Zenith Tropospheric Delay Research Articles

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.

RESEARCH ON ZENITH TROPOSPHERIC DELAY MODELING OF REGIONAL CORS NETWORK

Abstract. By calculating the zenith troposphere information from 11 reference stations of the CORS network in Guilin City, Guangxi Province in 2018 by GAMIT10.6 as the reference values, analyzing the relationship between the zenith troposphere and the elevation, as well as the latitude in the Guilin Region, building a model for the zenith tropospheric delay (ZTD) suitable for the Guilin Region, uniformly selecting 8 CORS reference stations in the Guilin Region to build a new model, comparing with the ZTD estimated by GAMIT10.6 and studying the applicability of the new model in the Guilin Region, the results showed that: 1) In the Guilin Region, the ZTD presented linear negative correlation with the station elevation; 2) the ZTD estimated by the new model was well consistent with the reference value, with high precision, and increased with the elevation.

ANALYSIS AND PREDICTION OF PM2.5 CONCENTRATION IN GUILIN CITY

Abstract. Atmospheric particulate matter is one of the most harmful pollutants in the atmospheric environment, and has an important impact on climate change, reduced visibility, environmental hazards (such as acid rain, smoke) and health hazards. PM2.5 is the main factor causing haze weather, reducing visibility and affecting traffic safety. PM2.5 enters the alveoli through the respiratory tract and endangers human health. The basic characteristics of PM2.5 are small size, light weight, long residence time in the atmosphere, and can be transported to a long distance by the atmospheric circulation, causing a wide range of air pollution.In 2018, the proportion of days with excellent and good air quality in Guilin City in January and February was 71.2%, ranking 10th among the 14 cities in the Guangxi Autonomous Region. As a famous tourist city, its air quality should be paid more attention. The main air pollutant in Guilin City is PM2.5, which refers to fine particles with a diameter of 2.5 μm or less in the air, it has great harm to human health and reduces the visibility of the atmosphere. Air quality data, meteorological data, and GPS tropospheric delay data (ZTD: zenith tropospheric delay) were collected to calculate the correlation between PM2.5 concentration and influencing factors by the gray correlation model, and the relationship between the main influencing factors and the variation characteristics of PM2.5 concentration was analyzed. The result shows that PM2.5 has strong correlation with other air pollutants SO2, NO2, wind direction, relative humidity, ZTD, temperature and wind speed; moderately correlated with rainfall and pressure. The degree of gray correlation between the impact factor and PM2.5 varies with the seasons.Based on this research and analysis, air quality pollutants and meteorological factors are used as input factors and the concentration of PM2.5 values as the output factor of the neural network model. The model is trained by training sample data to establish a neural network model to predict the concentration value of PM2.5 in Guilin. The accuracy of the model is verified by the accuracy index. The summer neural network model has the best precision. The MAE is 4.51 μg/m3, the RMSE is 5.73 μg/m3, and the MRE is 15.1%. The correlation coefficient between the predicted value and the measured value reaches 0.908. It shows that the neural network model based on meteorological factors and ZTD has a good predictive effect and has certain guiding significance for the prevention and control of air pollution in Guilin.

Real-Time Retrieval of Precipitable Water Vapor From Galileo Observations by Using the MGEX Network

The rapid development of the European Galileo system brings a great opportunity for the real-time retrieval of atmospheric parameters. In this contribution, Galileo observations are employed to retrieve real-time water vapor based on the precise point positioning (PPP) technique, where the benefit of ambiguity resolution on water vapor sensing is also investigated. The obtained atmospheric parameters, including zenith tropospheric delay (ZTD) and precipitable water vapor (PWV), are validated with respect to the postprocessing Global Positioning System (GPS) ZTD products and the PWV products derived from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that the real-time ZTDs, derived from the Galileo PPP solutions, agree well with the postprocessing GPS ZTDs. An averaged root-mean-square (rms) value of 8.5 mm for the ZTD differences is achieved for the float solution after an averaged initialization process of about 27.4 min. In terms of the fixed solution, the averaged rms value is decreased to 7 mm and the initialization time is shortened to 20.8 min, showing an improvement of 17.6% and 24.1%, respectively, when compared to the float solution. Furthermore, the derived Galileo-PWVs display good agreement with the ECMWF PWVs, with an accuracy of 1.9 and 1.7 mm for the float and the fixed solution, respectively.

Improved GPT2w (IGPT2w) model for site specific zenith tropospheric delay estimation in China

Despite being a systematic error source in global navigation satellite system (GNSS) positioning and navigation, tropospheric delay is a key parameter in GNSS meteorology. Therefore, deriving a zenith tropospheric delay (ZTD) value as accurately as possible from an empirical model without using auxiliary data is a prerequisite for the high-precision application of GNSS positioning and navigation. To reach the goal above, this paper proposes an improved model to estimate the ZTD based on the Global Pressure and Temperature 2 wet (GPT2w) model, which is called the improved GPT2w (IGPT2w) model. The GPT2w-derived ZTD is first calculated as the initial value of the IGPT2w model, and the time series of ZTD residual can thus be obtained between the GNSS- and GPT2w-derived ZTDs over GNSS stations. Analysis of the long time series variation of ZTD residuals using the multichannel singular spectrum analysis method reveals evident periodic signals. The Lomb–Scargle method is then used to determine the specific values of these periodic signals, and different periods are identified at various GNSS stations. Therefore, a ZTD residual model that considers annual, semi-annual, and seasonal periods is established. The IGPT2w model, in which the ZTD value is obtained by combining the estimated ZTD residual and the GPT2w-derived ZTD, can be acquired. A total of 188 GNSS stations in China throughout 2015 to 2017 are selected to validate the IGPT2w model. In the model, the GNSS-derived ZTD is obtained using the GAMIT/GLOBK software, and the accuracy is validated using radiosonde data with root mean square and bias of 1.9 and 0.1 cm, respectively, at 33 collocated stations in China. Statistical results reveal that the accuracy of the IGPT2w-derived ZTD is improved by 13.7% compared with that of the GPT2w-derived ZTD when the GNSS-derived ZTD is regarded as the reference. Such result indicates that the proposed IGPT2w model outperforms the GPT2w model in China.

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

The derivation of global navigation satellite systems (GNSSs) tropospheric products is nowadays a state-of-the-art technique that serves both research and operational needs in a broad range of applications in meteorology. In particular, GNSS zenith tropospheric delay (ZTD) data assimilation is widely applied in Europe to enhance numerical weather predictions (NWPs). The current study presents the first attempt at introducing assimilation of ZTDs, derived from more than 48 stations of the Hellenic GNSS network, into the operational NWP system of the National Observatory of Athens (NOA) in Greece, which is based on the mesoscale Weather Research and Forecasting (WRF) model. WRF was applied during seven high-impact precipitation events covering the dry and wet season of 2018. The simulation employing the ZTD data assimilation reproduces more accurately, compared to the control experiment, the observed heavy rainfall (especially for high precipitation events, exceeding 20 mm in 24h) during both dry and wet periods. Assimilating ZTDs also improves the simulation of intense (>20 mm) convective precipitation during the time window of its occurrence in the dry season, and provides a beneficial influence during synoptic-scale events in the wet period. The above results, which are statistically significant, highlight an important positive impact of ZTD assimilation on the model’s precipitation forecast skill over Greece. Overall, the modelling system’s configuration, including the assimilation of ZTD observations, satisfactorily captures the spatial and temporal distribution of the observed rainfall and can therefore be used as the basis for examining further improvements in the future.

Performance Evaluation of Troposphere Estimated from Galileo-Only Multi-Frequency Observations

The tropospheric delays estimated from the Global Navigation Satellite System (GNSS) have been proven to be an efficient product for monitoring variations of water vapor, which plays an important role in meteorology applications. The operational GNSS water vapor monitoring system is currently based on the Global Positioning System (GPS) and GLObal NAvigation Satellite System(GLONASS) dual-frequency observations. The Galileo satellite navigation system has been evolving continuously, and on 11 February 2019, the constellation reached 22 active satellites, achieving a capability of standalone Precise Point Positioning (PPP) and tropospheric estimation that is global in scope. This enhancement shows a 37% improvement if the precision of the Galileo-only zenith tropospheric delay, while we may anticipate further benefits in terms of tropospheric gradients and slant delays in the future if an optimal multi-constellation and multi-frequency processing strategy is used. First, we analyze the performance of the multi-frequency troposphere estimates based on the PPP raw observation model by comparing it with the standard ionosphere-free model. The performance of the Galileo-only tropospheric solution is then validated with respect to GPS-only solution using 48 globally distributed Multi-GNSS Experiment (MGEX) stations. The averaged bias and standard deviations are −0.3 and 5.8 mm when only using GPS satellites, respectively, and 0.0 and 6.2 mm for Galileo, respectively, when compared to the International GNSS Service (IGS) final Zenith Troposphere Delay(ZTD) products. Using receiver antenna phase center corrections from the corresponding GPS dual-frequency observations does not affect the Galileo PPP ambiguity float troposphere solutions. These results demonstrate a comparable precision achieved for both Galileo-only and GPS-only ZTD solutions, however, horizontal tropospheric gradients, estimated from standalone GPS and Galileo solutions, still show larger discrepancies, mainly due to their being less Galileo satellites than GPS satellites. Including Galileo E1, E5a, E5b, and E5 signals, along with their proper observation weighting, show the benefit of multi-frequency observations, further improving the ZTD precision by 4% when compared to the dual-frequency raw observation model. Overall, the presented results demonstrate good prospects for the application of multi-frequency Galileo observations for the tropospheric parameter estimates.

Reducing ZHD–ZWD mutual absorption errors for blind ZTD model users

In precise point positioning (PPP), the zenith hydrostatic delay (ZHD) is traditionally treated as a priori value from the ZHD model but the zenith wet delay (ZWD) is treated as an unknown parameter to estimated. For some near real-time PPP tasks, priori ZHD from the blind ZHD models are often used because external meteorological measurement or information about atmospheric pressure or ZHD can not be obtained in such conditions. On the other hand, a priori ZHD errors can project into GNSS height estimates errors in the PPP analysis, because unmodeled part of the ZHD is usually absorbed into the estimated ZWD while there is the difference between the hydrostatic mapping function and the wet mapping function. In this study, we found the errors of the ZHD seasonal models are not always less than those of the ZWD seasonal models, which implies that the traditional strategy for treating ZTD (priori ZHD and estimated ZWD) is not always the best choice when using the blind zenith tropospheric delay (ZTD) models. A decision model for the ZTD blind model (DMZBM), which is a new strategy of dealing with ZTD in PPP when using blind ZTD models, was proposed. For most cases, the traditional strategy works while for some exception cases, it is recommended that priori ZWD from the blind ZWD model was set but ZHD is treated as an unknown parameter to estimated. The DMZBM can directly reduce ZHD–ZWD mutual absorption errors which potentially reduce GNSS height estimates errors due to the difference between the hydrostatic mapping function and the wet mapping function. The results show that there are significant reductions of ZHD–ZWD mutual absorption errors in polar regions when using the new ZTD-treating strategy.

SHAtropE—A Regional Gridded ZTD Model for China and the Surrounding Areas

A regional zenith tropospheric delay (ZTD) empirical model, referred to as SHAtropE (SHanghai Astronomical observatory tropospheric delay model—Extended), is developed and provides tropospheric propagation delay corrections for users in China and the surrounding areas with improved accuracy. The SHAtropE model was developed based on the ZTD time series of the continuous GNSS sites from the Crustal Movement Observation Network of China (CMONOC) and GNSS sites of surrounding areas. It combines the exponential and periodical functions and is provided as regional grids with a resolution of 2.5° × 2.0° in longitude and latitude. At each grid point, the exponential function converts the ZTD from the site height to the ellipsoid, and the periodical terms, including both annual and semi-annual periods, describe ZTD’s temporal variation. Moreover, SHAtropE also provides the predicted ZTD uncertainty, which is valuable in Precise Point Positioning (PPP) with ZTD being constrained for faster convergence. The data of 310 GNSS sites over 7 years were used to validate the new model. Results show that the SHAtropE ZTD has an accuracy of 3.5 cm in root mean square (RMS) quantity, which has a mean improvement of 35.2% and 5.4% over the UNB3m (5.4 cm) and GPT3 (3.7 cm) models, respectively. The predicted uncertainty of SHAtropE ZTD shows seasonal variations, where the values are larger in summer than in winter. By applying the SHAtropE model in the static PPP, the convergence time of GPS-only and BDS-only solutions are reduced by 8.1% and 14.5% respectively compared to the UNB3m model, and the reductions are 6.9% and 11.2% respectively for the GPT3 model. As no meteorological data are required for the implementation of the model, the SHAtropE could thus be a refined tropospheric model for GNSS users in mainland China and the surrounding areas. The method of modeling the ZTD uncertainty can also be used in further global tropospheric delay modeling.

A New Four-Layer Inverse Scale Height Grid Model of China for Zenith Tropospheric Delay Correction

To reduce the convergence time in the real-time precise point positioning (PPP), the zenith tropospheric delay (ZTD) needs to be modeled with high accuracy and sent to the users to be introduced as a priori value. Because of the height difference between the model height and user’s location, the ZTD at the model height must be converted to that at the user’s height. Therefore, it is crucial to investigate an optimal method for this height correction of ZTD. It is normally assumed that the ZTD decreases exponentially with the altitude and an empirical decrease factor is adopted for depicting the ZTD variation with altitude. In this study, the performance of the commonly used single-layer model of the decrease factor was investigated. Based on the investigation of the single-layer model, a new four-layer grid model ( $0.25^{\circ }\times 0.25^{\circ }$ ) over China was constructed with 10-year (from 2009 to 2018) ERA5 data, and its performance was validated. The results indicate that the newly constructed four-layer model has a better performance than the single-layer model with the bias and root mean square (RMS) of the determined ZTD reduced by 54.4% and 27.6%, respectively. With the four- and single-layer grid model and GNSS-derived ZTD, two ZTD models i.e. four-layer ZTD and single-layer ZTD were constructed over China. The experiment shows that the convergence time of PPP can be obviously reduced when introducing these two ZTD models and the four-layer ZTD model has a much better performance than the single-layer ZTD, especially at stations with a high altitude.

The Performance of Different Mapping Functions and Gradient Models in the Determination of Slant Tropospheric Delay

Global navigation satellite systems (GNSSs) have become an important tool for remotely sensing water vapor in the atmosphere. In GNSS data processing, mapping functions and gradient models are needed to map the zenith tropospheric delay (ZTD) to the slant total tropospheric delay (STD) along a signal path. Therefore, it is essential to investigate the spatial–temporal performance of various mapping functions and gradient models in the determination of STD. In this study, the STDs at nine elevations were first calculated by applying the ray-tracing method to the atmospheric European Reanalysis-Interim (ERA—Interim) dataset. These STDs were then used as the reference to study the accuracy of the STDs that determined the ZTD together with mapping functions and gradient models. The performance of three mapping functions (i.e., Niell mapping function (NMF), global mapping function (GMF), and Vienna mapping function (VMF1)) and three gradient models (i.e., Chen, MacMillan, and Meindl) in six regions (the temperate zone, Qinghai–Tibet Plateau, Equator, Sahara Desert, Amazon Rainforest, and North Pole) in determining slant tropospheric delay was investigated in this study. The results indicate that the three mapping functions have relatively similar performance above a 15° elevation, but below a 15° elevation, VMF1 clearly performed better than the GMF and NMF. The results also show that, if no gradient model is included, the root-mean-square (RMS) of the STD is smaller than 2 mm above the 30° elevation and smaller than 9 mm above the 15° elevation but shows a significant increase below the 15° elevation. For example, in the temperate zone, the RMS increases from approximately 35 mm at the 10° elevation to approximately 160 mm at the 3° elevation. The inclusion of gradient models can significantly improve the accuracy of STDs by 50%. All three gradient models performed similarly at all elevations and in all regions. The bending effect was also investigated, and the results indicate that the tropospheric delay caused by the bending effect is normally below 13 mm above a 15° elevation, but this delay increases dramatically from approximately 40 mm at a 10° elevation to approximately 200 mm at a 5° elevation, and even reaches 500–700 mm at a 3° elevation in most studied regions.

Estimation accuracy analysis of zenith tropospheric delays obtained by precise point positioning technique

Estimation accuracy analysis of zenith tropospheric delays obtained by precise point positioning technique

Про визначення зенітних тропосферних затримок із GNSS-спостережень РРР-методом

Про визначення зенітних тропосферних затримок із GNSS-спостережень РРР-методом

A method for predicting short‐time changes in fine particulate matter (PM2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

AbstractIn this study, a method of haze prediction has been developed based on zenith tropospheric delay (ZTD). The relationship between ZTD and fine particulate matter (PM2.5, the main component of haze) during hazy periods in Beijing from 2015 to 2018 was analysed. The correlation between ZTD and the PM2.5 series was analysed based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall and relatively continuous data, and the correlation coefficient between ZTD and the PM2.5 series was maintained in the range [0.5207, 0.7883]. The analysis showed a strong correlation between them based on data from three hazy periods with relatively stable weather conditions. To enhance the correlation between ZTD and the PM2.5 sequence, the standard orthogonal wavelet Daubechies (db5) method was used in this research. By removing the influence of the high frequency signal using the method, the ZTD and PM2.5 sequences were reconstructed using the fourth‐layer low frequency coefficients, their correlation coefficient was maintained in the range [0.5540, 0.9067] and the percentage range of the correlation coefficient was increased to [2.46, 16.69]. Finally, the reconstructed PM2.5, ZTD, relative humidity, average wind speed and NO2 series through db5 were used to establish a multiple regression model to predict the change in the PM2.5 mass concentration. The experimental results show that the multivariate regression model after wavelet analysis is superior to the traditional multivariate regression method in predicting the short‐term change of PM2.5 through four statistics values (R2, F, P, S2) of the regression model and that it is effective and feasible for predicting short‐term haze.

Analysis and Comparison of GPS Precipitable Water Estimates between Two Nearby Stations on Tahiti Island.

Since Bevis first proposed Global Positioning System (GPS) meteorology in 1992, the precipitable water (PW) estimates retrieved from Global Navigation Satellite System (GNSS) networks with high accuracy have been widely used in many meteorological applications. The proper estimation of GNSS PW can be affected by the GNSS processing strategy as well as the local geographical properties of GNSS sites. To better understand the impact of these factors, we compare PW estimates from two nearby permanent GPS stations (THTI and FAA1) in the tropical Tahiti Island, a basalt shield volcano located in the South Pacific, with a mean slope of 8% and a diameter of 30 km. The altitude difference between the two stations is 86.14 m, and their horizontal distance difference is 2.56 km. In this paper, Bernese GNSS Software Version 5.2 with precise point positioning (PPP) and Vienna mapping function 1 (VMF1) was applied to estimate the zenith tropospheric delay (ZTD), which was compared with the International GNSS Service (IGS) Final products. The meteorological parameters sourced from the European Center for Medium-Range Weather Forecasts (ECMWF) and the local weighted mean temperature () model were used to estimate the GPS PW for three years (May 2016 to April 2019). The results show that the differences of PW between two nearby GPS stations is nearly a constant with value 1.73 mm. In our case, this difference is mainly driven by insolation differences, the difference in altitude and the wind being only second factors.

Analysis on the Impacts of Slant Tropospheric Delays on Precise Point Positioning

Tropospheric delay is one main factor affecting the accuracy of precise point positioning (PPP) ambiguity-float and fixed solutions. Investigations mainly focused on evaluating the contributions of tropospheric corrections to the accuracy and reliability of PPP solutions. The tropospheric corrections generally contained the zenith tropospheric delay (ZTD) and the horizontal gradients estimated from relative positioning or PPP. However, the estimated tropospheric delays can be partly absorbed by the carrier phase residuals if the stochastic model is not well-defined. Therefore, along with the ZTD and horizontal gradients, the carrier phase residuals from PPP backward filter are considered to reconstruct slant tropospheric delay (STD). Based on the proposed STD model, its marginal effects on GPS PPP were investigated. Results indicated that the consideration of carrier phase residuals for STD modeling can improve the three-dimensional accuracy to 0.5 cm/1 cm/1.2 cm in the South/North/Up (N/E/U) components. Then, the effects of internal and external STD corrections on PPP float and fixed solutions were analyzed. Compared to the ZTD + gradients augmentation, STD corrections from the same station could improve the PPP accuracy by 51%/51%/60%; the large improvements were because the multipath error and observation noise were eliminated. In comparison, the improvement was 14%/28%/31% using external STD corrections, which indicated the effects of unmodeled tropospheric errors in the phase residuals. The ambiguity-fixing results indicated that the fixing rate of PPP ambiguity was increased by 30% with STD augmentation. As the BeiDou System (BDS) suffered longer convergence than that of GPS, the benefits of STD modeling to the BDS observations were also validated. Overall, the results validated the performance of STD-augmented PPP, which demonstrated the potential application of high-accuracy troposphere products.

Assessment of BeiDou’s Tropospheric Model (IGGtrop) for tropospheric delay correction in northern hemisphere

Abstract Satellite navigation systems face different types of errors that affect the positioning accuracy. The tropospheric delay considers one of the major sources of error for the transmitted signal that could face a tropospheric delay of over 2 m at zenith and 20 m at lower satellite elevation angles. A positioning error of 10 m could result from inaccurate mitigation of the tropospheric delay. Two types of models are available; surface meteorological models and global empirical models. Surface meteorological models use surface meteorological data to give accurate mitigation while the global empirical models use global standard atmospheres. Global empirical models are favorable for satellite based augmentation systems such as EGNOS model for the EGNOS augmentation system. Several hybrid neutral atmosphere delay models have been developed (UNB1 through UNB4). The IGGtrop model is initially developed to provide tropospheric delay corrections for the users of Chinese BeiDou Navigation Satellite System (BDS) and its augmentation system. This paper presents an assessment study for the behaviour of IGGtrop model comparing with (EGNOS, UNB3M) models. The behavior of the three models is compared with IGS-tropospheric estimations for two different-latitude IGS stations; (bhr1 and eil1) in the northern hemisphere. This study recommends using IGGtrop model for estimating the zenith tropospheric delay correction for low-latitude regions in northern hemisphere for all seasons with an average zenith tropospheric difference of 0.75 cm. IGGtrop model is also recommended to be used for estimating the zenith tropospheric delay correction for high-latitude geographic regions in northern hemisphere during autumn and spring seasons with an average zenith tropospheric difference of 0.75 cm. While UNB3M model is the best choice for tropospheric delay correction for high-latitude geographic regions in northern hemisphere during winter and summer seasons with an average zenith tropospheric difference of 1.1 cm.

Study of the moisture content of the troposphere over the ULAB (Ulaanbaatar) monitoring station using GPS measurement, radio sounding and surface meteorology data

The water vapor content is calculated in a conditional vertical column to the tropopause level from remote sensing data on the lower atmosphere . The calculations are based on GPS measurements, surface meteorological data and vertical meteorological profiles produced by radiosonde s. A total zenith tropospheric delay (ZTD) is estimated as a difference between an estimated straight line , along which the GPS signal propagates in vacuum , and a length of the path between the signal source and the receiver , which is refracted in the real medium. A direct correlation is noted between changes in the ‘ wet ’ component of the tropospheric delay of the GPS signal and variations in the moisture content of the troposphere. A ‘ wet ’ component value of the tropospheric delay can be converted with a very high confidence into a value of integral water vapor above the observation point. In order to determine the integral water vapor amount s over the ULAB (Ulaanbaatar) permanent GPS monitoring station, empirical equations were used for the linear regressions of the ratios of surface temperature and mean weighted temperature for the water vapor elasticity using the data of radiosonde s launche d in the city of Ulaanbaatar and Muren settlement. We studied the features of the dynamics of variations in the integral water vapor, surface values of atmospheric pressure, air temperature and precipitation volumes over the ULAB station, using the data of July 2016.

Impact of GNSS Signal Delay Assimilation on Short Range Weather Forecasts Over the Indian Region

AbstractRadio signals transmitted from a satellite constellation and gathered by a ground‐based Global Navigation Satellite System (GNSS) receiver allow computation of zenith tropospheric delay (ZTD), which is related to the atmospheric moisture. This study investigates the impact of assimilating ZTD obtained from a ground‐based GNSS network on a numerical weather prediction model analyses and subsequent forecasts quality. The numerical data assimilation experiments are performed using three‐dimensional variational data assimilation system in the Weather Research and Forecasting model, at 10‐km horizontal grid spacing for the entire month of July 2017. A comparison with European Centre for Medium‐Range Weather Forecast analyses shows a clear positive impact of ZTD assimilation on the lower to middle tropospheric moisture, upper air temperature, and middle and upper tropospheric wind; errors are reduced by as large as 4%, when compared to the model run without ZTD assimilation. The impact on the analyses and forecast quality of surface meteorological variables is mostly neutral with some indication of positive impact on surface pressure. An improvement in the rainfall forecasts is also noticed when model assimilates ZTD observations. In addition, the impact of the formulation of forward model, which calculates model equivalent of the GNSS ZTD, has been assessed on ZTD assimilation. A revised forward model has been implemented within the Weather Research and Forecasting assimilation system. The revised forward model outperforms the original model for ZTD assimilation. Overall result implies that GNSS ZTD data has a good potential for improving the weather prediction and advocates the strengthening of the ground‐based GNSS network over the Indian region, which is currently very sparse.