Global Navigation Satellite System Stations Research Articles

Ionospheric climate index as a driving parameter for the NeQuick model

Ionospheric delay is one of the significant error sources for global navigation satellite system (GNSS) positioning. GNSSs broadcast the coefficients of the ionospheric model to correct ionospheric delay for single-frequency users. A modified three-dimensional model (NeQuick G) based on the NeQuick climatological model is adopted for Galileo users. The NeQuick G model uses the effective ionization level (Az) instead of the sunspot number as the driving parameter. In this study, we introduce the ionospheric climate index (ICI) as a new driving parameter for the NeQuick model. In comparison, the ICI-driven NeQuick model has a better performance than the Az-driven NeQuick G model at both low and high latitudes. In addition, only one GNSS station at low latitudes is required to calculate the ICI, which would save maintenance costs and improve the efficiency of updating the broadcast coefficients. This model has potential application value for future upgrades of Galileo’s ionospheric broadcast model.

Detection of Slow Slip Events Using Wavelet Analysis of GNSS Recordings

ABSTRACT At many places, tectonic tremor is observed in relation to slow slip and can be used as a proxy to study slow slip events of moderate magnitude in which surface deformation is hidden in Global Navigation Satellite System (GNSS) noise. However, in subduction zones in which no clear relationship between tremor and slow slip occurrence is observed, these methods cannot be applied, and we need other methods to be able to better detect and quantify slow slip. Wavelets methods such as the Discrete Wavelet Transform and the Maximal Overlap Discrete Wavelet Transform (MODWT) are mathematical tools for analyzing time series simultaneously in the time and the frequency domains by observing how weighted differences of a time series vary from one period to the next. In this article, we use wavelet methods to analyze GNSS time series and seismic recordings of slow slip events in Cascadia. We use detrended GNSS data, apply the MODWT transform, and stack the wavelet details over several nearby GNSS stations. As an independent check on the timing of slow slip events, we also compute the cumulative number of tremor in the vicinity of the GNSS stations, detrend this signal, and apply the MODWT transform. In both the time series, we can then see simultaneous waveforms where timing corresponds to the timing of slow slip events. We assume that there is a slow slip event whenever there is a positive peak followed by a negative peak in the wavelet signal. We verify that there is a good agreement between slow slip events detected with only GNSS data and slow slip events detected with only tremor data for northern Cascadia. The wavelet-based detection method effectively detects events of magnitude higher than 6, as determined by independent event catalogs (e.g., Michel et al., 2019). As a demonstration of using the wavelet analysis in a region without significant tremor, we also analyze GNSS data from New Zealand and detect slow slip events that are spatially and temporally close to those detected previously by other studies.

Comparative analysis of the correction effect of different environmental loading products on global GNSS coordinate time series

The global navigation satellite system (GNSS) coordinate time series is affected by the environmental loading. This study mainly selects the three agencies, namely, the School and Observatory of Earth Sciences (EOST) in France, the German Research Center for Geosciences (GFZ) in Germany, and the International Mass Loading Service (IMLS) in the United States, including 5 types of atmospheric loading (ATML) models, 7 types of hydrological loading (HYDL) models and 5 types of non-tidal oceanic loading (NTOL) models. The classification of these 17 environmental loading models was discussed, and the root mean square (RMS) reduction ratio of the GNSS coordinate time series after environmental loading corrections (ELCs) was used to evaluate the performance differences of various models. In order to correct the nonlinear deformation of the GNSS stations to the greatest extent, based on the above analysis, this study selects the optimal model combination of three environmental loadings as ECMWF_IB + MERRA2 + ECCO1, and then explores its influence on the periodic signals in the GNSS coordinate time series.

GNSS Storm Nowcasting Demonstrator for Bulgaria

Global Navigation Satellite System (GNSS) is an established atmospheric monitoring technique delivering water vapour data in near-real time with a latency of 90 min for operational Numerical Weather Prediction in Europe within the GNSS water vapour service (E-GVAP). The advancement of GNSS processing made the quality of real-time GNSS tropospheric products comparable to near-real-time solutions. In addition, they can be provided with a temporal resolution of 5 min and latency of 10 min, suitable for severe weather nowcasting. This paper exploits the added value of sub-hourly real-time GNSS tropospheric products for the nowcasting of convective storms in Bulgaria. A convective Storm Demonstrator (Storm Demo) is build using real-time GNSS tropospheric products and Instability Indices to derive site-specific threshold values in support of public weather and hail suppression services. The Storm Demo targets the development of service featuring GNSS products for two regions with hail suppression operations in Bulgaria, where thunderstorms and hail events occur between May and September, with a peak in July. The Storm Demo real-time Precise Point Positioning processing is conducted with the G-Nut software with a temporal resolution of 15 min for 12 ground-based GNSS stations in Bulgaria. Real-time data evaluation is done using reprocessed products and the achieved precision is below 9 mm, which is within the nowcasting requirements of the World Meteorologic Organisation. For the period May–September 2021, the seasonal classification function for thunderstorm nowcasting is computed and evaluated. The probability of thunderstorm detection is 83%, with a false alarm ration of 38%. The added value of the high temporal resolution of the GNSS tropospheric gradients is investigated for a storm case on 24–30 August 2021. Real-time tropospheric products and classification functions are integrated and updated in real-time on a publicly accessible geoportal.

Two-Decade GNSS Observation Processing and Analysis with the New IGS Repro3 Criteria: Implications for the Refinement of Velocity Field and Deformation Field in Continental China

Extensive observation collection, unified and rigorous data processing, and accurate construction of the station motion model are the three essential elements for the accuracy and reliability of the Global Navigation Satellite System (GNSS) velocity field. GNSS data reprocessing not only can weaken the influence of untrue nonlinear site signals caused by imperfect models but also can eliminate the displacement offset caused by frame transformation, solution strategy, and model change. Based on the new repro3 criteria of the International GNSS Service (IGS), we process rigorously GNSS observations of continental China from the period 2000 to 2020 to refine GNSS station secular velocities and analyze the present-day crustal deformation in continental China. The main contributions of this work included the followings. Firstly, the repro3 algorithm and model are used to uniformly and rigorously process the two-decade GNSS historical observations to obtain more reliable GNSS coordinate time series with mm-level precision. Combined with the historical records of major earthquakes in continental China, we build a GNSS time series model considering nonlinear factors (velocity, offset, period, co-seismic/post-seismic deformation) to extract GNSS horizontal velocity field whose root mean square (RMS) mean is 0.1 mm/a. Secondly, the GNSS horizontal grid velocity field in continental China is interpolated using the gpsgridder method (the minimum radius is set to 16, and the Poisson’s ratio is set to 0.5). Estimation and analysis of the crustal strain rate solution lead to the conclusion that the strain degree in West China (the high strain region is mainly located in the Qinghai Tibet Plateau and Tianshan Mountains) is much more intense than that in the east (the main strain rate is less than 5 nstrain/year). In addition, most strong earthquakes in the Chinese mainland occurred on active blocks and their boundary faults with large changes in the GNSS velocity field and strain field. Then, an improved K-means++ clustering analysis method is proposed to divide active blocks using GNSS horizontal velocity field. Furthermore, different relative motion models of different blocks are constructed using the block division results. Among them, the Eurasian block has the lowest accuracy (the RMS of residual velocity in the east and north directions are 5.60 and 9.65 mm/a, respectively), and the China block 7 has the highest accuracy (the RMS mean of relative velocity in the east and north directions are 2.60 and 2.65 mm/a, respectively). More observations (2260+ sites), longer time (20 years), and updated criteria (Repro3) are to finely obtain the GNSS velocity field in continental China, and depict crustal deformation and active block with the gpsgridder and improved K-means++ methods.

Annual, seasonal, monthly & diurnal IPWV analysis and precipitation forecasting over the Indian subcontinent based on monthly thresholds of ground-based GNSS-IPWV

In this study, diurnal, monthly, seasonal, and annual variations in the behavior of Integrated Precipitable Water Vapour (IPWV) were estimated from the ground-based Global Navigation Satellite System (GNSS) stations network of India Meteorological Department (IMD) over the Indian region from 2017 to 2020. It is noticed in the study that the availability of moisture for coastal, inland, and desert stations are different and mainly influenced by horizontal advection or deep convection (when it occurs) by local as well as global weather systems affecting throughout the year over Indian region. These changes are also reflected in GNSS IPWV retrievals and behave differently for inland, coastal and desert locations. The diurnal variation (maximum, minimum) shows that the minimum value of IPWV occurred almost the same time for inland (0000–0002 UTC), coastal (0002–0004 UTC) stations except for Dwarka (1500 UTC), and one dessert station Sri Ganganagar (1800 UTC) whereas the maximum value varying from 0800 to 1000 UTC at inland & desert stations except for Pune (1200 UTC) and coastal stations (0700–1500 UTC). The monthly variation of highest/lowest IPWV values occurred in June, July & August/December & January respectively for all the stations of the Indian region (≥60 mm for inland & desert, and for coastal stations it ranges between 55 and 61 mm). The station-wise seasonal distributions are further grouped as high and low-value regions of IPWV for all the seasons. The value of the seasonal mean IPWV was observed highest in the monsoon season (June to August), followed by post-monsoon (September to November) and pre-monsoon (March to May), and winter (December to February). It is noticed that the stations situated in North India (Sri Ganganagar) registered the lowest values of IPWV in ~all seasons, whereas the highest IPWV values are registered at the stations Jalpaiguri of the North East with 66.16 mm, Nagpur of Central India with 65.47 mm in monsoon season. The lowest values of the annual mean of IPWV were noticed at the inland stations in Sriganganagar (23.86 mm) and the highest ones at the stations on the Thiruvananthapuram (47.59 mm) on the west coast of India.This study will be useful to understand the relationship between precipitation and the IPWV up to some extent and helpful in operational forecasting by considering other parameters like combining background circulation, thermodynamic conditions, etc., especially during convective weather events. In this paper, 4 case studies (event-wise) are presented for New Delhi GNSS stations and statistical skill scores were calculated which agree reasonably well with the actual occurrence. All the rainfall correlated events of the year 2021 were analyzed based on the monthly threshold of IPWV and the correct rate & false alarm rate were observed as 86 % & 14 % for station Delhi and similarly 85% & 15% for station Jalpaiguri respectively.

Ground surface elevation changes over permafrost areas revealed by multiple GNSS interferometric reflectometry

Ground subsidence and uplift caused by the annual thawing and freezing of the active layer are important variables in permafrost studies. Global positioning system interferometric reflectometry (GPS-IR) has been successfully applied to retrieve the continuous ground surface movements in permafrost areas. However, only GPS signals were used in previous studies. In this study, using multiple global navigation satellite system (GNSS) signal-to-noise ratio (SNR) observations recorded by a GNSS station SG27 in Utqiaġvik, Alaska during the period from 2018 to 2021, we applied multiple GNSS-IR (multi-GNSS-IR) technique to the SNR data and obtained the complete and continuous ground surface elevation changes over the permafrost area at a daily interval in snow-free seasons in 2018 and 2019. The GLONASS-IR and Galileo-IR measurements agreed with the GPS-IR measurements at L1 frequency, which are the most consistent measurements among all multi-GNSS measurements, in terms of the overall subsidence trend but clearly showed periodic noises. We proposed a method to reconstruct the GLONASS- and Galileo-IR elevation changes by specifically grouping and fitting them with a composite model. Compared with GPS L1 results, the unbiased root mean square error (RMSE) of the reconstructed Galileo measurements reduced by 50.0% and 42.2% in 2018 and 2019, respectively, while the unbiased RMSE of the reconstructed GLONASS measurements decreased by 41.8% and 25.8% in 2018 and 2019, respectively. Fitting the composite model to the combined multi-GNSS-IR, we obtained seasonal displacements of − 3.27 ± 0.13 cm (R2 = 0.763) and − 10.56 ± 0.10 cm (R2 = 0.912) in 2018 and 2019, respectively. Moreover, we found that the abnormal summer heave was strongly correlated with rain events, implying hydrological effects on the ground surface elevation changes. Our study shows the feasibility of multi-GNSS-IR in permafrost areas for the first time. Multi-GNSS-IR opens up a great opportunity for us to investigate ground surface movements over permafrost areas with multi-source observations, which are important for our robust analysis and quantitative understanding of frozen ground dynamics under climate change.

Potential utilization of air temperature, total electron content, and air relative humidity as possible earthquake precursors: A case study of Mexico M7.4 earthquake

This study examines ionospheric total electron content (TEC) perturbations from six International Global Navigation Satellite System stations (GNSS) including GUAT-Guatemala, SSIA-El Salvador, INEG-Mexico, MANA-Nicaragua, MDO1-United States of America, and BOGT- Colombia for several days around the occurrence of a major earthquake (M 7.4 and depth 20.0 km) in Mexico, June 23, 2020 at 10:29 Local Time (LT). The INEG station in the North-Northwest of the epicenter at a distance of about 936 km indicated a positive TEC anomaly on June 18, 2020, which can be possibly viewed as an earthquake precursor due to its occurrence during a quiet geomagnetic storm and inactive solar activity. Study findings reveal that other TEC perturbations may not be related to the earthquake, because they appeared during geomagnetic activities. Moreover, the atmospheric parameters have significant and synchronous deviations from the earthquake epicenter on June 13, 2020. The highest atmospheric chemical potential (ACP) is about 0.010 eV, atmospheric air temperature has positive deviation of 3.937 °C at 15:00 LT, the lowest atmospheric relative humidity has negative deviation of 25.387% at 13:00 LT and outgoing longwave radiation (OLR) 27.58 W m−2. Observations validate that in the earthquake preparation zone, variations in atmospheric air temperature and relative humidity reach at peak value during 10 days prior to the impending earthquake event with the later perturbations in the ionosphere.

Post‐Rifting Relaxation During 2015–2020 Following the Bárðarbunga‐Holuhraun Dike Intrusion and Eruption in Iceland

AbstractPost‐rifting ground deformation may be driven by viscoelastic relaxation of stresses generated by dike intrusions. The single‐dike intrusion of the 2014–2015 Bárðarbunga eruption in Iceland presents an opportunity for a detailed study of this process. We use continuous Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) velocity fields to analyze the 2015–2020 post‐rifting deformation, showing uplift on both sides of the dike and horizontal displacement away from the dike after correcting for background deformation. Two GNSS stations experience baseline lengthening at a rate of 19 mm/yr in the direction perpendicular to the strike of the dike. A two‐layer viscoelastic model with a 0.4 × 1019 Pa s viscoelastic half‐space overlain by an 18 km thick elastic layer best explains the observed horizontal and vertical InSAR and GNSS displacements. The model misfit space shows a second regime of good fit, likely driven by deformation near the dike that may result from cooling compaction of the emplaced dike.

Time-varying uplift in Svalbard—an effect of glacial changes

SUMMARYWe analyse Global Navigation Satellite System (GNSS) data from Svalbard to understand how uplift rates are controlled by the elastic and viscoelastic response of the solid Earth to changes in glacier mass on annual, interannual, decadal, centennial and millennial timescales. To reveal local patterns of deformation, we filter the GNSS time-series with an enhanced common-mode filtering technique where the non-tidal loading signal is incorporated. This technique reduces the estimated uncertainties for 5-yr time-series from 0.8 to 0.3 mm yr–1. Analysis of the GNSS data with different software–GAMIT, GipsyX, and GINS–produce consistent results that all indicate large temporal variations in uplift. For example, at the Ny-Ålesund GNSS station, uplift varies between 6 and 12 mm yr–1 for different 5-yr periods, and also shows a significant increase in the last 15 yr. We show that this increase is due to climate change-related ice mass loss in Svalbard. We constrain recent glacier retreat on Svalbard using a series of digital elevation models, and then correct the GNSS-derived uplift records for the elastic signal from these ice mass changes. The residual uplift signal is relatively constant, confirming the hypothesis that current ice mass changes exert a strong influence on GNSS observations. The relatively constant record of residual uplift can be used to constrain other geophysical signals such as the viscoelastic response of the solid Earth to ice loading during the Little Ice Age and the Last Glacial Period. We review uplift results from previous viscoelastic modelling studies and show that the residual signal cannot yet be fully explained. Our new uplift results thus motivate the need for new viscoelastic modelling of the glacial isostatic adjustment process in Svalbard.

Phase Centre Corrections of GNSS Antennas and Their Consistency with ATX Catalogues

Changes of the antenna models on permanent global navigation satellite system (GNSS) stations can lead to jumps and discontinuities in the coordinate time series. In this paper, the results of research on the adequacy of the antenna phase centre corrections (PCC) variations are presented by analysing its component—the antennas’ phase centre offset (PCO). For this purpose, height differences were determined using different and independent methods: EUREF Permanent Network (EPN) combined solutions, Precise Point Positioning (PPP), and the single baseline solution. The results of GNSS processing were referenced to direct geometric levelling outputs. The research was conducted only within the global positioning system (GPS) system due to the compatibility of one of the receivers, and the experiment was based on a comparison of the height differences between four GNSS antennas located on the roof of a building: two permanent station antennas and two auxiliary points. The antennas were located at similar heights; precise height differences were determined by geometric levelling, both at the beginning and the end of the session. Post-processing was conducted with the use of the GPS system, precise ephemeris, the adopted antenna correction model, and a zero-elevation mask. For one of the antennas, a change of the antenna characteristic model from IGS08 to IGS14 leads to an 8-mm difference in height. Older antennas used in the national (or transnational) permanent network need individual PCC.

A Review of GNSS/GPS in Hydrogeodesy: Hydrologic Loading Applications and Their Implications for Water Resource Research.

Hydrogeodesy, a relatively new field within the earth sciences, is the analysis of the distribution and movement of terrestrial water at Earth's surface using measurements of Earth's shape, orientation, and gravitational field. In this paper, we review the current state of hydrogeodesy with a specific focus on Global Navigation Satellite System (GNSS)/Global Positioning System measurements of hydrologic loading. As water cycles through the hydrosphere, GNSS stations anchored to Earth's crust measure the associated movement of the land surface under the weight of changing hydrologic loads. Recent advances in GNSS‐based hydrogeodesy have led to exciting applications of hydrologic loading and subsequent terrestrial water storage (TWS) estimates. We describe how GNSS position time series respond to climatic drivers, can be used to estimate TWS across temporal scales, and can improve drought characterization. We aim to facilitate hydrologists' use of GNSS‐observed surface deformation as an emerging tool for investigating and quantifying water resources, propose methods to further strengthen collaborative research and exchange between geodesists and hydrologists, and offer ideas about pressing questions in hydrology that GNSS may help to answer.

A Rainfall Forecast Model Based on GNSS Tropospheric Parameters and BP-NN Algorithm

The occurrence of rainfall is the result of a combination of various meteorological factors. Traditional rainfall early warning models solely use Global Navigation Satellite System (GNSS)-derived Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) to forecast rainfall, resulting in a low true detected rate. While non-linear rainfall early warning models based on the Back-Propagation Neural Network (BP-NN) algorithm consider the influences of various meteorological factors, the forecasts often exhibit a high false rate. To further improve the prediction of rainfall, a short-term rainfall early warning model based on the GNSS and BP-NN algorithms is proposed in this study. The method uses the traditional rainfall forecasting model and utilizes the BP-NN algorithm to combine various meteorological factors for rainfall early warning. The results of GNSS and BP-NN together improve the precision of rainfall early warning. Observation data from eight GNSS stations, the fifth-generation reanalysis of European Centre for Medium-Range Weather Forecast (ECMWF ERA5), and temperature, pressure, and rainfall data from corresponding meteorological stations in Ningbo, China were utilized to verify the rainfall early warning model proposed in this study. The results show that the proposed model can complement the advantages of the traditional linear and non-linear rainfall early warning methods. The model can maintain a high True Detected Rate (TDR) of rainfall early warning while simultaneously reducing the False Forecasted Rate (FFR). The average TDR of the eight GNSS stations is 100% and the FFR is 20.75%, which are both better than those of existing traditional linear and non-linear rainfall early warning models.

Deriving 3-D Surface Deformation Time Series with Strain Model and Kalman Filter from GNSS and InSAR Data

This study proposes a new set of processing procedures based on the strain model and the Kalman filter (SM-Kalman) to obtain high-precision three-dimensional surface deformation time series from interferometric synthetic aperture radar (InSAR) and global navigation satellite system (GNSS) data. Implementing the Kalman filter requires the establishment of state and observation equations. In the time domain, the state equation is generated by fitting the pre-existing deformation time series based on a deformation model containing linear and seasonal terms. In the space domain, the observation equation is established with the assistance of the strain model to realize the spatial combination of InSAR and GNSS observation data at each moment. Benefiting from the application of the Kalman filter, InSAR and GNSS data at different moments can be synchronized. The time and measurement update steps are performed dynamically to generate a 3-D deformation time series with high precision and a high resolution in the temporal and spatial domains. Sentinel-1 SAR and GNSS datasets in the Los Angeles area are used to verify the effectiveness of the proposed method. The datasets include twenty-seven ascending track SAR images, thirty-four descending track SAR images and the daily time series of forty-eight GNSS stations from January 2016 to November 2018. The experimental result demonstrates that the proposed SM-Kalman method can produce high-precision deformation results at the millimeter level and provide two types of 3-D deformation time series with the same temporal resolution as InSAR or GNSS observations according to the needs of users. The new method achieves a high degree of temporal and spatial fusion of GNSS and InSAR data.

SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections

Real-time precise point positioning (PPP) has become a prevalent technique in global navigation satellite systems (GNSS). However, GNSS real-time users must receive space state representation (SSR) products to correct for satellite clock, orbit, and phase biases. The International GNSS Service (IGS) provides GNSS users with real-time services (RTSs) through different real-time correction SSR products. These products arrive at the GNSS users with some latency, which affects the quality of real-time PPP positioning. The autoregressive integrated moving average (ARIMA) and support vector regression (SVR) models are used in this research to predict those corrections to eliminate the latency effect. ARIMA model reduces the standard deviation by 28% and 13% for GPS and GLONASS constellations, respectively, compared to the real-time solution, which includes the latency effect, the research simulated the latency effect and named it a forced-latency solution, and the SVR model reduces the standard deviation by 28% and 23% for GPS and GLONASS constellations, respectively. The results for the permanent GNSS stations used in this study across different years 2013, 2014, 2015, 2019, and 2021 show a mean reduction in the 3D positioning standard deviation by 13% compared with the forced-latency solution for the ARIMA solution and 9% for the SVR solution. The potential of both models to overcome the latency effect is apparent based on the findings.

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

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

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

AbstractThe ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

3D GNSS Velocity Field Sheds Light on the Deformation Mechanisms in Europe: Effects of the Vertical Crustal Motion on the Distribution of Seismicity

AbstractCrustal deformation and seismicity in Europe are still poorly understood. Seismic activity is classically ascribed to crustal strain rates generated by edge‐driven tectonic forces. However, crustal deformation is not only due to tectonic loading, but can also be related to isostatic and mantellic processes that induce additional stress and strain on the crust by flexure. The influence that those processes have on seismic activity, as well their interaction, is still controversial. The main limitation to study it is because the deformation and its causal mechanisms are usually analyzed separately in small regions. We present here a 3D secular velocity field that covers Eurasia and its plate boundaries including 4,863 Global Navigation Satellite System stations obtained by combining 10 different datasets. We have developed a method based on spatial filtering to identify outliers and smooth the velocity field, and have computed both strain and uplift rate maps that are interpreted in the light of the different driving processes that contribute to the 3D deformation in Europe. The vertical and horizontal deformation features are compared with seismic rates obtained from the spatial and temporal distribution of the seismicity in Europe. Our results suggest that is not possible to explain the seismicity in Europe with edge‐driven horizontal tectonic forces only. In some areas markers of the crustal flexure such as the vertical velocity field and its derivative, resulting from buoyancy‐driven processes, may help to interpret earthquake distribution.

Tidal Modulation of a Lateral Shear Margin: Priestley Glacier, Antarctica

We use high resolution, ground-based observations of ice displacement to investigate ice deformation across the floating left-lateral shear margin of Priestley Glacier, Terra Nova Bay, Antarctica. Bare ice conditions allow us to fix survey marks directly to the glacier surface. A combination of continuous positioning of a local reference mark, and repeat positioning of a network of 33 stakes installed across a 2 km width of the shear margin are used to quantify shear strain rates and the ice response to tidal forcing over an 18-day period. Along-flow velocity observed at a continuous Global Navigation Satellite Systems (GNSS) station within the network varies by up to ∼30% of the mean speed (±28 m a−1) over diurnal tidal cycles, with faster flow during the falling tide and slower flow during the rising tide. Long-term deformation in the margin approximates simple shear with a small component of flow-parallel shortening. At shorter timescales, precise optical techniques allow high-resolution observations of across-flow bending in response to the ocean tide, including across-flow strains on the order of 10–5. An elastodynamic model informed by the field observations is used to simulate the across-flow motion and deformation. Flexure is concentrated in the shear margin, such that a non-homogeneous elastic modulus is implied to best account for the combined observations. The combined pattern of ice displacement and ice strain also depends on the extent of coupling between the ice and valley sidewall. These conclusions suggest that investigations of elastic properties made using vertical ice motion, but neglecting horizontal displacement and surface strain, will lead to incorrect conclusions about the elastic properties of ice and potentially over-simplified assumptions about the sidewall boundary condition.

Inclusion of data uncertainty in machine learning and its application in geodetic data science, with case studies for the prediction of Earth orientation parameters and GNSS station coordinate time series

Data uncertainty plays an important role in the field of geodesy. Even though deep learning is becoming increasingly important for geodetic applications due to its high accuracy, it typically does not consider the data uncertainty. As we demonstrate in this study, we propose to include the uncertainty of data in deep neural network architectures to achieve a better generalization. This is advantageous for big data applications as well as for small datasets. Inspired by weighted and total least squares, we formulate the problem for both input and target uncertainties, and combine it with the Bayesian learning method. This results in a new form of the loss function in machine learning. As an alternative approach, we consider data uncertainties by including them as additional features. For comparison purposes, we use models without the consideration of data uncertainty as a benchmark. To show the efficacy of the proposed method, we apply it to the prediction of Earth Orientation Parameters (EOPs, namely polar motion, dUT1, and LOD) and Global Navigation Satellite System (GNSS) station coordinate time series. We demonstrate that the least-squares-inspired method outperforms both the benchmark and the feature-inspired method for both the studies. In the EOPs study, the improvement can be more than 50% in the study interval. In the study of GNSS station coordinate time series, which is presented for 1000 stations across the globe, the improvement on an average basis is around 12%. The results demonstrate the advantage of using uncertainty information in the machine learning algorithms, when applied to geodetic time series.