Ground-based Global Navigation Satellite System Research Articles

Nighttime Ionospheric TEC Study Over Latin America During Moderate and High Solar Activity

AbstractThe present work is a comprehensive study of the ionospheric vertical total electron content (vTEC) variations during the nighttime, based on data collected by ground‐based Global Navigation Satellite System (GNSS) receivers over the Latin American region. We provide a qualitative and quantitative analysis of the ionospheric vTEC trend at 21:00, 00:00, and 03:00 local time (LT), during geomagnetically undisturbed days of 2011 (ascending phase) and 2014 (maximum phase), which encompassed (a) the response to the solar flux variation, (b) the seasonal trend in different latitudes and longitudes, and (c) the interhemispheric asymmetry. One significant result of this study is the development of TEC maps for the Latin American region, which are used for the monitoring and forecasting of the ionosphere for space weather purposes. The nighttime vTEC variations showed a strong latitudinal dependence, especially in the Northern Hemisphere. For 2011, the semiannual anomaly was similar to that observed in daytime; however, in 2014, the receivers at midlatitude presented asymmetric behavior. Similarly, the nighttime winter anomaly (NWA) was very weak in both years. The Equatorial Ionospheric Anomaly (EIA) signature was absent from June to August, a period in which the hemispheric disparity in the vTEC values became more evident, suggesting a feeble interhemispheric circulation. The Midlatitude Summer Nighttime Anomaly (MSNA) was also identified in the Southern Hemisphere, during January and February of 2011 (moderate solar activity). Model approximations suggest that the equatorward winds and the EIA were involved in the formation of the MSNA.

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.

Assimilation of Ionosphere Observations in the Whole Atmosphere Community Climate Model with Thermosphere‐Ionosphere EXtension (WACCMX)

AbstractA set of Observing System Simulation Experiments (OSSEs) are performed to assess the impact of assimilating Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) electron density profiles and ground‐based Global Navigation Satellite System (GNSS) total electron content (TEC) observations in a whole atmosphere data assimilation system. The OSSEs are performed using the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCMX) with data assimilation provided by the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. Results from the OSSEs demonstrate that the assimilation of ionosphere observations improves the short‐term (1 hr) forecasts and analyses. The OSSEs show that the short‐term forecasts and analyses are further improved when the ionosphere observations adjust the thermosphere neutral composition and temperature in addition to the ionosphere electron density. Based on an initialized forecast experiment, we find that adjusting the thermosphere neutral composition and temperature also leads to improved forecast skill in the ionosphere on longer time scales (i.e., beyond 1 hr). Additionally, it is shown that using a 1 hr data assimilation cycle, and removal of second‐order divergence damping in WACCMX+DART significantly improves tidal amplitudes, which were previously found to be too small. These initial results represent the first whole atmosphere data assimilation system with capabilities to assimilate observations from the troposphere to the ionosphere‐thermosphere.

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.

SNR-based GNSS reflectometry for coastal sea-level altimetry: results from the first IAG inter-comparison campaign

Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is quickly maturing toward the objective of becoming a viable alternative for operational coastal sea-level (SL) altimetry in a geocentric reference frame. SL has immense societal implications related to climate change. Of particular interest is the exploitation of existing coastal GNSS sites for reflectometry by means of signal-to-noise ratio (SNR) observables. We report results from the first inter-comparison campaign on SNR-based GNSS-R. The goal was to cross-validate retrieval solutions from independent research groups under comparable conditions. This action was an initiative of the International Association of Geodesy working group 4.3.9 (2015–2019 term). Data collected at the Onsala Space Observatory for a 1-year period (2015–2016) were compared to a co-located tide gauge (TG). SNR data for the GPS L1-C/A signal were processed by four groups, in Sweden, Luxembourg/Brazil, Germany, and the UK. Semidiurnal tidal constituents showed good agreement between TG and all GNSS-R groups. SL variations at diurnal and longer periods were also well captured by all series. Most GNSS-R solutions exhibited spurious tones at integer fractions of one sidereal day, the satellite revisit time of the particular GNSS constellation employed (GPS). Band-pass filtering between 3 h and 30 h confirmed that the dominant tidal components were well captured by most GNSS-R solutions. Higher-frequency SL variations (periods < 3 h) are poorly represented by GNSS-R as a consequence of its low temporal resolution. The solution with the worst agreement neglects a correction associated with the rate of change in sea level and uses narrower satellite elevation ranges per retrieval. Overall, there was excellent agreement, with correlation coefficients exceeding 0.9 and RMSE smaller than 5 cm.

BalkanMed real time severe weather service: Progress and prospects in Bulgaria

The main objective of the “BalkanMed real time severe weather service” (BeRTISS, 2017–2019) project is to establish a pilot transnational severe weather service by exploiting Global Navigation Satellite Systems (GNSS) tropospheric products to enhance the safety, the quality of life and environmental protection in the Balkan-Mediterranean region. In Bulgaria, severe weather events, like intense precipitation, hail and thunderstorms, are common in the summer months and are associated with large economic losses for example in agriculture. The Bulgarian Hail Suppression Agency and the Sofia University are partners in the BeRTISS project with final aim to develop the Bulgarian Integrated NowCAsting tool (BINCA). BINCA uses data from the recently deployed ground-based GNSS network of 12 stations in Bulgaria. The GNSS data is delivered to Sofia University GNSS Analysis Center (SUGAC) and processed in near real time. To derive GNSS Integrated Water Vapour from GNSS the Weather Research and Forecast (WRF) model is computed on daily basis. The GNSS IWV, weather radar as well as surface atmospheric observations and WRF model simulations covering Bulgaria are integrated in the BINCA web platform. The platform is publicly accessible and facilitates the operational tasks in hail suppression in Bulgaria but also other operational and public services. This manuscripts presents the BeRTISS service elements and their implementation in Bulgaria.

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.

Improved model of ionosphere variability and study for long-term statistical characteristics

Ionospheric variability is influenced by many factors, such as solar radiation, neutral atmosphere composition, and geomagnetic disturbances. Mainly characterized by the total electron content (TEC) and electron density, the climatology of the ionosphere features temporal and spatial changes. Establishing a multivariant regression model helps substantially in better understanding the ionosphere characteristics and their long-term variability. In this paper, an improvement of the existing ionosphere multivariate linear fitting regression model is proposed and investigated using data from both the ionosonde and the global ionosphere map (GIM) derived from ground-based Global Navigation Satellite System (GNSS) observations. The proposed method gives more consideration to the impact of the solar activity and adds modeling of the annual periodic fluctuations and half-year periodic fluctuations for the F10.7 index. The improved model is verified to have a better correlation with the real observations and can help reduce the calculation uncertainty. Moreover, the proposed model is used to evaluate the fitting accuracy of the GIMs produced by five authorized data analysis centers from the International GNSS Service (IGS). The results show that there is a fixing hole in the North America region for the GIM model where the correlation between the GIM and the proposed model always returns lower values compared to other places.

Exploiting the Potential of Coastal GNSS-R for Improving Storm Surge Modeling

The potential mymargin for improving storm surge simulation is demonstrated by using winds derived from ground-based Global Navigation Satellite System Reflectometry (GNSS-R) that uses BeiDou geostationary Earth orbit (GEO) satellite signals. We reconstruct wind fields by blending GNSS-R coastal winds with the European Center for Median Weather Forecasts (ECMWF) reanalysis product. The reconstructed winds agree well with the weather station data collected at Yangjiang in Guangdong, China. The ECMWF winds and the reconstructed winds are used to force a storm surge model off the Chinese coast during typhoon Utor 2013, respectively. The model storm surges forced by the reconstructed winds agree substantially better with tide-gauge observations than those forced by the ECMWF winds. The average error has been reduced by 30.5% from 24.3 cm with the ECMWF winds to 16.9 cm with the reconstructed winds. This letter suggests that GNSS-R coastal winds can have a positive impact on the accuracy of storm surge hindcasting directly and forecasting indirectly by improving the initial conditions.

Evaluation of Precipitable Water Vapor from Five Reanalysis Products with Ground-Based GNSS Observations

At present, the global reliability and accuracy of Precipitable Water Vapor (PWV) from different reanalysis products have not been comprehensively evaluated. In this study, PWV values derived by 268 Global Navigation Satellite Systems (GNSS) stations around the world covering the period from 2016 to 2018 are used to evaluate the accuracies of PWV values from five reanalysis products. The temporal and spatial evolution is not taken into account in this analysis, although the temporal and spatial evolution of atmospheric flows is one of the most important information elements available in numerical weather prediction products. The evaluation results present that five reanalysis products with PWV accuracy from high to low are in the order of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), ERA-Interim, Japanese 55-year Reanalysis (JRA-55), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), and NCEP/DOE (Department of Energy) according to root mean square error (RMSE), bias and correlation coefficient. The ERA5 has the smallest RMSE value of 1.84 mm, while NCEP/NCAR and NCEP/DOE have bigger RMSE values of 3.34 mm and 3.51 mm, respectively. The findings demonstrate that ERA5 and two NCEP reanalysis products have the best and worst performance, respectively, among five reanalysis products. The differences in the accuracy of the five reanalysis products are mainly attributed to the differences in the spatial resolution of reanalysis products. There are some large absolute biases greater than 4 mm between GNSS PWV values and the PWV values of five reanalysis products in the southwest of South America and western China due to the limit of terrains and fewer observations. The accuracies of five reanalysis products are compared in different climatic zones. The results indicate that the absolute accuracies of five reanalysis products are highest in the polar regions and lowest in the tropics. Furthermore, the effects of different seasons on the accuracies of five reanalysis products are also analyzed, which indicates that RMSE values of five reanalysis products in summer and in winter are the largest and the smallest in the temperate regions. Evaluation results from five reanalysis products can help us to learn more about the advantages and disadvantages of the five released water vapor products and promote their applications.

Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach

Snow cover plays a vital role in the climate system because it is related to climate, hydrological cycle, and ecosystem. On this basis, deriving a long-term and large-scale snow depth (SD) time series and monitoring its temporal and spatial variations are crucial. Passive microwave remote sensing data in combination with in-situ SD data have long been used to retrieve SD. However, the retrieval accuracy is limited in case of sparse meteorological stations, and the high-quality applications of retrieval results are hindered in specific areas. The ground-based global navigation satellite system reflectometry (GNSS-R) method is currently a potential way to monitor SD variations with a high degree of accuracy but has a limited spatial coverage. In this study, a deep learning-based approach, which displays a stronger nonlinear expressiveness capability than conventional neural networks, was applied to estimate SD by combining satellite observations, in-situ data, and GNSS-R estimates. The model was trained and tested with data obtained in Alaska between 2008 and 2017. Results show that the proposed deep belief network model performs better than linear methods and conventional neural network models and demonstrate the effectiveness of combining GNSS-R estimation with increased cross-validation R of 0.85 and decreased RMSE of 15.40 cm. The predicted SD distribution indicates that the variations in mean SD in Alaska for March and April between 2008 and 2017 were associated with the climate anomalies and air temperature. Overall, the proposed deep learning-based method is a promising approach in the satellite-retrieved SD field.

Impact of the variational assimilation of ground-based GNSS zenith total delay into AROME-Morocco model

The impact of assimilating zenith total delay (ZTD) observations from the Moroccan ground-based Global Navigation Satellite System (GNSS) network into the high-resolution operational model AROME-Morocco (2.5 km) is assessed over one month. The objective is to investigate the impact on moisture field and rainy event forecasts in a three-dimensional variational (3D-Var) data assimilation framework with a 3-hour cycling. As a first step, a pre-processing of ZTD observations is performed for quality control and bias correction and it points out that all GNSS stations available in the network can be potentially assimilated. Then, two parallel experiments, with and without assimilation of GNSS ZTD, are performed during February–March 2018, for 48-hour lead times. Compared against other observation systems of humidity (radiosonde and surface network), a small and beneficial improvement is found in the atmospheric moisture short-range forecast, despite the limited number of GNSS stations over Morocco. The impact of assimilating GNSS ZTD data on precipitation forecasts is evaluated both subjectively and objectively. The objective precipitation scores against daily rain gauge observations show that the impact is mixed, positive for larger rainfall accumulations and neutral to negative for smaller rainfall accumulations. A specific evaluation for a case study of a rain event highlights an improvement in terms of intensity and location of precipitating areas when GNSS ZTD observations are assimilated.

Multi-Instrumental Observations of Early Morning Equatorial Plasma Depletions During the 2017 Memorial Weekend Storm

We investigate an equatorial plasma depletion (EPD) event in Japan near the early morning sector (04:30–7:30 LT) during the recovery phase of a geomagnetic storm occurred on Memorial weekend (May 28) 2017 by using multi-instrumental measurements, including ground-based Global Navigation Satellite Systems (GNSS) network, ionosonde stations, and space-based Swarm and Defense Meteorological Satellite Program (DMSP) missions. The detailed 2-D evolution of the early morning EPD is well observed using GNSS ROTI maps with high temporal-spatial resolution. Moreover, ROTI data from BeiDou Geostationary Earth Orbit satellites are used to detect the EPD signature for the first time. The dawn EPD signature was also observed at the topside ionosphere by in situ electron density (Ne) measurements and ROTI results by space-borne Swarm Alpha (A), Charlie (C), and DMSP F17 satellites, indicating that the equatorial depletions arose to higher altitudes. We suggest that both the eastward disturbance dynamo electric field and overshielding electric field near the early morning sector created a favorable condition for the growth of the Rayleigh–Taylor (R-T) instability, and thus result in the generation of the dawn EPD.

Multi-scale ionosphere responses to the May 2017 magnetic storm over the Asian sector

We investigate multi-scale ionospheric responses to the May 27, 2017, geomagnetic storm over the Asian sector by using multi-instrumental observations, including ground-based global navigation satellite systems (GNSS) network, constellation observing system for meteorology, ionosphere and climate radio occultation, the FengYun-3C (FY-3C) GNSS occultation sounder electron density profiles and in situ plasma density observations provided by both Swarm and defense meteorological satellite program missions. This geomagnetic storm was an intense storm with the minimum symmetric horizontal component reaching − 150 nT and was caused by a coronal mass ejection released on May 23. The main observations are summarized below: (1) two ionospheric positive storm periods were observed. The first one was observed in the noon–afternoon sector during the main phase of the storm on May 28, with nearly 120% TEC enhancement. The second one was of a smaller scale and occurred on the nightside during the recovery phase of the storm on May 29. The first dayside positive storm was initiated by the interplanetary magnetic field (IMF) Bz southward turning and eastward penetration electric field, while the second nightside one was terminated by a later southward turning of the IMF Bz since the Asian sector was on the nightside and the penetration electric field changed westward. (2) A negative storm occurred from 00:00 to 12:00 UT on May 30 over the Asian sector, nearly 2 days after the main phase, which was due to the thermospheric composition change, i.e., decrease in the O/N2 ratio, as shown in the TIMED/GUVI measurements. (3) A band-like TEC enhancement was observed aligning in the northwest–southeast direction and propagated slowly southwestward from 15:00 to 20:00 UT (23:00–04:00 LT, near midnight) on May 28 during the recovery phase of the storm. In situ density observations from the Swarm B and DMSP F15&16 satellites confirmed the density enhancement at 510 km and 850 km, respectively, and revealed that this band-like TEC enhancement structure resembles the so-called plasma blob. The similarities of the observed plasma blob characteristics in terms of spatial structure, propagation trend and temporal evolution with the nighttime traveling ionospheric disturbance (TID) are consistent with the TID-blob theory.

Monitoring subway construction using Sentinel-1 data: a case study in Bucharest, Romania

ABSTRACTIn Bucharest, new subway tunnels are under construction since 2011. The whole project of extending Bucharest underground infrastructure is due to be finished by 2030. The M-5 artery has been under construction since 2011 and ongoing. In December 2015, dewatering procedures in one of the metro stations caused serious damage to the surface infrastructure above, which subsided and partially collapsed in some locations. This paper relies on multi-temporal interferometry techniques to characterize displacement patterns of the ground surface above M-5 beltway between 2014 and 2018, and presents the capacity of Sentinel-1 satellite imagery to detect potential hazards in urban environments. Two stacks of Synthetic Aperture Radar (SAR) images acquired between October 2014 and October 2018 by the Sentinel-1 A and B satellites on ascending and descending orbits have been processed using the Delft Persistent Scatterer Interferometry algorithm (DePSI). The results indicate atypical acceleration in velocities starting in November 2015, prior to the described events, and manifesting until April 2016, when the ground was completely stabilized. The displacement trends and velocity values in the area of the Eroilor underground station were successfully validated using the ground-based Global Navigation Satellite Systems (GNSS) technique.

GNSS Ionosphere Sounding of Equatorial Plasma Bubbles

Ground- and space-based Global Navigation Satellite System (GNSS) receivers can provide three-dimensional (3D) information about the occurrence of equatorial plasma bubbles (EPBs). For this study, we selected March 2014 data (during solar maximum of cycle 24) for the analysis. The timing and the latitudinal dependence of the EPBs occurrence rate are derived by means of the rate of the total electron content (TEC) index (ROTI) data from GNSS receivers in China, whereas vertical profiles of the scintillation index S4 are provided by COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate). The GNSS receivers of the low Earth orbit satellites give information about the occurrence of amplitude scintillations in limb sounding geometry where the focus is on magnetic latitudes from 20° S to 20° N. The occurrence rates of the observed EPB-induced scintillations are generally smaller than those of the EPB-induced ROTI variations. The timing and the latitude dependence of the EPBs occurrence rate agree between the ground-based and spaceborne GNSS data. We find that EPBs occur at 19:00 LT and they are mainly situated above the F2 peak layer which descended from 450 km at 20:00 LT to 300 km at 24:00 LT in the equatorial ionosphere. At the same time, the spaceborne GNSS data also show, for the first time, a high occurrence rate of post-sunset scintillations at 100 km altitude, indicating the coexistence of equatorial sporadic E with EPBs.

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 atmospheric weighted mean temperature model in the polar regions

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