Global Navigation Satellite System Sites Research Articles

Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches

Global navigation satellite system (GNSS) site coordinate time series provides essential data for geodynamic and geophysical studies, realisation of a regional or global geodetic reference frames, and crustal deformation research. The coordinate time series has been conventionally modelled by least squares (LS) fitting with harmonic functions, alongside many other analysis methods. As a key limitation, the traditional modelling approaches simply use the functions of time variable, despite good knowledge of various underlying physical mechanisms responsible for the site displacements. This paper examines the use of machine learning (ML) models to reflect the effects or residential effects of physical variables related to Sun and the Moon ephemerides, polar motion, temperature, atmospheric pressure, and hydrology on the site displacements. To form the ML problem, these variables are constructed as the input vector of each ML training sample, while the vertical displacement of a GNSS site is regarded as the output value. In the evaluation experiments, three ML approaches, namely the gradient boosting decision tree (GBDT) approach, long short-term memory (LSTM) approach, and support vector machine (SVM) approach, are introduced and evaluated with the time series datasets collected from 9 GNSS sites over the period of 13 years. The results indicate that all three approaches achieve similar fitting precision in the range of 3–5 mm in the vertical displacement component, which is an improvement in over 30% with respect to the traditional LS fitting precision in the range of 4–7 mm. The prediction of the vertical time series with the three ML approaches shows the precision in the range of 4–7 mm over the future 24- month period. The results also indicate the relative importance of different physical features causing the displacements of each site. Overall, ML approaches demonstrate better performance and effectiveness in modelling and prediction of GNSS time series, thus impacting maintenance of geodetic reference frames, geodynamics, geophysics, and crustal deformation analysis.

Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

High-precision GNSS PWV retrieval using dense GNSS sites and in-situ meteorological observations for the evaluation of MERRA-2 and ERA5 reanalysis products over China

Precipitable water vapor (PWV) product with high accuracy and high spatiotemporal resolution is important for climate change research. Hourly PWV products with a high spatiotemporal resolution can be provided by global navigation satellite system (GNSS) and global reanalysis data. The National Aeronautics and Space Administration (NASA) and the European Center for Medium-Range Weather Forecasts (ECMWF) have recently released their global reanalysis of the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and the fifth-generation ECMWF Reanalysis (ERA5), respectively. A comprehensive evaluation of these two PWV reanalysis products over China has yet to be fully investigated. In this study, the PWV products of MERRA-2 and ERA5 reanalysis over China have been systematically evaluated based on 339 GNSS sites and in-situ ground meteorological observations from year 2016 to 2018. To mitigate the uncertainties of this evaluation, three enhanced vertical correction models for temperature, pressure and PWV that we developed previously are adopted. Four weighted mean temperature (Tm) models are established for the four regions of China (North China, South China, Tibet Plateau, and Northwest China), and they exhibit better accuracy than the other classical Tm models in China. The evaluation results of the MERRA-2- and ERA5-derived PWV show that the correlation coefficient, biases, and root mean square error (RMSE) in China are 0.98/0.99, −0.51/0.38 mm, and 2.50/1.99 mm, respectively. They both exhibit relatively large difference when applied to South China. For the same grid spatial resolution, ERA5 PWV product exhibits similar performance with MERRA-2. The monthly-mean-reanalysis-derived PWV exhibits obvious seasonality and has the largest difference in summer. ERA5 PWV can provide more details of the spatial pattern compared to MERRA-2 PWV. The diurnal anomaly variation for ERA5 is more consistent with GNSS data, and some differences at certain moments during autumn and winter have been observed. These results show that ERA5 PWV and MERRA-2 PWV exhibits excellent applicability in China and can help us to efficiently use PWV products for climate research in China.

Geodetic Analysis inside the South Korean Peninsula and Impact of the 2011 Tohoku–Oki (TO) Earthquake

AbstractThe most powerful Tohoku–Oki (TO) earthquake that occurred in Japan on 11 March 2011 affected Japan as well as South Korea. In the current study, we investigated contemporary geodetic deformation inside South Korea before and after the TO earthquake using Global Navigation Satellite System (GNSS) measurements from 01 January 2008 to 31 December 2017. Measured velocities of GNSS sites are modeled by Auto‐regressive moving average (ARMA) method to analyze the long‐term GNSS time‐series variation and to investigate the secular tectonic crustal deformation. We found that the maximum co‐seismic displacements during the TO earthquake reached up to 36.82 ± 0.21 mm in the east and 5.90 ± 0.08 mm in the north directions. The geometric model of the co‐seismic thrust surface was characterized by a rectangular plane with a dip of 12.0° and strike 200°. The thrust is situated at 25 km hypocenter depth, with an area roughly ∼470 km long and ∼120 km wide. The seismicity pattern after the earthquake indicated that the compressional strain started to be replaced by the extensional strain during the post TO earthquake period from 2011 to 2014. Further, the strain became predominantly extensional during the period 2015 to 2017, revealing an effective rotational change that occurred inside the Korean Peninsula.

Earthquake Magnitude Estimation using Precise Point Positioning

An accurate estimation of an earthquake magnitude plays an important role in targeting emergency services towards affected areas. Along with the traditional methods using seismometers, site displacements caused by an earthquake can be monitored by the Global Navigation Satellite Systems (GNSS). GNSS can be used either in real-time for early warning systems or in offline mode for precise monitoring of ground motion. The Precise Point Positioning (PPP) offers an optimal method for such purposes, because data from only one receiver are considered and thus not affected by other potentially not stable stations. Precise external products and empirical models have to be applied, and the initial convergence can be reduced or eliminated by the backward smoothing strategy or integer ambiguity resolution. The product for the magnitude estimation is a peak ground displacement (PGD). PGDs observed at many GNSS stations can be utilized for a robust estimate of an earthquake magnitude. We tested the accuracy of estimated magnitude scaling when using displacement waveforms collected from six selected earthquakes between the years 2016 and 2020 with magnitudes in a range of 7.5–8.2 Moment magnitude MW. We processed GNSS 1Hz and 5Hz data from 182 stations by the PPP method implemented in the G-Nut/Geb software. The precise satellites orbits and clocks corrections were provided by the Center for Orbit Determination in Europe (CODE). PGDs derived on individual GNSS sites formed the basis for ground motion parameters estimation. We processed the GNSS observations by the combination of the Kalman filter (FLT) and the backward smoother (SMT), which significantly enhanced the kinematic solution. The estimated magnitudes of all the included earthquakes were compared to the reference values released by the U. S. Geological Survey (USGS). The moment magnitude based on SMT was improved by 20% compared to the FLT-only solution. An average difference from the comparison was 0.07 MW and 0.09 MW for SMT and FLT solutions, respectively. The corresponding standard deviations were 0.18 MW and 0.22 MW for SMT and FLT solutions, which shows a good consistency of our and the reference estimates.

Geodetic Constraints on Cratonic Microplates and Broad Strain During Rifting of Thick Southern African Lithosphere

AbstractSouthern Africa is typically considered to belong to a single tectonic plate, Nubia, despite active faulting along the southwestern branch of the East African Rift System. We analyze regional Global Navigation Satellite System (GNSS) measurements, and find that the “San” microplate, situated south of the southwestern branch of the East African Rift, is statistically distinct from Nubia, with 0.4–0.7 mm/yr of extension across the boundary. Adding nine new campaign GNSS sites, we show that the extension rate across the southern Malawi Rift is 2.2 ± 0.3 mm/yr, with 75% of the relative velocity occurring over 890 km, despite the surface expression of faulting being <150 km wide. Thus, for the first time, we use geodetic measurements to describe the accommodation of strain in broad zones between Archean cratons in southern Africa's thick continental lithosphere.

HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

DETERMINATION OF RECENT TECTONIC DEFORMATIONS ALONG THE TUZ GÖLÜ FAULT ZONE IN CENTRAL ANATOLIA (TURKEY) WITH GNSS OBSERVATIONS

The Tuz Gölü Fault Zone (TGFZ) is one of the most important active tectonic structures of Central Anatolia. The morphotectonic features of the TGFZ and the distribution of the epicenters of earthquakes over magnitude 5.0 show that this fault zone remains active today. In this study, the deformation of the TGFZ is determined with high sensitivity using geodetic measurements. To obtain accurate information about the deformation of the TGFZ, 24 GNSS sites and two continuously operating reference stations were constructed in the southern part of the TGFZ. Between 2018 and 2020, Global Navigation Satellite Systems (GNSS) measurements were made on this network. The data of the Turkish National Fundamental GPS Network (TNFGN) and the continuously operating reference stations-Turkey (CORS-TR) sites around the study area were also included in the study, and GNSS measurements were evaluated with the GAMIT/GLOBK software, and velocity fields of the region were determined. In addition, block modeling of the study area was calculated using the GeodSuit software. For the first time, slip rates provided by the geodetic network are established directly on the TGFZ segments, filling a significant deficiency in the literature, contributing to understanding the tectonics of the country and the region, and providing an important dataset for evaluating the degree of seismic activity of the fault zone. The slip rates obtained within the scope of this study are approximately 1.8 mm/yr strike-slip and 2 mm/yr dip-slip for the Acıpınar and Helvadere segments on that Aksaray city is built on. These results indicate that the active deformation in the TGFZ is greater than previously expressed compared to the slip rates calculated in previous studies.

Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

A variety of climate factors influence the precision of the long‐term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long‐term GNSS monitoring records, this study combines the extended seven‐parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local‐scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long‐term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long‐term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long‐term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit‐less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS‐derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long‐term GNSS monitoring.

Spatio-temporal reductions of the COVID-19 lockdown-induced noise anomalies in GNSS height time series over mainland China

ABSTRACT The lockdowns imposed worldwide to curb the coronavirus diseases (COVID-19) spread has positive effects on the environment. However, it is unclear what fraction is caused by weather and what is related to lockdown. Here we used Global Navigation Satellite System (GNSS) height anomaly time series to quantify the spatio-temporal characteristics of lockdown-induced noise anomalies at 231 selected sites over mainland China. The results indicated that apparent declines (0.52 mm) in noise in 6 days (24–29 January 2020) after the lockdown in Wuhan resulted in drastically reduced human activities, which accounts for 71% of the total noise decrease. The lockdown effects persisted for 8 weeks and reached the maximum in the third week (6–12 February 2020) with reduced GNSS noise anomalies occurring at 81% of the GNSS sites. With the control of pandemic, increased noise anomalies occurred at more than 60% of the sites during the 9th week, which correlated well with the easing of lockdown in many cities in China. We concluded that this study provides new insights to quantifying the effects of human activities on geodetic measurements during the COVID-19 lockdown.

Strain calculations of active tectonic blocks in northeastern Venezuela from GNSS analysis

We study translation, rotation, and strain of active tectonic blocks in Northeastern Venezuela from the Global Navigation Satellite System (GNSS) observations. Since the installation of the geodetic network in 2003, one of the goals was to place at least three observation sites at each tectonic block to study the deformation of each one. Based on this premise, we define at least seven blocks: Bergantín and Caripe blocks south of the El Pilar Fault (EPF), and Cariaco Gulf, Land bridge, Paria, North Peninsula, and Margarita Island blocks north of the EPF. Our preferred block modeling shows angular rotations from 0.02 to 0.29° Ma−1. It is known that the EPF concentrates the active deformation in this region of the Caribbean-South American plate boundary. However, the existent rotation could accommodate part of the motion. The strain rate tensors (SRT) indicate NW-SE compression and NE-SW extension for the western blocks. To the east, the 4-Land bridge block keeps the NW-SE compression but shows a decrease in the extensional component. The 5-Paria blocks show a complete inversion in the sense of semi-axis. Additionally, we evaluate the possibility of different motions in Margarita block calculating translational vector, rotational velocity, and strain from three GNSS sites at each side thereof. Our results show remarkable similarities for the Macanao Peninsula and Eastern Margarita Island, pointing to both belonging to a single block.

Analysis and combination of multi-GNSS snow depth retrievals in multipath reflectometry

The Global Navigation Satellite Systems (GNSS) continuously broadcast radio signals at two or more frequencies in the L-band, providing extensive data for GNSS multipath reflectometry. Multi-GNSS constellations provide more signals and more tracks than individual constellations, achieving greater azimuthal coverage and more frequent retrievals. The main aim of this study is to retrieve snow depth using multi-GNSS data, analyze the multi-GNSS retrievals, and then combine them. Data of four constellations from three GNSS sites are analyzed, including the BeiDou and Galileo signals, which are rarely used to retrieve snow depth. The snow depth retrievals are estimated for signal-to-noise ratio data of each signal at first. The retrievals of individual signals from four constellations, except that of the GPS P-code signal, have no detectable inter-signal bias, thus showing the same trend describing snow depth variation. Then a multi-GNSS combination method based on robust regression is used to combine the inter-constellation inter-signal retrievals, and the multi-GNSS combined retrievals show an improvement in precision, availability, and temporal sampling. Compared to that of individual signals, it achieves about 50% improvement in precision, a smaller uncertainty, and a constant sampling interval of 6 h.

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.

Survey and Continuous GNSS in the Vicinity of the July 2019 Ridgecrest Earthquakes

Abstract The Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes of July 2019 occurred within 34 hr of each other on conjugate strike-slip faults in the Mojave Desert, just north of the central Garlock fault. Here, we present the results of a survey of 18 Global Navigation Satellite Systems (GNSS) sites conducted in the immediate aftermath of the earthquakes, including five sites that recorded the motion of the second earthquake after having been set up immediately following the first, as well as processed results from continuous GNSS sites throughout the region. Our field work in response to the earthquakes provides additional constraints on the ground displacement due to both earthquakes, complementing data from a spatially sparser network of continuously recording GNSS sites in the area, as well as temporally sparser Interferometric Synthetic Aperture Radar data that were able to capture a combined deformation signal from the two earthquakes.

Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized least squares (LSQ) approach and a compressive sensing (CS) approach for water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) slant wet delay (SWD) estimates. In this context, the term “observing geometry” mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are (1) the comparison of the observing geometry's effects on the tomographic reconstruction accuracy when using LSQ or CS for the solution of the tomographic system and (2) the investigation of the effect of the signal directions' variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS intersite distance represents a good rule of thumb for both LSQ- and CS-based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available.

Contemporary deformation and strain analysis in South Korea based on long-term (2000–2018) GNSS measurements

South Korea forms the Korean Peninsula and is considered to be tectonically more stable than the other regions of Eastern Asia. Global Navigation Satellite System (GNSS) sites installed in the country contribute to geoscientific studies on contemporary deformation, strain pattern, and current kinematics of tectonic blocks. In the current study, we used long-term GNSS measurements (from 1 January 2000 to 31 December 2018) to study block-wise geodetic deformation and strain pattern of the country, including the co-seismic effect of the 2011 Tohoku-Oki (TO) Earthquake. Measured velocities of GNSS sites are modelled via exponential smoothing method to analyse the GNSS time-series and obtain the secular tectonic crustal deformation. The analysis of strain pattern and vertical displacements indicates that extensional deformation followed compressional deformation from the northern Korean blocks to the southern Korean blocks. Finally, we examined the angular velocity and pole of rotation of the South Korean blocks using pre-seismic and post-seismic displacements of the GNSS sites. The results indicate that the angular velocity of the northern blocks was lower, while the southern block velocity is higher after the TO earthquake. We believe that the integrated approaches applied in the study would provide better insights into the active deformation of South Korea.

The Influence of Different Modelling Factors on Global Temperature and Pressure Models and Their Performance in Different Zenith Hydrostatic Delay (ZHD) Models

Surface temperature and pressure are indispensable variables in Global Navigation Satellite System (GNSS) meteorology. The lack of meteorological observations located at or near the GNSS sites is a big challenge for the calculation of accurate zenith hydrostatic delay (ZHD). Therefore, various empirical models with different model forms were established to provide temperature and pressure values. In this study, the influence of different modelling factors, including model forms, temporal resolution of the data sources, and the spatial resolution of the data sources, is evaluated and the temperature and pressure model with the best performance is developed. On the basis of the meteorological parameters estimated by the above model, we analyzed the global performance of the three most commonly used ZHD models, that is, the Saastamoinen, Hopfield, and Black models. The numerical results show that the model with the idea of time-segmented modelling performs best, of which the global mean root mean square (RMS), mean absolute error (MAE), and standard deviation (SD) are 7.87/6.33/7.17 hPa and 2.95/2.31/2.79 K for pressure and temperature, respectively, using the data sources with temporal resolution of 2 h and spatial resolution of 2.5° × 2° in the reanalysis data comparison. In comparison with the radiosonde data, the mean RMS/MAE/SD are 7.02/5.24/6.46 hPa and 4.05/3.17/3.86 K for pressure and temperature, respectively. The Saastamoinen model with a global mean bias/RMS of 1.01/16.9 mm achieved the best ZHD estimated values compared with the other two ZHD models.

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.

A numerical method to improve the spatial interpolation of water vapor from numerical weather models: a case study in South and Central America

Abstract. Commonly, numerical weather model (NWM) users can get the vertically integrated water vapor (IWV) value at a given location from the values at nearby grid points. In this study we used a validated and freely available global navigation satellite system (GNSS) IWV data set to analyze the very well-known effect of height differences. To this end, we studied the behavior of 67 GNSS stations in Central and South America with the prerequisite that they have a minimum of 5 years of data during the period from 2007 to 2013. The values of IWV from GNSS were compared with the respective values from ERA-Interim and MERRA-2 from the same period. Firstly, the total set of stations was compared in order to detect cases in which the geopotential difference between GNSS and NWM required correction. An additive integral correction to the IWV values from ERA-Interim was then proposed. For the calculation of this correction, the multilevel values of specific humidity and temperature given at 37 pressure levels by ERA-Interim were used. The performance of the numerical integration method was tested by accurately reproducing the IWV values at every individual grid point surrounding each of the GNSS sites under study. Finally, considering the IWVGNSS values as a reference, the improvement introduced to the IWVERA-Interim values after correction was analyzed. In general, the corrections were always recommended, but they are not advisable in marine coastal areas or on islands as at least two grid points of the model are usually in the water. In such cases, the additive correction could overestimate the IWV.

Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator

Abstract. From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3-D grid of voxels, and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting, the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of central Europe during the period of 29 May–14 June 2013, when heavy-precipitation events were observed. For both models, slant wet delays (SWDs) were calculated based on estimates of zenith total delay (ZTD) and horizontal gradients, provided for 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model), in order to assess the impact of different factors on the tomographic solution. The GNSS tomography outputs have been assimilated into the nested (12 and 36 km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3D-Var) system, in particular, its radio occultation observation operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy-precipitation events. Future improvements to the assimilation method are also discussed.