides. We compared the data outputs from a radiosonde using GPS receiver measurements directly from the meteorological station from which the radiosondes were launched. The results indicate a high quality of the predicted ephemerides. This finding makes predicted ephemerides highly usable for near real-time estimations of PWV. To use PWV in meteorological forecast applications, this high speed of PWV values supply is necessary.     

Global monitoring of tropospheric water vapor with GPS radio occultation aboard CHAMP

The global positioning system radio occultation (GPS RO) technique provides a powerful tool for atmospheric sounding which requires no calibration, is not affected by clouds, aerosols or precipitation, and provides an almost uniform global coverage. The paper deals with application of GPS RO measurements from CHAllenging Minisatellite Payload (CHAMP) for the retrieval of tropospheric water vapor profiles. CHAMP RO data are available since 2001 with up to 200 high resolution atmospheric profiles per day. We introduce a new direct method for water vapor retrieval from GPS RO data. Additionally, a 1Dvar algorithm is used for this purpose. The so derived CHAMP water vapor profiles are validated with radiosonde data on a global scale. Here, both methods come to statistically comparable results revealing a negative bias of less than 0.1 g/kg and a standard deviation of less than 1 g/kg specific humidity in the mid troposphere. Potentials of CHAMP RO retrievals for monitoring the mean tropospheric water vapor distribution on a global scale are presented.     

Analysis of precipitable water vapor from GPS measurements in Chengdu region: Distribution and evolution characteristics in autumn

The rainfall process of Chengdu region in autumn has obvious regional features. Especially, the night-time rain rate of this region in this season is very high in China. Studying the spatial distribution and temporal variation of regional atmospheric precipitable water vapor (PWV) is important for our understanding of water vapor related processes, such as rainfall, evaporation, convective activity, among others in this area. Since GPS detection technology has the unique characteristics, such as all-weather, high accuracy, high spatial and temporal resolution as well as low cost, tracking and monitoring techniques on water vapor has achieved rapid developments in recent years. With GPS–PWV data at 30-min interval gathered from six GPS observational stations in Chengdu region in two autumns (September 2007–December 2007 and September 2008–December 2008), it is revealed that negative correlations exist between seasonally averaged value of GPS–PWV as well as its variation amplitude and local terrain altitude. The variation of PWV in the upper atmosphere of this region results from the water vapor variation from surface to 850 hPa. With the help of Fast Fourier Transform (FFT), it is found that the autumn PWV in Chengdu region has a multi-scale feature, which includes a seasonal cycle, 22.5 days period (quasi-tri-weekly oscillation). The variation of the GPS–PWV is related to periodical change in the transmitting of the water vapor caused by zonal and meridional wind strengths’ change and to the East Asian monsoon system. According to seasonal variation characteristics, we concluded that the middle October is the critical turning point in PWV content. On a shorter time scale, the relationship between autumn PWV and ground meteorological elements was obtained using the composite analysis approach.     

Combination methods of tropospheric time series

In this article we present two methods for combination of different Global Navigation Satellite Systems (GNSS) Zenith Total Delay (ZTD) time-series for the same GNSS site, but from different producers or different processing setups. One method has been setup at ASI/CGS, the other at KNMI. Using Near Real-Time (NRT) ZTD data covering 1 year from the E-GVAP project, the performance of the two methods is inter-compared and validation is made against a combined ZTD solution from EUREF, based on post-processed ZTDs. Further, validation of the ASI combined solutions is made against independent ZTDs derived from radiosonde, Numerical Weather Prediction (NWP) model and Very Long Baseline Interferometry (VLBI) ZTD.     

卫星精密测定轨中的对流层延迟估算系统

基于V isual Basic编程,设计了卫星精密测定轨中的实时气象采集与对流层延迟估算系统。利用实时采集ZQZ-A型自动气象站的温度、相对湿度、大气压等气象要素,采用Saastamoinen模型进行估算天顶方向对流层静力延迟和湿延迟,同时采用地基双波段微波辐射计测量大气水汽总量和云液态水总量,进而估算更高精度的湿延迟,在其有效时与模型计算的静力延迟相加,得到天顶方向总延迟;无效时采用模型计算的总延迟。最后将其投影到卫星方向得到总延迟。实际应用表明,设计的对流层延迟估算系统人机界面友好,具有易用性,能实时、稳定地为卫星精密测定轨提供服务。     

Near real-time estimation of tropospheric water vapour content from ground based GNSS data and its potential contribution to weather now-casting in Austria

The importance of high resolution meteorological analysis of the atmosphere increased over the past years. A detailed analysis of the humidity field is an important precondition for a better monitoring of local and regional extreme precipitation events and for forecasts with improved spatial resolution. For this reason, the Austrian Meteorological Agency (ZAMG) is operating the spatial and temporal high resolution INCA system (Integrated Now-casting through Comprehensive Analysis) since begin of 2005. Errors in this analysis occur mainly in the areas of rapidly changing and hard to predict weather conditions or rugged topography with extreme differences in height such as the alpine area of Austria. The aim of this work is to provide GNSS based measurements of the tropospheric water vapour content with a temporal resolution of 1 h and a temporal delay of less than 1 h to assimilate these estimates into the INCA system. Additional requirement is an accuracy of better than 1 mm of the precipitable water (PW) estimates.     

GPS sensing of precipitable water vapour during the March 2010 Melbourne storm

The March 2010 Melbourne storm is used as a case study to examine the potential of using Global Positioning System (GPS) observations for studying the precipitable water vapour (PWV) field. The Victorian statewide GPS infrastructure network, i.e. GPSnet, was used in this study. GPSnet is currently the only statewide and densest GPS infrastructure network in Australia, which provides an excellent opportunity to examine the distribution of water vapour as the severe weather system passed over the state. Data from 15 GPSnet stations were processed over a one-week period, i.e. a few days prior to and after the storm passage, during which the course of the storm extended from the west to the southeast corner of the state. In addition, data from two radiosonde sites of the Australian Bureau of Meteorology Upper Air Network were used to compare and validate the GPS derived PWV measurements. The findings demonstrate that there is strong spatial and temporal correlation between variations of the ground-based GPS-PWV estimates and the passage of the storm over the state. This is encouraging as the ground-based GPS water vapour sensing technique can be considered as a supplemental meteorological sensor in studying severe weather events. The advantage of using ground-based GPS-PWV technique is that it is capable of providing continuous observation of the storm passage with high temporal resolution. The spatial resolution of the distribution of water vapour is dependent on the geographical location and density of the GPS stations.     

Variability and climatology of precipitable water vapor from 12-year GPS observations in Taiwan

Because of global warming, global sea levels have risen, the frequency of drought in Taiwan is much more frequent in winter and spring, and rainfall tends to concentrate in summer. The probability of disaster-type weather has also increased significantly. Estimating precipitable water vapor (PWV) through GPS signals, related studies and analyses of weather conditions, and the effective use of meteorological forecasts have been valued by many meteorological research organizations and officials. In this study, PWV data from 2006 to 2017 and rainfall data were used for long-term harmonic analysis. PWV data calculated by ECMWF (ECMWF-PWV) and PWV data calculated by GPS (GPS-PWV) were subjected to regression analysis to verify the reliability of the GPS-PWV data. The research results show that GPS-PWV and ECMWF-PWV have extremely high correlations; however, the climatic characteristics of some regions and the high spatial resolution of GPS-PWV are able to accurately calculate the high topographic relief of small areas. It is judged that the GPS-PWV is more accurate than the ECMWF-PWV. It is worth noting that the PWV trend of the regions during the 6-year-before period has not changed very much, but the rainfall trend has changed obviously. Except for the eastern region, most of the regions show a decreasing trend year by year. More long-term observations are still needed to prove whether this phenomenon relates to global warming. Long-term rainfall analysis showed that the topography blocked water vapor to the western, southern, and mountainous regions, making them distinctly wet or dry. The harmonic curve showed great consistency with the peaks of PWV and rainfall. However, in the northern and eastern parts of the windward side, the time when maximum rainfall occurred each year may be one month later than the time when the maximum PWV value occurred each year. The reason for this difference is likely to be a decrease in the number of autumn typhoons, resulting in a nearly one-month difference in PWV peaks and rainfall peaks. Finally, we analyzed the linear trend of GPS-PWV and temperature for all regions in Taiwan, and found that annual increasing rate of GPS-PWV and temperature of all regions are within 0.4–0.5 mm/year and 0.04–0.11 C°/year, respectively.     

Determining the precipitable water vapor thresholds under different rainfall strengths in Taiwan

Precipitable Water Vapor (PWV) plays an important role for weather forecasting. It is helpful in evaluating the changes of the weather system via observing the distribution of water vapor. The ability of calculating PWV from Global Positioning System (GPS) signals is useful to understand the special weather phenomenon. In this study, 95 ground-based GPS and rainfall stations in Taiwan were utilized from 2006 to 2012 to analyze the relationship between PWV and rainfall. The PWV data were classified into four classes (no, light, moderate and heavy rainfall), and the vertical gradients of the PWV were obtained and the variations of the PWV were analyzed. The results indicated that as the GPS elevation increased every 100?m, the PWV values decreased by 9.5?mm, 11.0?mm, 12.2?mm and 12.3?mm during the no, light, moderate and heavy rainfall conditions, respectively. After applying correction using the vertical gradients mentioned above, the average PWV thresholds were 41.8?mm, 52.9?mm, 62.5?mm and 64.4?mm under the no, light, moderate and heavy rainfall conditions, respectively. This study offers another type of empirical threshold to assist the rainfall prediction and can be used to distinguish the rainfall features between different areas in Taiwan.     

基于N-BEATS的单站对流层天顶总延迟预报

高精度对流层延迟先验值有助于加速精密单点定位的快速收敛。基于高精度高分辨率气象数据库，采用深度学习N BEATS算法，进行了单站对流层天顶总延迟的预报试验。试验选取了9个IGS跟踪站，试验弧段从2002年1月至2019年6月共185a。首先基于N BEATS算法，设计了3种预报策略，然后基于前175a针对不同预报策略进行模型训练，并对最后365d的对流层天顶总延迟进行预报。试验结果表明，以该气象数据库为基准，12h以内预报弧段的预报残差均值量级大多可达亚毫米，2h、4h、6h的预报残差的标准差分别约为5mm、9mm、13mm。     

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.     

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.     

高纬度地区GPT2w模型的适应性分析

GPT2w模型是现有精度最高的天顶对流层模型，但是应用在高纬度地区时存在较大误差.为更好地保障卫星导航定位系统在高纬度地区的高精度应用，评定了GPT2w模型在高纬度地区的精度，获取天顶对流层湿延迟、干延迟和总延迟，探讨了GPT2w模型改正对精密单点定位的影响.试验结果表明: GPT2w模型在高纬度地区的精度为厘米级，优于其在中低纬度地区的精度；南北极地区天顶对流层呈现明显季节变化特征和区域一致性特征，夏季天顶对流层总延迟高于冬季，北极地区天顶对流层湿延迟明显高于南极地区，北极地区天顶对流层随季节的变化幅度大于南极地区.PPP试验结果表明，GPT2w模型能够有效改善定位精度，适应高纬度地区的高精度定位需求.