Ground-based Global Navigation Satellite System Observations Research Articles

Inversion of terrestrial water storage changes from GNSS vertical displacements using a priori constraint: A case study of the Yunnan Province, China

Global Navigation Satellite System (GNSS) is an effective means to monitor surface deformations associated with changes in terrestrial water storage (TWS). In this study, we introduced a priori constraint matrix and estimated the regularization parameter through an iterative least-squares method to infer the TWS changes using GNSS vertical displacements from December 2010 to February 2021 in the Yunnan Province (YNP), China. The GNSS-inferred TWS changes were validated through the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) estimates, the Global Land Data Assimilation System (GLDAS) hydrological model, and the meteorological data (i.e., precipitation and Drought Severity Index (DSI)). The results demonstrate that the spatial resolution of TWS changes derived from 45 GNSS stations can reach 2°×2° in the YNP, and the a priori constraint is better than the traditional Laplacian matrix constraint for solving the discrete ill-conditioned problem of GNSS inversion. The GNSS-inferred TWS changes are consistent with the TWS changes derived from GRACE/GFO and GLDAS estimates in the spatio-temporal domains over the YNP, but the GNSS inversion results show stronger amplitudes. Furthermore, the ground-based GNSS observations are more sensitive to the TWS changes, and the correlation between precipitation and GNSS-inferred TWS changes is improved by about 11 % and 5 % compared to GRACE/GFO and GLDAS estimates in the YNP, respectively. The GNSS-inferred DSI can well reveal the two severe drought events in the YNP during the study period, which is consistent with the DSI derived from GRACE/GFO and GLDAS estimates, as well as the published self-calibrating Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). The GNSS observations can compensate the limitations of GRACE/GFO observations (e.g., bridging the data gap between GRACE and GFO missions), and help to better investigate the TWS changes in the YNP, which has significant potentials for regional water resource management and extreme climate changes monitoring.

Water-Vapour Monitoring from Ground-Based GNSS Observations in Northwestern Argentina

The Central Andes in northwestern Argentina are characterized by steep topographic and climatic gradients. The humid foreland areas at 1 km asl elevation rapidly rise to over 5 km in the eastern Cordillera, and they form an orographic rainfall barrier on the eastern windward side. This topographic setting combined with seasonal moisture transport through the South American monsoon system leads to intense rainstorms with cascading effects such as landsliding and flooding. In order to better quantify the dynamics of water vapour transport, we use high-temporal-resolution global navigation satellite system (GNSS) remote sensing techniques. We are particularly interested in better understanding the dynamics of high-magnitude storms with high water vapour amounts that have destructive effects on human infrastructure. We used an existing GNSS station network with 12 years of time series data, and we installed two new ground stations along the climatic gradient and collected GNSS time series data for three years. For several stations we calculated the GNSS signal delay gradient to determine water vapour transport direction. Our statistical analysis combines in situ rainfall measurements and ERA5 reanalysis data to reveal the water vapour transport mechanism for the study area. The results show a strong relationship between altitude and the water vapour content, as well as between the transportation pathways and the topography.

Arctic TEC Mapping Using Integrated LEO-Based GNSS-R and Ground-Based GNSS Observations: A Simulation Study

Ionospheric total electron content (TEC) maps with high spatial and temporal resolutions are essential for depicting the state of the ionosphere and for performing ionospheric delay corrections associated with satellite navigation applications. Low Earth orbit (LEO) CubeSat-based global navigation satellite system (GNSS) reflectometry (GNSS-R) measurements provide a promising opportunity for retrieval of ionospheric TEC over sea ice and calm waters, which offers a potential new data source to fill the gaps of ground-based GNSS networks. However, the GNSS-R slant TEC (sTEC) estimations include contributions from the incident and reflection ray paths, whose ionospheric piercing points (IPPs) can be separated by hundreds of kilometers. This article presents an algorithm that integrates sTEC measurements from the GNSS-R CubeSats and available ground-based GNSS receivers to derive Arctic vertical TEC (vTEC) maps. A simulation study using the model ionosphere constructed from the NeQuick-2 is conducted to assess the performance of the algorithm. Varying levels of temporal resolutions and solar activities, and the number of CubeSats and the number of maximum simultaneously tracked reflection signals by a CubeSat are implemented in the simulation. The results show that the inclusion of coherent GNSS-R measurements improves the accuracy of the vTEC maps under all levels of solar activities. The RMSE improvement percentage is most obvious when the update interval is the shortest. Increasing the number of CubeSats further improves the accuracy. However, no significant improvement in vTEC map accuracy is observed when the number of maximum simultaneously tracked GNSS-R satellites is higher than 4. Quantitative measures and analyses of the algorithm performances are presented in this article.

Detecting Seismo‐Ionospheric Anomalies Possibly Associated With the 2019 Ridgecrest (California) Earthquakes by GNSS, CSES, and Swarm Observations

AbstractThe association between earthquakes and ionospheric precursors has been widely reported in previous studies. The Swarm satellite constellation, the China Seismo‐Electromagnetic Satellite (CSES), and the ground‐based global navigation satellite system (GNSS) observations provide us new opportunities to detect ionospheric precursors to earthquakes for a better understanding of the seismic‐ionospheric coupling. In California, two shallow earthquakes with magnitudes of 6.5 and 7.1, the two higher magnitude events of the Ridgecrest earthquakes sequence, occurred on July 4 and July 6, 2019, respectively. This study adopted total electron content (TEC) data from the GNSS, and electron density (Ne), and electron temperature (Te) data from the CSES and Swarm to study the possible ionospheric perturbations before the Ridgecrest earthquakes. A sliding interquartile range method was used to analyze the TEC data, and a spatial differential method and a revisit trajectory comparison were used to analyze the Ne and Te data. While excluding the influences of various factors including solar and geomagnetic activities, planetary waves, tropospheric convection, and medium‐scale traveling ionospheric disturbances, the cross‐validation of the multi‐source data found that anomalies detected on June 20, June 22, and June 26–29, 2019 could be regarded as precursors of the Ridgecrest earthquakes. A spatial distribution analysis showed that the pre‐earthquake anomalies offset the equatorial direction. TEC anomalies were all positively correlated with the Ne anomalies, with most of them were positive. The Ne and Te anomalies were both positively and negatively correlated with mostly negative, while showing a strong linear negative correlation at nighttime.

Extreme Positive Ionosphere Storm Triggered by a Minor Magnetic Storm in Deep Solar Minimum Revealed by FORMOSAT‐7/COSMIC‐2 and GNSS Observations

AbstractThis study examines an unexpected and extreme positive ionospheric response to a minor magnetic storm on August 5, 2019 by using global ionosphere specification (GIS) 3D electron density profiles obtained by assimilating radio occultation total electron content (TEC) measurements of the recently launched FORMOSAT‐7/COSMIC‐2 satellites, and ground‐based global navigation satellite system (GNSS) TEC. The results reveal ∼300% enhancement of equatorial ionization anomaly (EIA) crests, appearing over 200–300 km altitudes, and a much intense localized density enhancement over the European sector. These are the most intense ionospheric response that has ever been detected for a small magnetic storm with Dst ∼ −53 nT (SYM‐H ∼ −64 nT). The enhancements are validated by using global ionosphere map (GIM) TEC and ground‐based GNSS TEC. The GIS vertical electron density structures during the storm are examined to understand the physical processes giving rise to such an intense ionosphere response during deep solar minimum conditions when the background electron density is very low. Altitude variations and poleward shifts of the locations of the EIA crests indicate that prompt penetration electric fields (PPEF) play an important role in producing the observed positive storm responses, with the storm‐induced equatorward circulation supporting the plasma accumulation against recombination losses. Additional physical mechanisms are required to fully explain the unexpected electron density enhancements for this minor storm event.

Validating HY-2A CMR precipitable water vapor using ground-based and shipborne GNSS observations

Abstract. The calibration microwave radiometer (CMR) on board the Haiyang-2A (HY-2A) satellite provides wet tropospheric delay correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. The ground-based global navigation satellite system (GNSS) provides precise precipitable water vapor (PWV) with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 International GNSS Service (IGS) stations along the global coastline and 56 d shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made in the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm as the root mean square (rms) within 100 km. Geographically, the rms is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an rms of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well.

Empirical Model for Total Precipitable Water Retrieval from Ground-based GNSS Observations in Thailand

Retrieval of Total Precipitable Water (TPW) using ground-based Global Navigation Satellite System (GNSS) observations is a challenging task due to its real‐time and high temporal resolution. In this paper, we present a method for establishing an analytic model for retrieving the total precipitable water (TPW) based on Global Navigation Satellite System (GNSS) observations over one-year period from 12 distributed stations across Thailand. The derived zenith total delay (ZTD) at all stations agrees well with the TPW data available from Global Data Assimilation System (GDAS) Numerical Weather Prediction (NWP) model. At first, a unique relationship between the ZTD and the TPW was established by taking into account of the variation of station altitudes. In addition, the bias correction technique using probability distribution function (PDF) matching was also applied to improve the final model. The inversion model of TPW from ZTD is then easily obtained using a numerical technique. The performance of our method has been successfully evaluated on an independent test data. This model can be useful in the further near real-time TPW measurements from widely available GNSS receivers.

Precipitable Water Vapor Retrieval from Shipborne GNSS Observations on the Korean Research Vessel ISABU

We estimate precipitable water vapor (PWV) from data collected by the low-cost Global Navigation Satellite System (GNSS) receiver at a vessel. The dual-frequency GNSS receiver that the vessel ISABU is equipped with that is operated by the Korea Institute of Ocean Science and Technology. The ISABU served in the Pacific Ocean for scientific research during a period from August 30 to September 21, 2018. It also performs radiosonde observations to obtain a vertical profile of troposphere on the vessel’s path. The GNSS-derived PWV is compared to radiosonde observations and the Atmospheric Infrared Sounder (AIRS) on NASA’s Aqua satellite output. A bias and root-mean-square (RMS) error between shipborne GNSS-PWV and radiosonde-PWV were −1.48 and 5.22 mm, respectively. When compared to the ground GNSS-PWV, shipborne GNSS-PWV has a relatively large RMS error in comparison with radiosonde-PWV. However, the GNSS observations on the vessel are still in good agreement with radiosonde observations. On the other hand, the GNSS-PWV is not well linearly correlated with AIRS-PWV. The RMS error between the two observations was approximately 8.97 mm. In addition, we showed that the vessel on the sea surface has significantly larger carrier phase multipath error compared to the ground-based GNSS observations. This also can result in reducing the accuracy of shipborne GNSS-PWV. However, we suggest that the shipborne GNSS has sufficient potential to derive PWV with the kinematic precise point positioning (PPP) solution on the vessel.

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.

Preliminary result of capturing the signature of heavy rainfall events using the 2-d-/4-d water vapour information derived from GNSS measurement in Hong Kong

Apart from the well-known applications like positioning, navigation and timing (PNT), Global Navigation Satellite System (GNSS) has manifested its ability in many other areas that are vital to society largely. With the dense setting of the regional continuously operating reference station (CORS) networks, monitoring the variations in atmospheric water vapour using a GNSS technique has become a focus in the field of GNSS meteorology. Most previous studies mainly concentrate on the analysis of relationship between the two-dimensional (2-d) Precipitable Water Vapour (PWV) and rainfall while the three-dimensional (3-d) variations of atmospheric water vapour derived from the GNSS tomographic technique are more rarely discussed. In this work, we investigate changes in the emerging field of GNSS technology for monitoring changes in 2-d/3-d atmospheric water vapour during rainfall period. The signature of atmospheric water vapour variation was captured using the ground-based GNSS observations from the CORS network of Hong Kong which presents the particularity of being dense. In addition, the corresponding rain gauges and radiosonde stations are also used. Our analysis of the 2-d PWV/3-d water vapour profiles changes during the arrival, occurrence and depression of heavy rainfall shows that: (i) PWV increases before the arrival of heavy rainfall (ii) and decreases to its average value after the depression of rainfall; (iii) rainfall leads to an anomalous variation in relative humidity and temperature while their trends are totally opposite; (iv) atmospheric water vapour presents unstable conditions with intense vertical convective and horizontal convergence motions and hydrometeors are formed before the arrival of rainfall. This study indicates the potential of using GNSS technique to monitor spatio-temporal variations of atmospheric water vapour during rainfall period, which provides a better understanding of the mechanism of convection and rainfall induced by the extreme weather events.

Integrated Precise Orbit Determination of Multi-GNSS and Large LEO Constellations

Global navigation satellite system (GNSS) orbits are traditionally determined by observation data of ground stations, which usually need even global distribution to ensure adequate observation geometry strength. However, good tracking geometry cannot be achieved for all GNSS satellites due to many factors, such as limited ground stations and special stationary characteristics for the geostationary Earth orbit (GEO) satellites in the BeiDou constellation. Fortunately, the onboard observations from low earth orbiters (LEO) can be an important supplement to overcome the weakness in tracking geometry. In this contribution, we perform large LEO constellation-augmented multi-GNSS precise orbit determination (POD) based on simulated GNSS observations. Six LEO constellations with different satellites numbers, orbit types, and altitudes, as well as global and regional ground networks, are designed to assess the influence of different tracking configurations on the integrated POD. Then, onboard and ground-based GNSS observations are simulated, without regard to the observations between LEO satellites and ground stations. The results show that compared with ground-based POD, a remarkable accuracy improvement of over 70% can be observed for all GNSS satellites when the entire LEO constellation is introduced. Particularly, BDS GEO satellites can obtain centimeter-level orbits, with the largest accuracy improvement being 98%. Compared with the 60-LEO and 66-LEO schemes, the 96-LEO scheme yields an improvement in orbit accuracy of about 1 cm for GEO satellites and 1 mm for other satellites because of the increase of LEO satellites, but leading to a steep rise in the computational time. In terms of the orbital types, the sun-synchronous-orbiting constellation can yield a better tracking geometry for GNSS satellites and a stronger augmentation than the polar-orbiting constellation. As for the LEO altitude, there are almost no large-orbit accuracy differences among the 600, 1000, and 1400 km schemes except for BDS GEO satellites. Furthermore, the GNSS orbit is found to have less dependence on ground stations when incorporating a large number of LEO. The orbit accuracy of the integrated POD with 8 global stations is almost comparable to the result of integrated POD with a denser global network of 65 stations. In addition, we also present an analysis concerning the integrated POD with a partial LEO constellation. The result demonstrates that introducing part of a LEO constellation can be an effective way to balance the conflict between the orbit accuracy and computational efficiency.

Establishment of a Real-Time Local Tropospheric Fusion Model

The tropospheric delay is one major error source affecting the precise positioning provided by the global navigation satellite system (GNSS). This error occurs because the GNSS signals are refracted while travelling through the troposphere layer. Nowadays, various types of model can produce the tropospheric delay. Among them, the globally distributed GNSS permanent stations can resolve the tropospheric delay with the highest accuracy and the best continuity. Meteorological models, such as the Saastamoinen model, provide formulae to calculate temperature, pressure, water vapor pressure and subsequently the tropospheric delay. Some grid-based empirical tropospheric delay models directly provide tropospheric parameters at a global scale and in real time without any auxiliary information. However, the spatial resolution of the GNSS tropospheric delay is not sufficient, and the accuracy of the meteorological and empirical models is relatively poor. With the rapid development of satellite navigation systems around the globe, the demand for real-time high-precision GNSS positioning services has been growing dramatically, requiring real-time and high-accuracy troposphere models as a critical prerequisite. Therefore, this paper proposes a multi-source real-time local tropospheric delay model that uses polynomial fitting of ground-based GNSS observations, meteorological data, and empirical GPT2w models. The results show that the accuracy in the zenith tropospheric delay (ZTD) of the proposed tropospheric delay model has been verified with a RMS (root mean square) of 1.48 cm in active troposphere conditions, and 1.45 cm in stable troposphere conditions, which is significantly better than the conventional tropospheric GPT2w and Saastamoinen models.

Ionospheric Scintillation Observation Using Space‐Borne Synthetic Aperture Radar Data

AbstractNetworks of ground‐based global navigation satellite system (GNSS) receivers have been widely used to monitor scintillation caused by irregularities in the disturbed ionosphere. Due to the relative sparseness of such networks, however, scintillation measurements are lacking in many regions of the globe, and even in well‐instrumented geographic areas, the spacing between receivers is often too large to study the systematic spatial changes in scintillation characteristics, for example, across the equatorial anomaly region. This paper discusses the potential of studying ionospheric scintillations using low‐frequency synthetic aperture radar (SAR). It compares standard metrics of scintillation including the amplitude scintillation index S4 and vertically integrated strength of turbulence CkL, from GNSS and SAR, on two different dates with varying ionospheric conditions. For this study, polarimetric L‐band SAR images acquired from the Phased Array‐type L‐band Synthetic Aperture Radar sensor onboard the Advanced Land Observational Satellite‐2 have been used. A number of GNSS satellites also observed the particular scintillation event that was encountered by SAR on the night of 23 March 2015 over the southern and mid‐central India. The S4 index derived from SAR are computed using previously published techniques in terms of radar backscatter (σ°) enhancement and the image contrast. The results show a favorable correlation with the GNSS observations. Along with accurate information about satellite geometry and operating frequency, few spectral properties of ionospheric irregularities, such as spectral index, anisotropy, and outer scale, have been assumed from historically available low‐latitude scintillation observations to calculate the turbulence strength parameter. The results are well corroborated by measurements from four GNSS stations in India, thus demonstrating the utility of the SAR measurements in augmenting and complementing the ionospheric scintillation diagnostics available from GNSS.

Estimating trends in atmospheric water vapor and temperature time series over Germany

Abstract. Ground-based GNSS (Global Navigation Satellite System) has efficiently been used since the 1990s as a meteorological observing system. Recently scientists have used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data and meteorological measurements. We aim to evaluate climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: (1) estimated from ground-based GNSS observations using the method of precise point positioning, (2) inferred from ERA-Interim reanalysis data, and (3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods: the first applies least squares to deseasonalized time series and the second uses the Theil–Sen estimator. The trends estimated at 113 GNSS sites, with 10 to 19 years temporal coverage, vary between −1.5 and 2.3 mm decade−1 with standard deviations below 0.25 mm decade−1. These results were validated by estimating the trends from ERA-Interim data over the same time windows, which show similar values. These values of the trend depend on the length and the variations of the time series. Therefore, to give a mean value of the PWV trend over Germany, we estimated the trends using ERA-Interim spanning from 1991 to 2016 (26 years) at 227 synoptic stations over Germany. The ERA-Interim data show positive PWV trends of 0.33 ± 0.06 mm decade−1 with standard errors below 0.03 mm decade−1. The increment in PWV varies between 4.5 and 6.5 % per degree Celsius rise in temperature, which is comparable to the theoretical rate of the Clausius–Clapeyron equation.

Ground-based GNSS network and integrated water vapor mapping during the development of severe storms at the Cuyo region (Argentina)

Mendoza is a province of Argentina located between 32° S and 34° S at the leeside of the Andes Foothills. Very intense thunderstorms form between October and March (southern hemisphere summer), which produce large hail and damage in crops and properties. Although some hypotheses and conceptual models were proposed in order to identify key possible mechanisms that contribute to trigger convection, they are still waiting for the validation process. As moisture is the main ingredient for storms formation, the identification of its geographical distribution could be used together with other synoptic and mesoscale forcing features to forecast intense convective events. A novel technique in estimating moisture concentration and its geographical distribution has been introduced in order to observe the influx and variability of humidity at this region, during a 45-day period in midsummer. In doing so, we resort to the information provided by the ground-basedGlobal Navigation Satellite System (GNSS) network. More than 300 active stations constitute the continuously operating GNSS network over Southern and Central America (SIRGAS-CON, Sistema de Referencia Geocéntrico para las Américas de Operación Continua). This network allows to retrieve integrated water vapor (IWV) content, mapping this variable by the use of a digital model of terrain. In the period and region under study, a prevailing influx of humidity from N and NE and a high correlation between the accumulation/depletion of humidity and the hail/no hail precipitation days is observed. We discuss in particular the development of five storms detected by the S-Band radar network belonging to the Province of Mendoza. Although the results strongly suggest that IWV maps are capable to represent the humidity dynamics in the considered region, it is still important to highlight that the calculated values for IWV are unrealistic at some locations as the consequence of deep atmospheric gradients. These biases may be explained by the fact that the GNSS observations are made over the whole horizon of each given site.

The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations

Abstract. Within the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) there is a need for an assessment of the uncertainty in the integrated water vapour (IWV) in the atmosphere estimated from ground-based global navigation satellite system (GNSS) observations. All relevant error sources in GNSS-derived IWV are therefore essential to be investigated. We present two approaches, a statistical and a theoretical analysis, for the assessment of the uncertainty of the IWV. The method is valuable for all applications of GNSS IWV data in atmospheric research and weather forecast. It will be implemented to the GNSS IWV data stream for GRUAN in order to assign a specific uncertainty to each data point. In addition, specific recommendations are made to GRUAN on hardware, software, and data processing practices to minimise the IWV uncertainty. By combining the uncertainties associated with the input variables in the estimations of the IWV, we calculated the IWV uncertainties for several GRUAN sites with different weather conditions. The results show a similar relative importance of all uncertainty contributions where the uncertainties in the zenith total delay (ZTD) dominate the error budget of the IWV, contributing over 75 % of the total IWV uncertainty. The impact of the uncertainty associated with the conversion factor between the IWV and the zenith wet delay (ZWD) is proportional to the amount of water vapour and increases slightly for moist weather conditions. The GRUAN GNSS IWV uncertainty data will provide a quantified confidence to be used for the validation of other measurement techniques.

Assessment of vertical TEC mapping functions for space-based GNSS observations

The mapping function is commonly used to convert slant to vertical total electron content (TEC) based on the assumption that the ionospheric electrons concentrate in a layer. The height of the layer is called ionospheric effective height (IEH) or shell height. The mapping function and IEH are generally well understood for ground-based global navigation satellite system (GNSS) observations, but they are rarely studied for the low earth orbit (LEO) satellite-based TEC conversion. This study is to examine the applicability of three mapping functions for LEO-based GNSS observations. Two IEH calculating methods, namely the centroid method based on the definition of the centroid and the integral method based on one half of the total integral, are discussed. It is found that the IEHs increase linearly with the orbit altitudes ranging from 400 to 1400 km. Model simulations are used to compare the vertical TEC converted by these mapping functions and the vertical TEC directly calculated by the model. Our results illustrate that the F&K (Foelsche and Kirchengast) geometric mapping function together with the IEH from the centroid method is more suitable for the LEO-based TEC conversion, though the thin layer model along with the IEH of the integral method is more appropriate for the ground-based vertical TEC retrieval.

A feasibility study of the radio occultation electron density retrieval aided by a global ionospheric data assimilation model

Radio Occultation (RO) has been proven a powerful technique on profiling ionospheric electron density profile. The currently used Abel inversion in RO electron density profile retrieval has degraded performance in regions with large horizontal gradients because of an assumption of spherical symmetry as indicated by many studies. Due to the success of COSMIC/FORMOSAT‐3, the Joint U.S.‐Taiwan Executive Steering Committee has decided to move forward with a follow‐on RO mission (called COSMIC‐2/FORMOSAT‐7), which implies that the number of RO observations will increase rapidly in the near future. It is thus worthwhile continuing retrieval method improvements. In this study, a global ionospheric data assimilation model is constructed. A series of simulation tasks are conducted to study the feasibility of RO electron density profile retrieval aided by a global ionospheric data assimilation model based on the planned COSMIC‐2/FORMOSAT‐7 orbits and existing GPS, GLONASS and ground‐based Global Navigation Satellite System (GNSS) stations configuration. We find that the data assimilation aided retrieval can significantly improve upon the Abel inversion and offers an optimal electron density profile retrieval approach if sufficient RO and ground GNSS observations are available. The retrieval error is affected by the data quantity, ionospheric variability, and accuracy of the data assimilation background. It is found that ∼700 globally distributed occultations per hour are sufficient to get better performance than the Abel inversion with the resolution of 2.5° in latitude, 5° in longitude, and 20 km in altitude in the data assimilation model.

Sensing the Earth using Global Navigation Satellite System signals

International Workshop on GNSS Remote Sensing for Future Missions and Sciences; Shanghai, China, 7–9 August 2011 The Global Navigation Satellite System (GNSS) has been widely used in navigation, positioning, and geoscience applications. Recently, the versatility of GNSS as a new remote sensing tool has been demonstrated with the use of refracted, reflected, and scattered GNSS signals to sound the atmosphere and ionosphere, ocean, land surfaces (including soil moisture), and cryosphere. Existing GPS radio occultation (RO) missions—e.g., the U.S.‐Argentina SAC‐C, German Challenging Minisatellite Payload (CHAMP), U.S.‐Germany Gravity Recovery and Climate Experiment (GRACE), Taiwan‐U.S. Formosa Satellite Mission‐3/Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT‐3/COSMIC) satellites, German TerraSAR‐X satellite, and European MetOp—together with groundbased GNSS observations, have provided precise and high‐resolution information on tropospheric water vapor, pressure, temperature, tropopause parameters, ionospheric total electron content, and electron density profiles. GNSS signals reflected from the ocean and land surface can determine the ocean height, ocean surface wind speed and wind direction, soil moisture, and ice and snow thickness. With improvement expected due to the next generation of multifrequency GNSS systems and receivers, and new space‐based instruments tracking GNSS reflected and refracted signals, new scientific applications of GNSS are expected in the near future across a number of environmental remote sensing fields.