Global Navigation Satellite System Ground Research Articles

GNSS Ground deformation observation network optimization assisted using prior InSAR-derived ground surface deformation and multiscale iteration estimation

ABSTRACT Ground surface deformation must be monitored to understand and prevent geological hazards. Global navigation satellite system (GNSS) technology has been widely used in deformation monitoring; however, current GNSS station layout strategies are often subjective and lack theoretical guidance. Therefore, in this study, a GNSS deformation observation network optimization method was developed based on interferometric synthetic aperture radar (InSAR) prior to deformation assistance. This method considers the deformation distribution, cost, accuracy, topography, and actual monitoring requirements and uses kriging interpolation and multiscale iterative optimization to obtain the reference number, spatial distribution, and deformation capture accuracy of GNSS stations in areas experiencing deformation. Simulation experiments showed that the proposed method strengthened the spatial deformation monitoring ability of GNSS by 46%–68% compared with the traditional station layout methods, which ignore the distribution of the deformation. Then, based on the small baseline subset (SBAS) InSAR technology and applying the new method, we evaluated the number and location of the most suitable GNSS stations for a mining area and found that 89.6% of the error was less than 1.5 cm, which further illustrates the practicability and reliability of the proposed method. This method provides an effective supplement to station layouts for monitoring GNSS deformation.

Sensitivity of Shipborne GNSS Estimates to Processing Modeling Based on Simulated Dataset.

The atmospheric water vapor is commonly monitored from ground Global Navigation Satellite System (GNSS) measurements, by retrieving the tropospheric delay under the Zenith Wet Delay (ZWD) component, linked to the water vapor content in the atmosphere. In recent years, the GNSS ZWD retrieval has been performed on shipborne antennas to gather more atmospheric data above the oceans for climatology and meteorology study purposes. However, when analyzing GNSS data acquired by a moving antenna, it is more complex to decorrelate the height of the antenna and the ZWD during the Precise Point Positioning (PPP) processing. Therefore, the observation modeling and processing parametrization must be tuned. This study addresses the impact of modeling on the estimation of height and ZWD from the simulation of shipborne GNSS measurements. The GNSS simulation is based on an authors-designed simulator presented in this article. We tested different processing models (elevation cut-off angle, elevation weighting function, and random walk of ZWD) and simulation configurations (the constellations used, the sampling of measurements, the location of the antenna, etc.). According to our results, we recommend processing shipborne GNSS measurements with 3° of cut-off angle, elevation weighting function square root of sine, and an average of 5 mm·h-1/2 of random walk on ZWD, the latter being specifically adapted to mid-latitudes but which could be extended to other areas. This processing modeling will be applied in further studies to monitor the distribution of water vapor above the oceans from systematic analysis of shipborne GNSS measurements.

Ionospheric Disturbances Triggered by China's Long March 2D Rocket

Using the data of 382 ground global navigation satellite system (GNSS) network stations in Western China, we studied and analyzed the ionospheric disturbances triggered by the “Long March” 2D rocket launch in Jiuquan, China on December 3, 2017. As compared with previous research, a higher sampling resolution for GNSS data (with a frequency of 1 Hz) was used to obtain more accurate occurrence times and propagation velocities of ionospheric disturbances. By using a method based on a quadratic function of time to fit a raw total electron content (TEC) series, a filtered TEC series was calculated using carrier observations, and a two-dimensional disturbances map was drawn. A new method, which accounts for the flight time of the rocket, was used to calculate the velocity of the shock wave. Ionospheric depletions and the shock wave were observed after the launch of the rocket. The depletion was observed within 100 to 1000 km south of the launch site along the rocket trajectory, which had a maximum amplitude of ∼3.8 TEC units (TECU), reaching ∼56% of the background TEC. A shock wave of V-shaped disturbances with amplitudes of ∼0.67 TECU was detected on both sides of the rocket trajectory. The shock wave moved southeast at an average velocity of ∼1861 m/s at a location 2200 km away from the launch site. Ionospheric disturbances with distances of more than ∼3000 km from the launch site were also observed.

Common-mode signals and vertical velocities in the greater Alpine area from GNSS data

Abstract. We study the time series of vertical ground displacements from continuous global navigation satellite system (GNSS) stations located in the European Alps. Our goal is to improve the accuracy and precision of vertical ground velocities and spatial gradients across an actively deforming orogen, investigating the spatial and temporal features of the displacements caused by non-tectonic geophysical processes. We apply a multivariate statistics-based blind source separation algorithm to both GNSS displacement time series and ground displacements modeled from atmospheric and hydrological loading, as obtained from global reanalysis models. This allows us to show that the retrieved geodetic vertical deformation signals are influenced by environment-related processes and to identify their spatial patterns. Atmospheric loading is the most important process, reaching amplitudes larger than 2 cm, but hydrological loading is also important, with amplitudes of about 1 cm, causing the peculiar spatial features of GNSS ground displacements: while the displacements caused by atmospheric and hydrological loading are apparently spatially uniform, our statistical analysis shows the presence of N–S and E–W displacement gradients. We filter out signals associated with non-tectonic deformation from the GNSS time series to study their impact on both the estimated noise and linear rates in the vertical direction. Taking into account the long time span of the time series considered in this work, while the impact of filtering on rates appears rather limited, the uncertainties estimated from filtered time series assuming a power law plus white noise model are significantly reduced, with an important increase in white noise contributions to the total noise budget. Finally, we present the filtered velocity field and show how vertical ground velocity spatial gradients are positively correlated with topographic features of the Alps.

Routine Measurement of Water Vapour Using GNSS in the Framework of the Map-Io Project

The “Marion Dufresne Atmospheric Program-Indian Ocean” (MAP-IO) project is a research program that aims to collect long-term atmospheric observations in the under-instrumented Indian and Austral Oceans. As part of this project, a Global Navigation Satellite System (GNSS) antenna was installed on the research vessel (R/V) Marion Dufresne in October 2020. GNSS raw data is intended to be used to retrieve Integrated Water Vapour (IWV) content along the Marion Dufresne route, which cruises more than 300 days per year in the tropical and austral Indian Ocean. This paper presents a first assessment of this GNSS-based IWV retrieval, based on the analysis of 9 months of GNSS raw data acquired along the route of the R/V Marion Dufresne in the Indian Ocean. A first investigation of GNSS raw data collected during the first 5 months of operation has highlighted the bad positioning of the antenna on the R/V that makes it prone to interference. Changing the location of the antenna has been shown to improve the quality of the raw data. Then, ship-borne GNSS-IWV are compared with IWV estimates deduced using more conventional techniques such as European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), ground-launched radiosondes and permanent ground GNSS stations operating close to the route of the R/V Marion Dufresne. The rms difference of 2.79 kg m−2 shows a good match with ERA5 and subsequently improved after the change in location of the GNSS antenna (2.49 kg m−2). The match with ground-based permanent GNSS stations fluctuates between 1.30 and 3.63 kg m−2, which is also shown to be improved after the change in location of the GNSS antenna. However, differences with ground-launched radiosondes still exhibit large biases (larger than 2 kg m−2). Finally, two operational daily routine analyses (at day+1 and day+3) are presented and assessed: the rms of the differences are shown to be quite low (1 kg m−2 for the day+1 analyses, 0.7 kg m−2 for the day+3 analysis), which confirms the quality of these routine analysis. These two routine analyses are intended to provide a continuous monitoring of water vapour above the Indian Ocean and deliver ship-borne IWV with a low latency for the entire scientific community.

Aging of ground Global Navigation Satellite System oscillators

Global Navigation Satellite System (GNSS) are widely used in many areas of human life and activity. The proper functioning of GNSS systems depends on several factors, the most important of which is the correct knowledge of time. The position indirectly is based on the knowledge of the distance, which is determined based on time with the knowledge of the speed of the electromagnetic wave. Thus, proper (accurate) knowledge of time (GNSS clock stability) is a key to precise positioning. In this text, the long-term stability of the GNSS station clocks covering the years 1994-2020 was analysed. For this purpose, the corrections of the clocks at selected permanent stations were used, and their stability was determined for all years separately. Then the change of clock stability over time and the search for correlation were analysed. As the results showed, there are clearly differences between four of the type of oscillators analysed. In case of the comparison on an annual basis, no change over time was found.

Flash Floods Prediction Using Precipitable Water Vapor Derived From GPS Tropospheric Path Delays Over the Eastern Mediterranean

A flash flood is a rapid and intense response of a drainage area to heavy rainfall events. In the arid and semi arid parts of the Eastern Mediterranean (EM) region, the spatio-temporal distribution of rainfall is the most important factor for flash flood generation. A possible precursor to heavy rainfall events is the rise in tropospheric water vapor amount, which can be remotely sensed using ground based Global Navigation Satellite Systems (GNSS) stations. Here, we use the Precipitable Water Vapor (PWV) derived from 9 GNSS ground based stations in the arid part of the EM region in order to predict flash floods. Our approach includes using three types of Machine Learning (ML) models in a binary classification task which predicts whether a flash flood will occur given 24 hours of PWV data. We train our models with 107 unique flash flood events and vigorously test them using a nested cross validation technique. The results indicate a good agreement between all three types of models and across various score metrics. In addition, the models are further improved by adding more features such as surface pressure measurements. Finally, a feature importance analysis shows that the most important features are the PWV values from 2 to 6 hours prior a flash flood. These promising results indicate that it is possible to augment the current flash flood warning systems with a near real-time GNSS ground based data driven approach as demonstrated in this work.

A Method for dSTEC Interpolation: Ionosphere Kernel Estimation Algorithm

Ionospheric structure is important for estimating ionospheric delay for user stations in Global Navigation Satellite System (GNSS). However, most existing parameter estimation methods suffer from challenges due to data inaccuracy and unavailability of limited and sparse scattered data at ground reference stations. The high variability of active low latitude or disturbed ionosphere leads to GNSS signal scintillation. It is critical to capture ionospheric random structure and estimate ionospheric parameter using data of disperse receivers to improve accuracy. This paper proposes a unifying method named Ionosphere Kernel Estimation Algorithm (IKEA) to retrieve the information of ionospheric spatial structure. The proposed model utilities the semi-parametric representation theorem to incorporate prior information and constraints. The multiple kernel technique is adopted firstly to include physical correlations. Additionally, a learning approach is deployed to determine model parameters. The IKEA model has been verified based on simulated and experimental data at active low latitudes from a network of ground GNSS reference stations from all visible GPS and GALILEO satellites. The IKEA model reduces approximately 19.5% and 24.2% of differential Slant Total Electron Content (dSTEC) in the root mean square error with respect to Inverse Distance Weighting (IDW) and the Kriging model during high ionospheric activities. The IKEA architecture has been demonstrated effective to make robust ionospheric estimation, which may be further extended for various GNSS applications and beyond.

Systematic Detection of Anomalous Ionospheric Perturbations Above LEOs From GNSS POD Data Including Possible Tsunami Signatures

In this paper we show the capability of Global Navigation Satellite System (GNSS) Precise Orbit Determination (POD) Low Earth Orbiter (LEO) data to detect anomalous ionospheric disturbances in the spectral range of the signals associated to earthquakes and tsunamis, applied to two of these events in Papua New Guinea (PNG) and Solomon Islands during 2016. This is achieved thanks to the new PIES approach <i>( POD-GNSS LEO Detrended Ionospheric Electron Content Significant Deviations)</i>. The significance of such ionospheric signals above the Swarm LEOs is confirmed with different types of independent data: in-situ electron density measurements provided by the Langmuir Probe (LP) on board Swarm LEOs, DORIS and ground-based GNSS co-located measurements, as it is described in the paper. In this way we conclude the possible detection of the tsunami related ionospheric gravity wave in PNG 2016 event, consistent with the most-recent theory which shows that a tsunami (which is localized in space and time) excites a spectrum of gravity waves, some of which have faster horizontal phase speeds than the tsunami. We believe that this work shows as well the feasibility of a future potential monitoring system of ionospheric disturbances, to be made possible by hundreds of cubesats with POD GNSS receivers among other appropriate sensors, and supported for real-time or near real-time confirmation and characterization by thousands of worldwide existing ground GNSS receivers.

An EISCAT UHF/ESR Experiment That Explains How Ionospheric Irregularities Induce GPS Phase Fluctuations at Auroral and Polar Latitudes

AbstractA limitation to the use of Global Navigation Satellite System (GNSS) for precise and real‐time services is introduced by irregularities in the ionospheric plasma density. An EISCAT UHF/ESR experiment was conducted to characterize the effect of electron density irregularities on temporal fluctuations in TEC along directions transverse to GPS ray paths in the high latitudes ionosphere. Two representative case studies are described: Enhancements in temporal TEC fluctuations originating (a) in the auroral ionosphere following auroral particle precipitation and (b) in the polar ionosphere following the drift of a polar patch as well as particle precipitation. The results indicate that the origin of enhancements in TEC fluctuations is due to the propagation through large‐to‐medium scale irregularities (i.e., ranging from few kilometres in the E region to few tens of kilometres in the F region) and occurring over spatial distances of up to approximately in the E region and up to approximately in the F region with a patchy distribution. Furthermore, the results indicate that enhancements in TEC fluctuations produced by polar plasma patches and particle precipitation occur over similar temporal scales, thus explaining the overall observation of higher phase scintillation indices in the high‐latitude ionosphere. The similarity in the temporal scales over which enhancements in TEC fluctuations occur in the presence of both particle precipitation and plasma patches suggests an intrinsic limitation in the monitoring and tracking of plasma patches through ground GNSS observations.

A Review of Voxel-Based Computerized Ionospheric Tomography with GNSS Ground Receivers

Ionized by solar radiation, the ionosphere causes a phase rotation or time delay to trans-ionospheric radio waves. Reconstruction of ionospheric electron density profiles with global navigation satellite system (GNSS) observations has become an indispensable technique for various purposes ranging from space physics studies to radio applications. This paper conducts a comprehensive review on the development of voxel-based computerized ionospheric tomography (CIT) in the last 30 years. A brief introduction is given in chronological order starting from the first report of CIT with simulation to the newly proposed voxel-based algorithms for ionospheric event analysis. The statement of the tomographic geometry and voxel models are outlined with the ill-posed and ill-conditioned nature of CIT addressed. With the additional information from other instrumental observations or initial models supplemented to make the coefficient matrix less ill-conditioned, equation constructions are categorized into constraints, virtual data assimilation and multi-source observation fusion. Then, the paper classifies and assesses the voxel-based CIT algorithms of the algebraic method, statistical approach and artificial neural networks for equation solving or electron density estimation. The advantages and limitations of the algorithms are also pointed out. Moreover, the paper illustrates the representative height profiles and two-dimensional images of ionospheric electron densities from CIT. Ionospheric disturbances studied with CIT are presented. It also demonstrates how the CIT benefits ionospheric correction and ionospheric monitoring. Finally, some suggestions are provided for further research about voxel-based CIT.

GROOPS: A software toolkit for gravity field recovery and GNSS processing

The Gravity Recovery Object Oriented Programming System (GROOPS) is a software toolkit written in C++ that enables the user to perform core geodetic tasks. Key features of the software include gravity field recovery from satellite and terrestrial data, the determination of satellite orbits from global navigation satellite system (GNSS) measurements, and the computation of GNSS constellations and ground station networks. Next to raw data processing, GROOPS is capable to operate on time series and spatial data to directly analyze and visualize the computed data sets. Most tasks and algorithms are (optionally) parallelized through the Message Passing Interface, thus the software enables a smooth transition from single-CPU desktop computers to large distributed computing environments for resource intensive tasks. For an easy and intuitive setup of complex workflows, GROOPS contains a graphical user interface to create and edit configuration files. The source code of the software is freely available on GitHub (https://github.com/groops-devs/groops) together with documentation, a cookbook with guided examples, and step-by-step installation instructions.

Global Ionospheric Modeling Using Multi-GNSS and Upcoming LEO Constellations: Two Methods and Comparison

Global ionosphere model with high accuracy and resolution is of great importance for ionospheric research and global navigation satellite system (GNSS) precise positioning applications. In the last 20 years, the accuracy and reliability of global ionospheric model benefitted from the development of GNSS technology have been improved significantly, but its performance is still not good over many regions (e.g., oceans and polar region) due to the lack of ground GNSS stations. Fortunately, the rapid development of low earth orbit (LEO) satellite constellations provides a potential opportunity to address this issue. However, there is still no optimal approach to make use of the LEO-based ionospheric observations because of the different observation ranges compared with that of ground-based GNSS ionospheric observations. In this article, two approaches are proposed to combine GNSS and LEO observation data for ionosphere modeling, which are single-layer normalization (SLN) method and dual-layer superposition (DLS) method, respectively. The results exhibit a significant improvement of ionospheric model accuracy by combining LEO and GNSS observation data based on our proposed methods compared with that using GNSS data only, with a reduction in root mean square (rms) error of about 25% and 21% for SLN method and DLS method, respectively. The article also highlights the relations between the performance of ionospheric model estimated by the SLN method and LEO ionospheric observations with different observation accuracy and different satellite cut-off elevations. The results indicate that ionospheric model estimated by GNSS/LEO using SLN method improves at least 25% compared with that by GNSS only. The improvement of ionospheric model estimated with the cut-off elevation of 50° is the best, followed by 70°, and then 20°.

IWV retrieval from ground GNSS receivers during NAWDEX

Abstract. A ground-based network of more than 1200 Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) was analysed using GIPSY-OASIS II software package for the documentation of time and space variations of water vapor in atmosphere during the North Atlantic Waveguide and Downstream impact EXperiment (NAWDEX) during fall 2016. The network extends throughout the North Atlantic, from the Caribbeans to Morocco through Greenland. This paper presents the methodology used for GNSS data processing, screening, and conversion of Zenith Tropospheric Delay (ZTD) estimates to Integrated Water Vapor content (IWV) using surface parameters from reanalysis. The retrieved IWV are used to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses ERAI and ERA5. ERA5 shows an overall improvement over ERAI in representing the spatial and temporal variability of IWV over the study area. The mean bias is decreased from 0.31±0.63 to 0.19±0.56 kg m−2 (mean ±1σ over all stations) and the standard deviation reduced from 2.17±0.67 to 1.64±0.53 kg m−2 combined with a slight improvement in correlation coefficient from 0.95 to 0.97. At regional scale, both reanalyses show a general wet bias at mid and northern latitudes but a dry bias in the Caribbeans. We hypothesize this results from the different nature of data being assimilated over the tropical oceans. This GNSS IWV data set is intended to be used for a better description of the high impact weather events that occurred during the NAWDEX experiment.

First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS

At some frequencies, Earth’s ionosphere may significantly impact satellite communications, Global Navigation Satellite Systems (GNSS) positioning, and Earth Observation measurements. Due to the temporal and spatial variations in the Total Electron Content (TEC) and the ionosphere dynamics (i.e., fluctuations in the electron content density), electromagnetic waves suffer from signal delay, polarization change (i.e., Faraday rotation), direction of arrival, and fluctuations in signal intensity and phase (i.e., scintillation). Although there are previous studies proposing GNSS Reflectometry (GNSS-R) to study the ionospheric scintillation using, for example TechDemoSat-1, the amount of data is limited. In this study, data from NASA CYGNSS constellation have been used to explore a new source of data for ionospheric activity, and in particular, for travelling equatorial plasma depletions (EPBs). Using data from GNSS ground stations, previous studies detected and characterized their presence at equatorial latitudes. This work presents, for the first time to authors’ knowledge, the evidence of ionospheric bubbles detection in ocean regions using GNSS-R data, where there are no ground stations available. The results of the study show that bubbles can be detected and, in addition to measure their dimensions and duration, the increased intensity scintillation (S4) occurring in the bubbles can be estimated. The bubbles detected here reached S4 values of around 0.3–0.4 lasting for some seconds to few minutes. Furthermore, a comparison with data from ESA Swarm mission is presented, showing certain correlation in regions where there is S4 peaks detected by CYGNSS and fluctuations in the plasma density as measured by Swarm.

3‐D Tomography of the Ionospheric Anomalies Immediately Before and After the 2011 Tohoku‐Oki (Mw9.0) Earthquake

AbstractA dense network of ground global navigation satellite system receivers detected ionospheric total electron content (TEC) changes starting ~40 min before the 2011 Tohoku‐oki (Mw9.0) earthquake around the ruptured fault, together with the long‐lasting postseismic TEC drop. In this paper, we robustly estimate three‐dimensional (3‐D) distribution of both preseismic and postseismic ionospheric anomalies of the 2011 Tohoku‐oki earthquake by tomographic inversions of electron density anomalies. We set up &gt;6,000 blocks, as large as 1.0° (east‐west) × 0.9° (north‐south) × 60 km (vertical), over the Japanese Islands, the Sea of Japan, and the Korean Peninsula, up to 870 km altitude. By using TEC anomalies of pairs exceeding ~1,300 stations and eight satellites obtained using reference curves, we estimated electron density anomalies within individual blocks. The results showed that the preseismic and postseismic anomalies do not overlap in space. The preseismic anomalies are composed of low (~300 km height) positive and high (~600 km height) negative anomalies. They occurred above the land of NE Japan without extending offshore, suggesting its origin related to surface electric charges. On the other hand, the postseismic electron depletion occurred offshore above the region where large coseismic uplift took place. These results demonstrate that the preseismic and postseismic ionospheric anomalies are independent not only temporarily but also spatially and certainly in underlying physical mechanisms. We propose a simple model to explain how surface charges redistribute ionospheric electrons to make the observed preseismic electron density anomalies.

Ionospheric Responses to the June 2015 Geomagnetic Storm from Ground and LEO GNSS Observations

Geomagnetic storms are extreme space weather events, which have considerable impacts on the ionosphere and power transmission systems. In this paper, the ionospheric responses to the geomagnetic storm on 22 June 2015, are analyzed from ground-based and satellite-based Global Navigation Satellite System (GNSS) observations as well as observational data of digital ionosondes, and the main physical mechanisms of the ionospheric disturbances observed during the geomagnetic storm are discussed. Salient positive and negative storms are observed from vertical total electron content (VTEC) based on ground-based GNSS observations at different stages of the storm. Combining topside observations of Low-Earth-Orbit (LEO) satellites (GRACE and MetOp satellites) with different orbital altitudes and corresponding ground-based observations, the ionospheric responses above and below the orbits are studied during the storm. To obtain VTEC from the slant TEC between Global Positioning System (GPS) and LEO satellites, we employ a multi-layer mapping function, which can effectively reduce the overall error caused by the single-layer geometric assumption where the horizontal gradient of the ionosphere is not considered. The results show that the topside observations of the GRACE satellite with a lower orbit can intuitively detect the impact caused by the fluctuation of the F2 peak height (hmF2). At the same time, the latitude range corresponding to the peak value of the up-looking VTEC on the event day becomes wider, which is the precursor of the Equatorial Ionization Anomaly (EIA). However, no obvious response is observed in the up-looking VTEC from MetOp satellites with higher orbits, which indicates that the VTEC responses to the geomagnetic storm mainly take place below the orbit of MetOp satellites.

Mapping of Archaeological Sites using UAV Aerial Survey and PPK GNSS Ground Survey Techniques in Central Asia

Geospatial technologies such as Unmanned Aerial Vehicle (UAV) and Global Navigation Satellite Systems (GNSS) are becoming popular in various applications also for archaeological purposes. This is due to the abilities also the usefulness of UAV and GNSS technologies for digitally documenting and recording archaeological sites in great detail. Such information is crucial for the efforts of preserving and protecting historical sites. This paper demonstrates the workflows and results of mapping techniques using consumer-grade UAV for aerial survey and low-cost Post-Processing Kinematics (PPK) GNSS ground survey for documentation of two archaeological sites in Batken State, Kyrgyzstan in Central Asia. Both UAV and PPK GNSS data were acquired simultaneously during the fieldworks in early winter while processing were explicitly done using currently available commercial and open-source software. Results of the UAV and PPK GNSS was combined and qualitatively evaluated. Despite advantages and disadvantages identified in both UAV and PPK GNSS results, it was learned that the combination of both techniques found to be very beneficial for mapping of archaeological sites.

Estimation of LEO-GPS receiver differential code bias based on inequality constrained least square and multi-layer mapping function

The differential code bias (DCB) of the global navigation satellite system (GNSS) receiver onboard a low earth orbit (LEO) satellite is one of the crucial hardware error sources in ionospheric estimation. In common practice, LEO DCBs are considered as constant within a single day. However, since such receivers are installed on moving platforms, they are subjected to the ever-changing space environment, and thus, LEO DCBs are prone to more frequent fluctuations than receiver DCBs of ground GNSS stations. We estimate the LEO GPS receiver DCBs and plasmaspheric vertical total electron content by using inequality constrained least square and a multi-layer mapping function (MF) approach to minimize mapping errors. The GPS receiver DCBs onboard constellation observing system for meteorology, ionosphere, and climate satellites (COSMIC) is investigated and analyzed during the January 2008 period. The results show that in most cases the multi-layer MF approach performs better than the commonly used single-layer MF. The mapping error from an inaccurately fixed single-layer height can be considerably reduced. Meanwhile, with the help of a multi-layer MF, the mean deviations of DCBs for five receivers compared with those based on a geometric MF at 1400 km are significantly decreased by 59%, 15%, 26%, 47%, and 22%, respectively.

Precipitable Water Vapor Converted from GNSS-ZTD and ERA5 Datasets for the Monitoring of Tropical Cyclones

Tropical cyclones (TCs) are intense rotating storm systems, characterized by strong winds, heavy humidity and storm surges, and usually cause an abnormal increase in regional water vapor during the period of their occurrence. Global navigation satellite system (GNSS) is well-established and initially mainly for positioning, navigation and timing. However, due to their global coverage and 24/7 operability under all weather conditions, GNSS has been widely used in meteorology such as the retrieval of precipitable water vapor (PWV). Since PWV time series obtained from a regional ground GNSS tracking stations network has high temporal and spatial resolutions, in this research, the temporal and spatial variation trends of regional PWVs during several TCs’ periods in Hong Kong were investigated, and a new method using high-resolution GNSS-PWV time series to estimate a TC’s movement was proposed. Test data were from 15 GNSS tracking stations equipped with meteorological sensors, and three GNSS stations without meteorological sensors but using meteorological data interpolated from ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset, and six TC events occurred in 2017 were selected for cases studies. Moreover, for a better accuracy of GNSS-PWV, which is converted from the zenith wet delay of the GNSS signal multiplying a conversion factor that uses a weighted mean temperature ( $T_{m}$ ), a seasonal multi-factor $T_{m}$ model was established and tested. Results showed that the new $T_{m}$ model outperformed the previous regional $T_{m}$ model used in Hong Kong. In addition, based on the temporal variation trend and spatial distribution of the regional PWV, one can determine if a TC approaches the region. A geometric approach to estimating a TC’s movement was developed and its test results agreed well with the truth. These results suggest the potential of using GNSS signals to monitor the movements of TCs for short-time TC forecasting due to their high temporal resolution.