Ground-based Global Navigation Satellite System Research Articles

A Case Study of Ionospheric Storm‐Time Altitudinal Differences at Low Latitudes During the May 2021 Geomagnetic Storm

AbstractPrevious studies paid little attention to the ionospheric storm‐time altitudinal differences due to insufficiency of ionospheric measurements. In this work, multiple instrumental observations were used to investigate the ionospheric storm‐time response at low latitudes in the American and Asian‐Australian sectors during the May 2021 geomagnetic storm. The ground‐based Global Navigation Satellite Systems (GNSS) total electron content (TEC) and Low Earth Orbit (LEO) satellite topside TEC presented opposite (positive/negative) variations in the low‐latitude and equatorial region of both sectors during this storm. The electron density profiles from the Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (COSMIC‐2) and the Sanya Incoherent Scatter Radar showed a good agreement and well explained the opposite variations between GNSS TEC and LEO satellite topside TEC. The F2‐layer peak height (hmF2) and peak density (NmF2) displayed inverse variations, and the feature was present mostly in the regions between equatorial ionization anomaly crests. The combined modulation effects of the storm‐time zonal electric fields and the field‐aligned transports possibly resulted in the contrary variations of hmF2 and NmF2 in the low‐latitude and equatorial region, leading to the storm‐time altitudinal differences during this storm. Relatively, the storm‐time thermospheric composition disturbances might be a minor factor responsible for these differences.

GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering

AbstractThe Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is a near‐real‐time data assimilation model of the high latitude ionosphere, incorporating measurements from many instruments, including slant Total Electron Content measurements from ground‐based Global Navigation Satellite System (GNSS) receivers. These measurements have receiver‐specific Differential Code Biases (DCB) which must be resolved to produce an absolute measurement, which are resolved simultaneously with the ionospheric state using Rao‐Blackwellized particle filtering. These DCBs are compared to published values and to DCBs determined using eight different Global Ionospheric Maps (GIM), which show small but consistent systematic differences. The potential cause of these systematic biases is investigated using multiple experimental A‐CHAIM test runs, including the effect of plasmaspheric electron content. By running tests using the GIM‐derived DCBs, it is shown that using A‐CHAIM DCBs produces the lowest overall error, and that using GIM DCBs causes an overestimation of the topside electron density which can exceed 100% when compared to in situ measurements from DMSP.

2‐D Total Electron Content and 3‐D Ionospheric Electron Density Variations During the 14 October 2023 Annular Solar Eclipse

AbstractThis study investigates the ionospheric total electron content (TEC) responses in the 2‐D spatial domain and electron density variations in the 3‐D spatial domain during the annular solar eclipse on 14 October 2023, using ground‐based Global Navigation Satellite System (GNSS) observations, a novel TEC‐based ionospheric data assimilation system (TIDAS), ionosonde measurements, and satellite in situ data. The main results are summarized as follows: (a) The 2‐D TEC responses exhibited distinct latitudinal differences. The mid‐latitude ionosphere exhibited a more substantial TEC decrease of 25%–40% along with an extended recovery time of 3–4 hr. In contrast, the equatorial and low‐latitude ionosphere experienced a smaller TEC reduction of 10%–25% and a faster recovery time of 20–50 min. The minimal eclipse effect was observed near the northern equatorial ionization anomaly crest region. (b) The ionospheric electron density variations during the eclipse were effectively reconstructed by TIDAS data assimilation in the 3‐D domain, providing important altitude information with validity. (c) The ionospheric electron density variations showed a notable altitude‐dependent feature. The eclipse led to a substantial electron density reduction of 30%–50%, with the maximum depletion occurring around the ionospheric F2‐layer peak height (hmF2) of 250–350 km. The post‐eclipse recovery of electron density exhibited a relatively slower pace near the F2‐layer peak height than that at lower and higher altitudes.

3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA)

AbstractThis study has developed a new TEC‐based ionospheric data assimilation system for 3‐D regional ionospheric imaging over the South American sector (TIDAS‐SA) (45°S–15°N, 35°–85°W, and 100–800 km). The TIDAS‐SA data assimilation system utilizes a hybrid Ensemble‐Variational approach to incorporate a diverse set of ionospheric data sources, including dense ground‐based Global Navigation Satellite System (GNSS) line‐of‐sight Total Electron Content (TEC) data, radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (COSMIC‐2), and altimeter TEC data from the JASON‐3 satellite. TIDAS‐SA can produce a reanalyzed three‐dimensional (3‐D) electron density spatial variation with a high time cadence, yielding spatial‐temporal resolution of 1° (latitude) × 1° (longitude) × 20 km (altitude) × 5 min. This allows us to reconstruct and study the 3‐D ionospheric morphology with multi‐scale structures. The performance of the data assimilation system is validated against independent ionosonde and in situ measurements through an experiment for a strong geomagnetic storm event on 03–04 November 2021. The results demonstrate that TIDAS‐SA can provide detailed and altitude‐resolved information that accurately characterizes the storm‐time ionospheric disturbances in vertical and horizontal domains over the equatorial and low‐latitude regions of South America.

Ionospheric response to the 2020 Samos earthquake and tsunami

On 30 October 2020 at 11:51 UT, a magnitude 7.0 earthquake occurred in the Dodecanese sea (37.84°N, 26.81°E, 10 km depth) and generated a tsunami with an observed run-up of more than 1 m on the Turkish coasts. Both the earthquake and the tsunami produced acoustic and gravity waves that propagated upward, triggering co-seismic and co-tsunamic ionospheric disturbances. This paper presents a multi-instrumental study of the ionospheric impact of the earthquake and related tsunami based on ionosonde data, ground-based Global Navigation Satellite Systems (GNSS) data and data from DORIS beacons received by Jason3 in the Mediterranean region. Our study focuses on the Total Electron Content to describe the propagation of co-seismic and co-tsunami ionospheric disturbances (CSID, CTID), possibly related to gravity waves triggered by the earthquake and tsunami. We use simultaneous vertical ionosonde soundings to study the interactions between the upper and lower atmosphere, highlighting the detection of acoustic waves generated by the seismic Rayleigh waves reaching the ionosonde locations and propagating vertically up to the ionosphere. The results of this study provide a detailed picture of the Lithosphere-Atmosphere–Ionosphere coupling in the scarcely investigated Mediterranean region and for a relatively weak earthquake.Graphical abstract

Performance of the double-thin-shell approach for studying nighttime medium-scale traveling ionospheric disturbances using two dense GNSS observation networks in Japan

Electrodynamic coupling between the ionospheric E and F regions is widely recognized as the underlying mechanism for generating medium-scale traveling ionospheric disturbances (MSTIDs) during nighttime at midlatitudes. Recently, the double-thin-shell approach has proven to be a useful tool for studying the E–F coupling. By using total electron content (TEC) measurements, this approach enables the simultaneous reconstruction of electron density perturbations in both the E and F regions with broad and continuous coverage. However, the current reconstruction performance is limited when using only GPS-TEC measurements from GEONET, a dense network of ground-based Global Navigation Satellite System (GNSS) receivers over Japan. The expansion of available data sources and the integration of multi-GNSS observation data are considered important to enhance the double-thin-shell model. Fortunately, SoftBank Corp., a Japanese telecommunications provider, has recently developed a dense independent GNSS observation network to improve positioning services. In this paper, we analyze the potential of the improved double-thin-shell approach and emphasize the importance of incorporating multi-GNSS observation data from both GEONET and SoftBank networks. The solvability analysis, simulation, and observation results collectively indicate a substantial improvement in the spatiotemporal resolution. Specifically, the longitudinal and latitudinal resolution is improved from 0.15° to 0.1° in the E region, and from 0.5° to 0.3° in the F region. The temporal resolution is also improved from 2 to 1 min. In addition, significant improvements have been achieved in the reconstruction performance, particularly for the E region under complex background conditions. Based on these assessments, we conclude that the incorporation of GEONET and SoftBank GNSS observation data holds significant potential for improving the double-thin-shell model and advancing our understanding of MSTIDs.Graphical abstract

Statistical models of the variability of plasma in the topside ionosphere: 2. Performance assessment

Statistical models of the variability of plasma in the topside ionosphere based on the Swarm data have been developed in the “Swarm Variability of Ionospheric Plasma” (Swarm-VIP) project within the European Space Agency’s Swarm+4D-Ionosphere framework. The models can predict the electron density, its gradients for three horizontal spatial scales – 20, 50 and 100 km – along the North-South direction and the level of the density fluctuations. Despite being developed by leveraging on Swarm data, the models provide predictions that are independent of these data, having a global coverage, fed by various parameters and proxies of the helio-geophysical conditions. Those features make the Swarm-VIP models useful for various purposes, which include the possible support for already available ionospheric models and proxy of the effect of ionospheric irregularities of the medium scales that affect the signals emitted by Global Navigation Satellite Systems (GNSS). The formulation, optimisation and validation of the Swarm-VIP models are reported in Paper 1 (Wood et al. 2024. J Space Weather Space Clim. in press). This paper describes the performance assessment of the models, by addressing their capability to reproduce the known climatological variability of the modelled quantities, and the ionospheric weather as depicted by ground-based GNSS, as a proxy for the ionospheric effect on GNSS signals. Additionally, we demonstrate that, under certain conditions, the model can better reproduce the ionospheric variability than a physics-based model, namely the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM).

Double resonance in seismo-lithosphere-atmosphere-ionosphere coupling

nvestigations into causal mechanisms behind anomalous pre-earthquake phenomena are considered a promising way of earthquake prediction. Numerous promising channels for seismo-lithosphere-atmosphere-ionosphere coupling have been proposed; however, predicting earthquakes remains a great challenge in the scientific society. Short-period ground vibrations exhibiting frequency characteristics similar to natural frequencies caused by strata failure resonance have recently been detected using tiltmeters embedded in magnetometers prior to earthquakes. These vibrations originate from regions near the epicentres of forthcoming earthquakes and can be simultaneously detected by broadband seismometers and ground-based global navigation satellite system (GNSS) receivers. Unlike the total electron contents (TECs) obtained from orbiting satellites, the vibrations and the identifiable TEC perturbations in data from geostationary satellites of the BeiDou Navigation System share frequencies prior to earthquakes. However, the causal relationship between the vibrations and TEC perturbations remains unclear due to a gap in data observations between the lithosphere and ionosphere. To address this issue, an instrumental array was established to monitor vibrations and perturbations in the lithosphere, atmosphere, and ionosphere. Observational data from the array partially fill the gap, and analytical results show that ground vibrations, air pressure, magnetic fields, and TEC data shared a common frequency of approximately 5 × 10–3 Hz (5 mHz) before major earthquakes. This suggests that the resonant ground vibrations trigger atmospheric resonance before earthquakes. Therefore, the double resonance (crustal and atmospheric resonance) model is a new explanation for the observed anomalies in multiple geophysical parameters in the lithosphere, atmosphere, and ionosphere. Retrieving resonant signals from multiple sources of observational data is a significant challenge, but once this issue is overcome, double resonance may contribute to practical earthquake prediction.

Monitoring urban heat island intensity based on GNSS tomography technique

Monitoring urban heat island (UHI) effect is critical because it causes health problems and excessive energy consumption more energy when cooling buildings. In this study, we propose an approach for UHI monitoring by fusing data from ground-based global navigation satellite system (GNSS), space-based GNSS radio occultation (RO), and radiosonde. The idea of the approach is as follows: First, the first and second grid tops are defined based on historical RO and radiosonde observations. Next, the wet refractivities between the first and second grid tops are fitted to higher-order spherical harmonics and they are used as the inputs of GNSS tomography. Then, the temperature and water vapor partial pressure are estimated by using best search method based on the tomography-derived wet refractivity. In the end, the UHI intensity is evaluated by calculating the temperature difference between the urban regions and nearby rural regions. Feasibility of the UHI intensity monitoring approach was evaluated with GNSS RO and radiosonde data in 2010–2019, as well as ground-based GNSS data in 2020 in Hong Kong, China, by taking synoptic temperature data as reference. The result shows that the proposed approach achieved an accuracy of 1.2 K at a 95% confidence level.

Co-seismic characterization analysis in PWV and land-atmospheric observations associated with Luding Ms 6.8 earthquake occurrence in China on September 5, 2022

The Sichuan Luding earthquake that struck on September 5, 2022 is one of the strongest earthquakes in China in recent years. The analysis of precipitable water vapor (PWV) retrieved from the ground-based global navigation satellite system (GNSS), surface pressure (SP), surface latent heat flux (SLHF), and land surface temperature (LST) from the reanalysis dataset was carried out in the epicenter and the nearby areas. The results show that PWV decreases distinctly and reaches the trough at the outburst with significant minimums of 43.21 mm and 37.84 mm over the nearest SCSM and SCTQ station from the epicenter. SLHF also has the same trend, and SP increased. Additionally, the LST analysis from two-temporal series was conducted to reveal that the Luding event accompanies by a low-temperature anomaly. Based on the background field established from the same period of the last ten years, LST at the epicenter on the day of occurrence was 5.68 °C lower than in previous years. Furthermore, the strongest low-temperature anomalies were observed from September 4 to 6, with the anomaly index of −1.95, −1.71, and −1.60, respectively. It is plain that the parameters from the land and atmosphere perform the anomalies at the minimum during the Luding earthquake.

Assessing the Effects of a Minor CIR‐HSS Geomagnetic Storm on the Brazilian Low‐Latitude Ionosphere: Ground and Space‐Based Observations

AbstractThis paper investigates the effects of a minor G1 Co‐rotating Interaction Region (CIR)/High‐Speed Stream (HSS)‐driven geomagnetic storm that occurred on (13–14 October 2018), during deep solar minimum. We used simultaneous observations from multiple instruments, namely; ground‐based Global Navigation Satellite Systems (GNSS) receivers, a Digisonde, ground magnetometers, and space‐based observations from the National Aeronautics and Space Administration Global‐scale Observations of Limb and Disk (GOLD) and SWARM missions. This study presents a detailed picture of the low‐latitude ionosphere response over the Brazilian sector during a minor storm. Our results showed that the minor CIR/HSS‐driven storm caused a positive ionospheric storm of over ∼20 TECU in magnitude. For the first time, periodic post‐sunset irregularities and Equatorial Plasma Bubbles, equatorial plasma bubbles, were analyzed using GOLD FUV OI 135.6 nm emission, Total Electron Content (TEC) maps, Rate of TEC index, ROTI, and TEC gradients. Fluctuations in the interplanetary magnetic field Bz and changes in the thermospheric column density ratio (∑O/N2) are discussed as the main sources of ionospheric changes during the storm. This paper highlights the importance of monitoring and understanding the impact of Sun‐Earth interactions and provides insight into the behavior of the low‐latitude ionosphere during minor geomagnetic storms.

The 6 February 2023 Türkiye Earthquake Sequence as Detected in the Ionosphere

AbstractOn 6 February 2023, a series of large earthquakes struck Turkey and Northern Syria. The main earthquake of Mw 7.8 occurred at 01:17:34 UTC and was followed by the three notable (Mw > 5.5) aftershocks within the next 18 min. Then, ∼9 hr later, the biggest aftershock with magnitude Mw 7.5 and a Mw 6.0 earthquake occurred to the north‐east from the first main earthquake. In this work, we use data of ground‐based Global Navigation Satellite Systems (GNSS) receivers in Turkey, Israel and Cyprus to analyze the ionospheric response to this series of earthquakes. We separate these events in two groups: the first sequence of earthquakes (at 01–02 UTC) and the second sequence (at 10–11 UTC). For the first sequence, we observe a clear N‐shaped total electron content (TEC) response after the Mw 7.8 mainshock earthquake and Mw 6.7 aftershock, and a smaller TEC disturbance that is, most likely, caused by the Mw 5.6 earthquake. The latter is now the smallest earthquake detected by using ionospheric GNSS data. The co‐seismic ionospheric disturbances (CSID) propagated from the epicentral area in the south‐west direction with velocities of about 750–830 m/s. For the second sequence, we observed the response to the Mw 7.5 aftershock earthquake and the Mw 6.0 aftershock. The CSID propagated both to the south‐west and the north‐west to the epicentral area, with velocities of about 950–1,100 m/s.

Establishing a high‐precision real‐time Precipitable Water Vapor model in China with Global Navigation Satellite System and Fifth‐Generation Reanalysis Model data

AbstractThe real‐time high precision of Precipitable Water Vapor (PWV) is widely recognized as crucial for advancing numerical weather prediction and enhancing our understanding of climate change. PWV is usually measured with radiosondes, microwave radiometers, and meteorological satellites. Nevertheless, these instruments have limitations including low temporal or spatial resolutions, high cost and weather dependence. To address these issues, we developed a real‐time PWV grid model for China by integrating the ground‐based Global Navigation Satellite System (GNSS) network with ERA5 reanalysis products. In this study, we explored an alternative method using ERA5 products to construct an accurate Elevation Normalization Factor Model (ENFM) and Tm models. The culmination of our efforts resulted in the creation of a real‐time 1° × 1° PWV grid model for China. To validate the models of Tm and PWV, we compared them against the data obtained from radiosondes in China throughout 2021. The results indicate that the root‐mean‐square error (RMSE) value of the deviation between the Tm derived from our new Tm model, excluding meteorological parameters, and the radiosonde‐derived Tm, is better than 4.22 K. The Tm values of the new Tm model are improved by 15.31% compared to those estimated based on the Bevis model plus HGPT2 temperature model. The RMSE values of the deviations between the new grid PWV and the radiosonde‐derived PWV are less than 3.45 mm. The precision of our new PWV grid model is improved by 37.2% compared to that of the traditional Askne and Nordius model.

Prospects for meteotsunami detection in earth’s atmosphere using GNSS observations

We study, for the first time, the physical coupling and detectability of meteotsunamis in the earth’s atmosphere. We study the June 13, 2013 event off the US East Coast using Global Navigation Satellite System (GNSS) radio occultation (RO) measurements, Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperatures, and ground-based GNSS ionospheric total electron content (TEC) observations. Hypothesizing that meteotsunamis also generate gravity waves (GWs), similar to tsunamigenic earthquakes, we use linear GW theory to trace their dynamic coupling in the atmosphere by comparing theory with observations. We find that RO data exhibit distinct stratospheric GW activity at near-field that is captured by SABER data in the mesosphere with increased vertical wavelength. Ground-based GNSS-TEC data also detect a far-field ionospheric response 9 h later, as expected by GW theory. We conclude that RO measurements could increase understanding of meteotsunamis and how they couple with the earth’s atmosphere, augmenting ground-based GNSS TEC observations.

Validation of precipitable water vapor estimates from an inexpensive infrared thermometer

Water vapor is a fundamental component of the Earth's atmosphere with a high spatial and temporal variability. This work studies to what extent low-cost infrared thermometers can infer precipitable water (variable commonly used to characterize atmospheric water vapor). In a calibration process, infrared thermometer readings recorded at Badajoz (Spain) during the 2015–2018 period are compared against precipitable water data measured with reference ground-based Global Navigation Satellite Systems (GNSS) in order to obtain conversion factors through regression analyses considering two exponential fits. After this calibration, using the equation of the best fit, thermometer readings for the year 2019 are transformed into precipitable water estimates. A validation analysis in which these estimates are compared with GNSS measurements yields rms differences of 19% and 17% when normal and seasonal calibration had been employed, respectively. These results are similar (or even better) to those obtained with satellite data. In addition, we explore if certain factors, such as solar elevation, precipitable water content, precipitable water measurements used as reference and equations to convert temperature readings into precipitable water estimates, can significantly affect the quality of the estimates. In view of our results, low-cost infrared thermometers could be used to create an extensive and dense network for a better characterization of the spatial and temporal variability of water vapor.

Predicting Eastern Mediterranean Flash Floods Using Support Vector Machines with Precipitable Water Vapor, Pressure, and Lightning Data

Flash floods in the Eastern Mediterranean (EM) region are considered among the most destructive natural hazards, which pose a significant challenge to model due to their high complexity. Machine learning (ML) methods have made a significant contribution to the advancement of flash flood prediction systems by providing cost-effective solutions with improved performance, enabling the modeling of the complex mathematical expressions underlying physical processes of flash floods. Thus, the development of ML methods for flash flood prediction holds the potential to mitigate risks, inform policy recommendations, minimize loss of human life, and reduce property damage caused by flash floods. Here, we present a novel approach for improving flash flood predictions in the EM region using Support Vector Machines (SVMs) with a combination of precipitable water vapor (PWV) data, derived from ground-based global navigation satellite system (GNSS) receivers, along with surface pressure measurements, and nearby lightning occurrence data to predict flash floods in an arid region of the EM. The SVM model was trained on historical data from 2004 to 2019 and was used to forecast the likelihood of flash floods in the region. The study found that integrating nearby lightning data with the other variables significantly improved the accuracy of flash flood prediction compared to using only PWV and surface pressure measurements. The results of the SVM model were validated using observed flash flood events, and the model was found to have a high predictive accuracy with an area under the receiver operating characteristic curve of 0.93 for the test set. The study provides valuable insights into the potential of utilizing a combination of meteorological and lightning data for improving flash flood forecasting in the Eastern Mediterranean region.

Monitoring Seismo-TEC Perturbations Utilizing the Beidou Geostationary Satellites

Electromagnetic signals transmitted from the Beidou geostationary satellites can be utilized to monitor changes in ionospheric total electron contents (TECs) at motionless ionospheric pierce points (IPPs) over the Earth’s surface 24 h a day. The TEC perturbations at close IPPs detected via distinct horizontal azimuths and elevation angles can be examined by utilizing different measuring geometries formed by the selected geostationary satellites and ground-based Global Navigation Satellite System (GNSS) stations. The M6.9 Menyuan earthquake occurred in northwest China on 7 January 2022. We collected TEC perturbations associated with the Menyuan earthquake at those motionless IPPs to examine the capability of the TEC measurements utilizing distinct horizontal azimuths and elevation angles. The experimental results show that the TEC perturbations associated with the earthquake traveled away from the area around the epicenter with velocities of ~800 m/s and ~1000 m/s in the ionosphere. The traveling TEC perturbations were consistently observed in different geometries. Such novel results show that the pronounced TEC perturbations can be obtained once the satellite hanging high over the Earth’s surface in front of the traveling TEC perturbations is selected. This study shows that geostationary satellites provide an excellent opportunity to conduct experiments on the advantage of the TEC observation technology.

Comprehensive analysis of the ionospheric response to the largest geomagnetic storms from solar cycle 24 over Europe

A multi-instrumental analysis of the meridional ionospheric response is presented over Europe during the two largest ICME-driven geomagnetic storms of solar cycle #24 maximum. Data from 5 European digisonde stations, ground-based Global Navigation Satellite System, Total Electron Content (GNSS TEC), the ratio of the TEC difference (rTEC), as well as Swarm and Thermosphere, Ionosphere, Mesosphere, Energetics and Dynamics (TIMED) satellite observations have been used for the investigation of selected intervals (11–17 November, 2012, and 16–25 March, 2015). The storm evolution is monitored by digisonde foF2 critical frequency (related to the maximum electron density of F2-layer) and GNSS TEC data. Moreover, Global Ultraviolet Imager (GUVI) measurements from the TIMED satellite are used to investigate the changes in the thermospheric O/N2 ratio. Our main focus was on the main phase of the geomagnetic storms, when during the nighttime hours extremely depleted plasma was detected. The extreme depletion is observed in foF2, TEC and rTEC, which is found to be directly connected to the equatorward motion of the midlatitude ionospheric trough (MIT) on the nightside. We demonstrate a method (beside the existing ones) which allows the monitoring of the storm-time evolution of the disturbances (e.g., MIT, SAPS, SED) in the thermosphere-ionosphere-plasmasphere system by the combined analysis of the worldwide digisonde system data (with the drift measurements and the ionospheric layer parameters with 5–15 min cadence), with rTEC and GNSS TEC data, and with the satellite data like Swarm, TIMED/GUVI.

Fog in Sofia 2010–2019: Objective Circulation Classification and Fog Indices

Low visibility caused by fog events can lead to disruption of every type of public transportation, and even loss of life. The focus of this study is the synoptic conditions associated with fog formation. The data used in this study was collected over the course of ten years (2010–2019) in Sofia, Bulgaria. The forecast skills of the Fog Stability Index (FSI) and the local Sofia Stability Index (SSI), as well as the relation between the Integrated Water Vapor (IWV) and fog from the Global Navigation Satellite System (GNSS), were tested. Both fog indices are used for fog nowcasting as their lead times are short and unclear. The Jenkinson–Collison Type method was used for extracting the predominant synoptic-scale pressure systems which provide suitable weather conditions for fog formation. Surface observations from two synoptic stations were used to calculate and evaluate the performance of the two fog indices and of the ground-based GNSS receiver for the IWV. The forecast skills provided by Probability of Detection (POD) and False Alarm Ratio (FAR), for both fog and no-fog periods, were obtained by discriminant analysis. Additionally, several weather parameters, such as surface wind speed, relative humidity and IWV, were added in order to improve the results of the local index (SSI). This led to a 77.9% hit rate. The cyclonic system influence and zonal flows from the west and the southwest are both responsible for a number of fog cases that are comparable to those associated with the anticyclonic system. The IWV was not found to improve the forecast skill of the fog indices. However, it was found that its values had a larger spread during no-fog periods in comparison to fog periods.

Approximate maximum likelihood time-delay estimation for two closely spaced sources

The study of ground reflections of Global Navigation Satellite System (GNSS) signals, as in GNSS Reflectometry (GNSS-R) can lead to the receiver height estimation. The latter is estimated by comparing the time of arrival difference between the direct and reflected signals, also called path separation. In ground-based scenarios, this path separation can be very small, inducing important interference between paths, which makes it difficult to correctly obtain altimetry products. The path separation estimation can be obtained by a brute force dual source maximum likelihood estimator (2S-MLE), but this solution has a large computational cost. On the other hand, the path separation is so small that a number of approximations can be done. In this study, a third order Taylor approximation of the dual source likelihood criterion is proposed to reduce its complexity. The proposed algorithm performance is compared to the non approximated 2S-MLE for the estimation of the path separation, and to a standard single source processing for the estimation of the direct signal time-delay. These results, along with the corresponding lower bounds, prove that the proposed approach may be of interest for two applications: ground-based GNSS-R altimetry (or radar with low elevation targets) and GNSS multipath mitigation.