Ground-based Global Navigation Satellite System Research Articles

Precise water level measurements using low-cost GNSS antenna arrays

Abstract. We have developed a ground-based Global Navigation Satellite System Reflectometry (GNSS-R) technique for monitoring water levels with a comparable precision to standard tide gauges (e.g. pressure transducers) but at a fraction of the cost and using commercial products that are straightforward to assemble. As opposed to using geodetic-standard antennas that have been used in previous GNSS-R literature, we use multiple co-located low-cost antennas to retrieve water levels via inverse modelling of signal-to-noise ratio data. The low-cost antennas are advantageous over geodetic-standard antennas not only because they are much less expensive (even when using multiple antennas in the same location) but also because they can be used for GNSS-R analysis over a greater range of satellite elevation angles. We validate our technique using arrays of four antennas at three test sites with variable tidal forcing and co-located operational tide gauges. The root mean square error between the GNSS-R and tide gauge measurements ranges from 0.69–1.16 cm when using all four antennas at each site. We find that using four antennas instead of a single antenna improves the precision by 30 %–50 % and preliminary analysis suggests that four appears to be the optimum number of co-located antennas. In order to obtain precise measurements, we find that it is important for the antennas to track GPS, GLONASS and Galileo satellites over a wide range of azimuth angles (at least 140∘) and elevation angles (at least 30∘). We also provide software for analysing low-cost GNSS data and obtaining GNSS-R water level measurements.

A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing

A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor (PWV) observed at 05 a.m. (Universal Time Coordinated: UTC) on 10 December 2019, was used to reconstruct three-dimensional (3D) water vapor density fields over the test area. The tomographic results (GNSS_T) show that the water vapor density above this area is mainly below 25 g/m3 and is concentrated between the first to the fourth layers. The vertical distribution conforms to the exponential characteristics, while the horizontal distribution shows a decreasing trend from southwest to northeast. In addition, the results of the 0.25° grid dataset generated by the Global Forecast System (GFS) of the National Center for Environmental Forecasting (NCEP) (GFS_L) were interpolated to the height of the tomographic grid, which is in good agreement with the tomographic results. GFS_L is larger than GNSS_T on the first floor at the surface, with an average deviation of 0.19 g/m3. In contrast, GFS_L from the second floor to the top of the model is smaller than GNSS_T, with the average deviations distributed between −0.08 and −0.15 g/m3.

Assessing the performance of a Northeast Asia Japan-centered 3-D ionosphere specification technique during the 2015 St. Patrick\u2019s day geomagnetic storm

This paper demonstrates and assesses the capability of the advanced three-dimensional (3-D) ionosphere tomography technique, during severe conditions. The study area is northeast Asia and quasi-Japan-centred. Reconstructions are based on total electron content data from a dense ground-based global navigation satellite system receiver network and parameters from operational ionosondes. We used observations from ionosondes, Swarm satellites and radio occultation (RO) to assess the 3-D picture. Specifically, we focus on St. Patrick’s day geomagnetic storm (17–19 March 2015), the most intense in solar cycle 24. During this event, the energy ingested into the ionosphere resulted in Dst and Kp and reaching values ~ − 223 nT and 8, respectively, and the region of interest, the East Asian sector, was characterized by a ~ 60% reduction in electron densities. Results show that the reconstructed densities follow the physical dynamics previously discussed in earlier publications about storm events. Moreover, even when ionosonde data were not available, the technique could still provide a consistent picture of the ionosphere vertical structure. Furthermore, analyses show that there is a profound agreement between the RO profiles/in-situ densities and the reconstructions. Therefore, the technique is a potential candidate for applications that are sensitive to ionospheric corrections.

First Comparison of Traveling Atmospheric Disturbances Observed in the Middle Thermosphere by Global‐Scale Observations of the Limb and Disk to Traveling Ionospheric Disturbances Seen in Ground‐Based Total Electron Content Observations

AbstractTraveling ionospheric disturbances (TIDs) and their neutral counterparts known as traveling atmospheric disturbances (TADs) are believed to play a role in communicating inputs to other locations in the fluid. While these two phenomena are believed to be connected, they may not have a one‐to‐one correspondence as the geomagnetic field influences the TID but has no direct impact on the TAD. The relative amplitudes of the perturbations seen in the ionosphere and atmosphere have been observed but rarely together. This study reports results from a 3‐day campaign to observe TIDs and TADs simultaneously over a broad latitudinal region over the eastern United States using a combination of Global‐scale Observations of the Limb and Disk (GOLD) and a distributed network of ground‐based Global Navigation Satellite System (GNSS) receivers. These results demonstrate that GOLD and the ground‐based total electron content (TEC) observations can see the atmospheric and ionospheric portions of a large‐scale traveling disturbance. The phase difference in the perturbations to the GOLD airglow brightness, O/N2 and thermospheric disk temperature are consistent with an atmospheric gravity wave moving through this region. The ionospheric signatures move at the same rate as those in the atmosphere, but their amplitudes do not have a simple correspondence to the amplitude of the signal seen in the atmosphere. This campaign demonstrates a proof‐of‐concept that this combination of observations is able to provide information on TIDs and TADs, including quantifying their impact on the temperature and chemical composition of the upper atmosphere.

Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC<sup>4</sup>A

Abstract. Ground-based Global Navigation Satellite System (GNSS) measurements from nearly 50 stations distributed over the Caribbean arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality integrated water vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the trade winds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD)-to-IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams International Airport (GAIA), Bridgetown, Barbados. A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2), where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a collocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth-generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS-derived IWV peaks. Two successive peaks are observed on 22 January and 23–24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organization for five representative GNSS stations across the Caribbean arc using visible satellite images. A statistically significant link was found between the cloud patterns and the local IWV observations from the GNSS sites as well as the larger-scale IWV patterns from the ECMWF ERA5 reanalysis. The reprocessed ZTD and IWV data sets from 49 GNSS stations used in this study are available from the French data and service centre for atmosphere (AERIS) (https://doi.org/10.25326/79; Bock, 2020b).

Modelling Precipitable Water Vapour (PWV) Over Nigeria from Ground-Based GNSS

Global Navigational Satellite System (GNSS) over the past and present time has shown a great potential in the retrieval of the distribution of water vapour in the atmosphere. Taking the advantage of the effect of the atmosphere on GNSS signal as they travel from the constellation of satellite to ground-based GNSS receivers such that information (water vapour content) about the atmosphere (mostly from the troposphere) can be derived is referred to as GNSS meteorology. This paper presents the spatiotemporal variability of Precipitable Water Vapour (PWV) retrieved from ground–based Global Navigation Satellite System (GNSS) stations over Nigeria for the years 2012 to 2013. In this paper, the GNSS data were processed using GAMIT (ver. 10.70). The GNSS PWV were grouped into daily and monthly averages; the variability of the daily and monthly GNSS PWV were compared and validated with the daily and monthly PWV from National Centre for Environmental Prediction (NCEP) and monthly Rainfall data for the study years respectively. The results revealed that the spatiotemporal variability of PWV across Nigeria is a function of geographic location and seasons. The result shows that there is temporal correlation between GNSS PWV, NCEP PWV and rainfall events. The research also affirms that GNSS PWV could be used to improve weather forecasting/monitoring as well as climate monitoring.

Statistical study of medium-scale traveling ionospheric disturbances in low-latitude ionosphere using an automatic algorithm

This study investigates the medium-scale traveling ionospheric disturbances (MSTIDs) statistically at the low-latitude equatorial ionization anomaly (EIA) region in the northern hemisphere. We apply the automatic detection algorithm including the three-dimensional fast Fourier transform (3-D FFT) and support vector machine (SVM) on total electron content (TEC) observations, derived from a network of ground-based global navigation satellite system (GNSS) receivers in Taiwan (14.5° N geomagnetic latitude; 32.5° inclination), to identify MSTID from other waves or irregularity features. The obtained results are analyzed statistically to examine the behavior of low-latitude MSTIDs. Statistical results indicate the following characteristics. First, the southward (equatorward) MSTIDs are observed almost every day during 0800–2100 LT in Spring and Winter. At midnight, southward MSTIDs are more discernible in Summer and majority of them are propagating from Japan to Taiwan. Second, northward (poleward) MSTIDs are more frequently detected during 1200–2100 LT in Spring and Summer with the secondary peak of occurrence between day of year (DOY) 100–140 during 0000–0300 LT. The characteristics of the MSTIDs are interpreted with additional observations from radio occultation (RO) soundings of FORMOSAT-3/COSMIC as well as modeled atmospheric waves from the high-resolution Whole Atmosphere Community Climate Model (WACCM) suggesting that the nighttime MSTIDs in Summer is likely connected to the atmospheric gravity waves (AGWs).

Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC<sup>4</sup>A

Abstract. In the framework of the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) campaign that took place in January and February 2020, integrated water vapour (IWV) contents were retrieved over the open tropical Atlantic Ocean using Global Navigation Satellite System (GNSS) data acquired from three research vessels (R/Vs): R/V Atalante, R/V Maria S. Merian and R/V Meteor. This paper describes the GNSS processing method and compares the GNSS IWV retrievals with IWV estimates from the European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), from the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared products and from terrestrial GNSS stations located along the tracks of the ships. The ship-borne GNSS IWV retrievals from R/V Atalante and R/V Meteor compare well with ERA5, with small biases (−1.62 kg m−2 for R/V Atalante and +0.65 kg m−2 for R/V Meteor) and a root mean square (rms) difference of about 2.3 kg m−2. The results for the R/V Maria S. Merian are found to be of poorer quality, with an rms difference of 6 kg m−2, which is very likely due to the location of the GNSS antenna on this R/V prone to multipath effects. The comparisons with ground-based GNSS data confirm these results. The comparisons of all three R/V IWV retrievals with MODIS infrared products show large rms differences of 5–7 kg m−2, reflecting the enhanced uncertainties in these satellite products in the tropics. These ship-borne IWV retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign, east of Barbados, Guyana and northern Brazil. Both the raw GNSS measurements and the IWV estimates are available through the AERIS data centre (https://en.aeris-data.fr/, last access: 20 September 2020). The digital object identifiers (DOIs) for R/V Atalante IWV and raw datasets are https://doi.org/10.25326/71 (Bosser et al., 2020a) and https://doi.org/10.25326/74 (Bosser et al., 2020d), respectively. The DOIs for the R/V Maria S. Merian IWV and raw datasets are https://doi.org/10.25326/72 (Bosser et al., 2020b) and https://doi.org/10.25326/75 (Bosser et al., 2020e), respectively. The DOIs for the R/V Meteor IWV and raw datasets are https://doi.org/10.25326/73 (Bosser et al., 2020c) and https://doi.org/10.25326/76 (Bosser et al., 2020f), respectively.

A New Method for Determining an Optimal Diurnal Threshold of GNSS Precipitable Water Vapor for Precipitation Forecasting

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the “threshold-based” models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King’s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.

Investigating GNSS multipath effects induced by co-located Radar Corner Reflectors

Abstract Radar Corner Reflectors (CR) are increasingly used as reference targets for land surface deformation measurements with the Interferometric Synthetic Aperture Radar (InSAR) technique. When co-located with ground-based Global Navigation Satellite Systems (GNSS) infrastructure, InSAR observations at CR can be used to integrate relative measurements of surface deformation into absolute reference frames defined by GNSS. However, CR are also a potential source of GNSS multipath effects and may therefore have a detrimental effect on the GNSS observations. In this study, we compare daily GNSS coordinate time series and 30-second signal-to-noise ratio (SNR) observations for periods before and after CR deployment at a GNSS site. We find that neither the site coordinates nor the SNR values are significantly affected by the CR deployment, with average changes being within 0.1 mm for site coordinates and within 1 % for SNR values. Furthermore, we generate empirical site models by spatially stacking GNSS observation residuals to visualise and compare the spatial pattern in the surroundings of GNSS sites. The resulting stacking maps indicate oscillating patterns at elevation angles above 60 degrees which can be attributed to the CR deployed at the analysed sites. The effect depends on the GNSS antenna used at a site with the magnitude of multipath patterns being around three times smaller for a high-quality choke ring antenna compared to a ground plane antenna without choke rings. In general, the CR-induced multipath is small compared to multipath effects at other GNSS sites located in a different environment (e. g. mounted on a building).

Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude

Global ionospheric total electron content (TEC) is generally derived with ground-based Global Navigation Satellite System (GNSS) observations based on mathematical models in a solar-geomagnetic reference frame. However, ground-based observations are not well-distributed. There is a lack of observations over sparsely populated areas and vast oceans, where the accuracy of TEC derivation is reduced. Additionally, the modified dip (modip) latitude is more suitable than geomagnetic latitude for the ionosphere. This paper investigates the improvement of global TEC with multi-source data and modip latitude, and a simulation with International Reference Ionosphere (IRI) model is developed. Compared with using ground-based observations in geomagnetic latitude, the mean improvement was about 10.88% after the addition of space-based observations and the adoption of modip latitude. Nevertheless, the data from JASON-2 satellite altimetry and COSMIC occultation are sparsely-sampled, which makes the IRI TEC a reasonable estimation for the areas without observation. By using multi-source data from ground-based, satellite-based and IRI-produced observations, global TEC was derived in both geomagnetic and modip latitudes for 12 days of four seasons in 2014 under geomagnetic quiet conditions. The average root-mean-square error (RMSE) of the fitting was reduced by 7.02% in modip latitude. The improvement was largest in March and smallest in June.

Ionosonde Observations of Spread F and Spread Es at Low and Middle Latitudes during the Recovery Phase of the 7–9 September 2017 Geomagnetic Storm

This study presents observations of nighttime spread F/ionospheric irregularities and spread Es at low and middle latitudes in the South East Asia longitude of China sectors during the recovery phase of the 7–9 September 2017 geomagnetic storm. In this study, multiple observations, including a chain of three ionosondes located about the longitude of 100°E, Swarm satellites, and Global Navigation Satellite System (GNSS) ROTI maps, were used to study the development process and evolution characteristics of the nighttime spread F/ionospheric irregularities at low and middle latitudes. Interestingly, spread F and intense spread Es were simultaneously observed by three ionosondes during the recovery phase. Moreover, associated ionospheric irregularities could be observed by Swarm satellites and ground-based GNSS ionospheric TEC. Nighttime spread F and spread Es at low and middle latitudes might be due to multiple off-vertical reflection echoes from the large-scale tilts in the bottom ionosphere. In addition, we found that the periods of the disturbance ionosphere are ~1 h at ZHY station, ~1.5 h at LSH station and ~1 h at PUR station, respectively. It suggested that the large-scale tilts in the bottom ionosphere might be produced by LSTIDs (Large scale Traveling Ionospheric Disturbances), which might be induced by the high-latitude energy inputs during the recovery phase of this storm. Furthermore, the associated ionospheric irregularities observed by satellites and ground-based GNSS receivers might be caused by the local electric field induced by LSTIDs.

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°.

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.

Data assimilation impact studies with the AROME-WMED reanalysis of the first special observation period of the Hydrological cycle in the Mediterranean Experiment

Abstract. This study was performed in the framework of HyMeX (Hydrological cycle in the Mediterranean Experiment), which aimed to study the heavy precipitation that regularly affects the Mediterranean area. A reanalysis with a convective-scale model AROME-WMED (Application of Research to Operations at MEsoscale western Mediterranean) was performed, which assimilated most of the available data for a 2-month period corresponding to the first special observation period of the field campaign (Fourrié et al., 2019). Among them, observations related to the low-level humidity flow were assimilated. Such observations are important for the description of the feeding of the convective mesoscale systems with humidity (Duffourg and Ducrocq, 2011; Bresson et al., 2012; Ricard et al., 2012). Among them there were a dense reprocessed network of high-quality Global Navigation Satellite System (GNSS) zenithal total delay (ZTD) observations, reprocessed data from wind profilers, lidar-derived vertical profiles of humidity (ground and airborne) and Spanish radar data. The aim of the paper is to assess the impact of the assimilation of these four observation types on the analyses and the forecasts from the 3 h forecast range (first guess) up to the 48 h forecast range. In order to assess this impact, several observing system experiments (OSEs) or so-called denial experiments, were carried out by removing one single data set from the observation data set assimilated in the reanalysis. Among the evaluated observations, it is found that the ground-based GNSS ZTD data set provides the largest impact on the analyses and the forecasts, as it represents an evenly spread and frequent data set providing information at each analysis time over the AROME-WMED domain. The impact of the reprocessing of GNSS ZTD data also improves the forecast quality, but this impact is not statistically significant. The assimilation of the Spanish radar data improves the 3 h precipitation forecast quality as well as the short-term (30 h) precipitation forecasts, but this impact remains located over Spain. Moreover, marginal impact from wind profilers was observed on wind background quality. No impacts have been found regarding lidar data, as they represent a very small data set, mainly located over the sea.

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error.

An Ionospheric Climate Index Based on GNSS

AbstractThe ionospheric total electron content (TEC) is a significant parameter for scientific studies of the ionosphere and space weather. Ground‐based Global Navigation Satellite System (GNSS) station network provides an opportunity for modeling the ionospheric TEC with high accuracy. Based on global ionospheric TEC maps, by using data from the International GNSS Service (IGS) stations for 21 years (from 1998 to 2018), we establish an ionospheric climate index (ICI) for representing the general state of the ionosphere. In addition, we build a multiple linear regression model for fitting ICI using the SSN, F10.7, Ap, and Dst indices and upper atmospheric wind field. We quantitatively analyze the contribution of these factors to ICI time series. ICI during the past six solar cycles, namely, from 1958 to 2018, are presented, which are assigned as the adjectives (cold, cool, mild, warm, hot, and sizzling) for ionospheric climatology studies. This new index is a very promising representative of day‐to‐day variations of middle‐ and low‐latitude ionosphere.

A Resilient Smartphone Positioning Approach by Tightly Integrating the Monocular Camera and GNSS Signals

The Global Navigation Satellite System (GNSS) signals are often blocked or interfered in complex geographical or electromagnetic environments, which may make GNSS receivers unable to provide satisfying navigation and positioning services. There have been many ground-based or space-based GNSS augmentation systems to improve the resilience of GNSS positioning, of which most of them rely on additional infrastructures. In this study, a smartphone-based tightly-coupled positioning method was developed using the images from a build-in monocular camera and GNSS signals. In this method, the feature points with the known coordinates are regarded as 'visual pseudolite' and the distance between the camera and the feature points was calculated according to the photogrammetry approaches and used to estimate the user positioning with GNSS signals. The experimental results showed the feasibility of the tightly-coupled positioning algorithm and reached the positioning accuracy of ±5.56 m (1?), which is significantly higher than that of GNSS-only and vision-only positioning solutions.

Determination of Epicenters before Earthquakes Utilizing Far Seismic and GNSS Data: Insights from Ground Vibrations

Broadband seismometers, ground-based Global Navigation Satellite Systems (GNSS), and magnetometers that were located within an epicentral distance of approximately 150 km consistently observed the novel anomalous behaviors of the common-mode ground vibrations approximately 5–10 days before the M6.6 Meinong earthquake in Taiwan. The common-mode ground vibrations with amplitudes near 0.1 m at frequencies ranging from 8 × 10−5 to 2 × 10−4 Hz were generated near the region close to the epicenter of the impending earthquake. The common-mode vibrations were consistently observed in seismic and GNSS data associated with five other earthquakes in four distinct areas. The results reveal that the common-mode vibrations could be a typical behavior before earthquakes. The causal mechanism of common-mode vibrations can be attributed to crustal resonance excitations before fault dislocations occur. Potential relationships with other pre-earthquake anomalies suggest that the common-mode vibrations could be ground motion before earthquakes, which was investigated for a significant length of time.

Trends of atmospheric water vapour in Switzerland from ground-based radiometry, FTIR and GNSS data

Abstract. Vertically integrated water vapour (IWV) is expected to increase globally in a warming climate. To determine whether IWV increases as expected on a regional scale, we present IWV trends in Switzerland from ground-based remote sensing techniques and reanalysis models, considering data for the time period 1995 to 2018. We estimate IWV trends from a ground-based microwave radiometer in Bern, from a Fourier transform infrared (FTIR) spectrometer at Jungfraujoch, from reanalysis data (ERA5 and MERRA-2) and from Swiss ground-based Global Navigation Satellite System (GNSS) stations. Using a straightforward trend method, we account for jumps in the GNSS data, which are highly sensitive to instrumental changes. We found that IWV generally increased by 2 % per decade to 5 % per decade, with deviating trends at some GNSS stations. Trends were significantly positive at 17 % of all GNSS stations, which often lie at higher altitudes (between 850 and 1650 m above sea level). Our results further show that IWV in Bern scales to air temperature as expected (except in winter), but the IWV–temperature relation based on reanalysis data in the whole of Switzerland is not clear everywhere. In addition to our positive IWV trends, we found that the radiometer in Bern agrees within 5 % with GNSS and reanalyses. At the Jungfraujoch high-altitude station, we found a mean difference of 0.26 mm (15 %) between the FTIR and coincident GNSS data, improving to 4 % after an antenna update in 2016. In general, we showed that ground-based GNSS data are highly valuable for climate monitoring, given that the data have been homogeneously reprocessed and that instrumental changes are accounted for. We found a response of IWV to rising temperature in Switzerland, which is relevant for projected changes in local cloud and precipitation processes.