Global Navigation Satellite System Stations Research Articles

Snow Depth Measurements by GNSS-IR at an Automatic Weather Station, NUK-K

Studies have shown that geodetic Global Navigation Satellite System (GNSS) stations can be used to measure snow depths using GNSS interferometric reflectometry (GNSS-IR). Here, we study the results from a customized GNSS setup installed in March through August 2020 at the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station NUK-K located on a small glacier outside Nuuk, Greenland. The setup is not optimized for reflectometry purposes. The site is obstructed between 85 and 215 degrees, and as the power supply is limited due to the remote location, the logging time is limited to 3 h per day. We estimate reflector heights using GNSS-IR and compare the results to a sonic ranger also placed on the weather station. We find that the snow melt measured by GNSS-IR is comparable to the melt measured by the sonic ranger. We expect that a period of up to 45 cm difference between the two is likely related to the much larger footprint GNSS-IR and the topography of the area. The uncertainty on the GNSS-IR reflector heights increase from approximately 2 cm for a snow surface to approximately 5 cm for an ice surface. If reflector height during snow free periods are part of the objective of a similar setup, we suggest increasing the logging time to reduce the uncertainty on the daily estimates.

PPP-RTK considering the ionosphere uncertainty with cross-validation

With the high-precision products of satellite orbit and clock, uncalibrated phase delay, and the atmosphere delay corrections, Precise Point Positioning (PPP) based on a Real-Time Kinematic (RTK) network is possible to rapidly achieve centimeter-level positioning accuracy. In the ionosphere-weighted PPP–RTK model, not only the a priori value of ionosphere but also its precision affect the convergence and accuracy of positioning. This study proposes a method to determine the precision of the interpolated slant ionospheric delay by cross-validation. The new method takes the high temporal and spatial variation into consideration. A distance-dependent function is built to represent the stochastic model of the slant ionospheric delay derived from each reference station, and an error model is built for each reference station on a five-minute piecewise basis. The user can interpolate ionospheric delay correction and the corresponding precision with an error function related to the distance and time of each reference station. With the European Reference Frame (EUREF) Permanent GNSS (Global Navigation Satellite Systems) network (EPN), and SONEL (Système d'Observation du Niveau des Eaux Littorales) GNSS stations covering most of Europe, the effectiveness of our wide-area ionosphere constraint method for PPP-RTK is validated, compared with the method with a fixed ionosphere precision threshold. It is shown that although the Root Mean Square (RMS) of the interpolated ionosphere error is within 5 cm in most of the areas, it exceeds 10 cm for some areas with sparse reference stations during some periods of time. The convergence time of the 90th percentile is 4.0 and 20.5 min for horizontal and vertical directions using Global Positioning System (GPS) kinematic solution, respectively, with the proposed method. This convergence is faster than those with the fixed ionosphere precision values of 1, 8, and 30 cm. The improvement with respect to the latter three solutions ranges from 10 to 60%. After integrating the Galileo navigation satellite system (Galileo), the convergence time of the 90th percentile for combined kinematic solutions is 2.0 and 9.0 min, with an improvement of 50.0% and 56.1% for horizontal and vertical directions, respectively, compared with the GPS-only solution. The average convergence time of GPS PPP-RTK for horizontal and vertical directions are 2.0 and 5.0 min, and those of GPS + Galileo PPP-RTK are 1.4 and 3.0 min, respectively.

The Conjugated Ionospheric Anomalies Preceding the 2011 Tohoku-Oki Earthquake

We present the conjugated ionospheric total electron content (TEC) anomalies prior to the 2011 Mw9.0 Tohoku-oki earthquake, Japan, observed by the Global Navigation Satellite System (GNSS) stations in northern Australia. The onset time of the anomaly, determined by the Akaike’s information criterion, is 41.5 min before the earthquake in Australia, which is very close to the time observed in Japan. The positive TEC anomalies in Australia emerged on the same longitude as the land area of NE Japan. This supports the model that electric fields within the ionosphere redistributed the electrons immediately before large earthquakes. However, the observed anomaly is shifted equatorward by ∼500 km reflecting the difference in physical mechanisms between the two hemispheres. We also found that the geomagnetic declination near the conjugate point simultaneously started to change ∼40 min before the earthquake, but its physical implication is yet to be explored.

Slow slip events and flank instability at Mt. Etna volcano (Italy)

We analyzed a set of 11 slow slip events occurred during the 2006–2016 period and affecting the GNSS (Global Navigation Satellite System) stations of the unstable flank of Mt. Etna volcano. Observed surface deformation for most of the detected slow slip events, concentrates on the south-eastern edge of the unstable flank while the slow slip events involving the north-eastern edge are less frequent. Such a pattern highlights the existence of two distinct families of events, involving two contiguous sectors of the unstable flank, which occasionally slip together in large slow slip events. The modelled slips also highlight that both contiguous sectors extend ~10–12 km offshore, on areas where active tectonic lineaments such as the ESE (northward of Catania Canyon) and the N102° (along the southern slope of the Riposto Ridge) ones have been recently discovered. Equivalent seismic moments of slow slip events occurred in the last ten years (corresponding to magnitudes in the range 5.4–5.9) are larger than those associated to seismic events observed in the last 200 years, suggesting that most of the deformation affecting the eastern flank occurs aseismically.

Impact assessment of the seasonal hydrological loading on geodetic movement and seismicity in Nepal Himalaya using GRACE and GNSS measurements

The Himalayan terrain is an epitome of ongoing convergence and geodetic deformation where both tectonic and non-tectonic forces prevail. In this study, the Gravity Recovery and Climate Experiment (GRACE) and Global Positioning System (GPS) datasets are used to assess the impact of seasonal loading on deformation with seismicity in Nepal. The recorded GPS data from 21 Global Navigation Satellite System (GNSS) stations during 2017–2020 are processed with respect to ITRF14 and the Indian reference frame, and the Center for Space Research (CSR) mascon RL06 during 2002–2020 is adopted to estimate the terrestrial water storage (TWS) change over the Ganga-Brahmaputra River basin. The results indicate that the hydrological loading effect or TWS change shows high negative, high positive, and moderately positive values in pre-monsoon, co-monsoon, and post-monsoon months, respectively. The detrended GPS data of both horizontal and vertical components correlate with the seasonal TWS change using the Pearson correlation coefficient at each GNSS site. In addition, the correlation coefficient has been interpolated using inverse distance weighting to investigate the regional TWS influence on geodetic displacement. In the north component, the correlation coefficient ranges from −0.6 to 0.6. At the same time, the TWS is positively correlated with geodetic displacement (0.82) in the east component, and the correlation coefficient is negative (−0.69) in the vertical component. The negative correlation signifies an inverse relationship between seasonal TWS variation and geodetic displacements. The strain rate is estimated, which shows higher negative values in pre-monsoon than in post-monsoon. Similarly, the effect of seismicity is 47.90% for pre-monsoon, 15.97% for co-monsoon, and 17.56% for post-monsoon. Thus we can infer that the seismicity decreases with the increase of seasonal hydrological loading. Furthermore, the effect of strain is much higher in pre-monsoon than in post-monsoon since the impact of co-monsoon continues to persist on a small scale in the post-monsoon season.

An Ionospheric TEC Forecasting Model Based on a CNN-LSTM-Attention Mechanism Neural Network

Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R2 of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions.

Investigation of determining the accuracy of spatial vectors by the satellite method in a real time mode

Abstract The study of determining the accuracy of spatial vectors by the global navigation satellite system (GNSS) in real time (RTK) was conducted. The possibility of construction of precision geodetic networks by the combined method of static and RTK GNSS measurements which correspond to the set accuracy and reach the maximum economic efficiency is investigated. A technique providing the densification of GNSS network and the use of two simultaneously operating GNSS receivers (Rover) is proposed. The research was carried out at the points of the GNSS network of Dnister Pumped Storage Power Plant (PSPP) (Ukraine). As a result of comparison of reference and measured elements of vectors, it was found that the average absolute error in determining the spatial distance of 14 vectors was 5.3 mm. Rejection of vectors with a closed horizon reduced the error to 2.1 mm. The vectors are most accurately determined from two satellite systems (GPS, GLONASS) and from a single base station. The recommended distance to the base station is within one kilometer. Increasing the accuracy by 75 % in determining the vector by the proposed method in RTK mode is also shown. As a result of a posteriori optimization of combined GNSS networks, high accuracy on the reliability of vectors determined by the method in RTK mode was confirmed. The technique can be used to construct precision networks, to carry out repeated measurements for the monitoring of large engineering structures with an open horizon.

Evaluation of the Multipath Environment Using Electromagnetic-Absorbing Materials at Continuous GNSS Stations.

To date, no universal modelling technique is available to mitigate the effect of site-specific multipaths in high-precision global navigation satellite system (GNSS) data processing. Multipaths affect both carrier-phase and code/pseudorange measurements, and the errors can propagate and cause position biases. This paper presents the use of an Eccosorb AN-W-79 microwave-absorbing material mounted around a GNSS antenna that reflects less than dB of normal incident energy above a frequency of 600 MHz. To verify the feasibility and effectiveness of the Eccosorb, we installed two close stations by continuously operating multi-GNSS (BeiDou, GLONASS, Galileo and GPS) in a challenging location. One station is equipped with the Eccosorb AN-W-79, covering a square area of 3.35 m around the antenna, and the second station operates without it. The standard deviation reductions from single point positioning estimates are significant for all the individual GNSS solutions for the station equipped with microwave-absorbing material. The reductions are as follows: for GPS, between 15% and ; for Galileo, between 22% and ; for GLONASS, ; and for BeiDou, . Furthermore, we assess the influence of multipaths by analysing the linear combinations of code and carrier phase measurements for various GNSS frequencies. The Galileo code multipath shows a reduction of more than for the station with microwave-absorbing material. For GLONASS, particularly for the GLOM3X and GLOM1P code multipath combinations, the reduction reaches , depending on the observation code types. For BeiDou, the reduction is more than , and for GPS, it reaches between and . The Eccosorb AN-W-79 microwave-absorbing material shows convincing results in reducing the code multipath noise level. Again, using microwave-absorbing material leads to an improvement between and in carrier phase cycle slips. The carrier-phase multipath contents on the post-fit residuals from the processed GNSS solutions show a relative RMS reduction of for Galileo and for GLONASS and GPS when using the microwave-absorbing material. This study also presents power spectral contents from residual signal-to-noise ratio time series using Morlet wavelet transformation. The power spectra from the antenna with the Eccosorb AN-W-79 have the smallest magnitude, demonstrating the capacity of microwave-absorbing materials to lessen the multipath influence while not eliminating it.

L1 Trend Filtering‐Based Detection of Short‐Term Slow Slip Events: Application to a GNSS Array in Southwest Japan

AbstractThe discovery of slow slip events (SSEs) based on the installation of dense geodetic observation networks has provided important clues to understanding the process of stress release and accumulation in subduction zones. Because SSEs with durations of days to weeks do not often result in sufficient displacements that can be visually inspected, refined automated detection methods are required to understand the occurrence of SSEs. In this study, we propose a new method based on which SSEs can be detected in observations derived by a Global Navigation Satellite System (GNSS) array by using l1 trend filtering, a variation of sparse estimation, in conjunction with combined ‐value techniques. The sparse estimation technique and data‐driven determination of hyperparameters are utilized in the proposed method to identify candidates of the event onsets. In addition, combined ‐value techniques are used to provide confidence values for the detections. The synthetic test demonstrated that the new method can detect 22 events of total 23 events and has only 1% false detections with the detection threshold 68%. The proposed method was then applied to daily displacements obtained at 39 GNSS stations in the Nankai subduction zone in western Shikoku, Southwest Japan. The results revealed that, in addition to all known events, new 12 events can be detected with the proposed method. Finally, we found the number of low‐frequency earthquakes in the target region increased around at the onsets of potential events.

Aging of ground Global Navigation Satellite System oscillators

Global Navigation Satellite System (GNSS) are widely used in many areas of human life and activity. The proper functioning of GNSS systems depends on several factors, the most important of which is the correct knowledge of time. The position indirectly is based on the knowledge of the distance, which is determined based on time with the knowledge of the speed of the electromagnetic wave. Thus, proper (accurate) knowledge of time (GNSS clock stability) is a key to precise positioning. In this text, the long-term stability of the GNSS station clocks covering the years 1994-2020 was analysed. For this purpose, the corrections of the clocks at selected permanent stations were used, and their stability was determined for all years separately. Then the change of clock stability over time and the search for correlation were analysed. As the results showed, there are clearly differences between four of the type of oscillators analysed. In case of the comparison on an annual basis, no change over time was found.

Long-term correlation analysis between monthly precipitable water vapor and precipitation using GPS data over China

Precipitable Water Vapor (PWV) is an important driving factor for the occurrence of precipitation. A deep understanding of the correlation between the PWV and the precipitation helps to understand the regional water cycle based on the theory of atmospheric water balance. This study aims to analyze the correlation between the precipitation and the PWV derived from Global Positioning System (GPS) observations collected from a total of 233 Global Navigation Satellite System (GNSS) stations in China mainland over 10 years. The PWV was calculated through Zenith Total Delay (ZTD) from the Precise Point Positioning (PPP) technique and metrological data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The relationship between PWV derived from GPS and the precipitation from the Tropical Rainfall Measuring Mission (TRMM) at monthly temporal resolution was analyzed through the cross-correlation analysis and the cross-wavelet analysis. Results showes that a 1-month lag was observed between monthly PWV and precipitation at some inland stations in the south-eastern region of China, which is due to the earlier peaks of precipitation caused by the quasi-stationary front in this region. After the time-lag shift, most of the stations showed a cross-correlation coefficient higher than 0.7, indicating that monthly PWV and precipitation are generally correlated over China mainland. Specifically, the cross-correlation coefficients from stations located in western China have a wide range of 0.16–0.95 and a large dispersion degree with the standard deviation (STD) of 0.2, which is due to the variable climatic conditions caused by the topographical distribution. The cross-correlation coefficients from stations located in the middle and north-eastern region of China are generally greater than 0.75 and have an STD of 0.08, while those located in the south-eastern region of China are generally less than 0.65 and have an STD of 0.09. This is because the south-eastern region of China has a relatively longer rainy season caused by the influences from Asian monsoon systems. These results could contribute to the further understanding of the regional water cycle based on the theory of atmospheric water balance within China.

Possible seismo-ionospheric anomalies associated with Mw > 5.0 earthquakes during 2000–2020 from GNSS TEC

The recent advances in space-based ionosphere measurements provide more detailed information about transient ionospheric anomalies associated with earthquakes (EQs). In this paper, we study the possible relation of EQs and ionospheric anomalies in a statistical analysis by analyzing 534 EQs of Mw > 5.0 during 2000–2020 from Vertical Total Electron Content (VTEC) acquired from the Global Navigation Satellite System (GNSS) of International GNSS Services (IGS) network. The selection criteria of these EQs was mainly subjected to two conditions: (1) at least three or more GNSS stations around the Dobrovolsky region were present, (2) the EQs occurred during quiet geomagnetic activity (Kp < 3, Dst ≥ -20nT). Particularly, we performed a statistical analysis on the basis of the median and interquartile range (IQR) over VTEC for each EQ, and the positive/negative anomalies were analyzed in the form of cumulative count for 20 days prior and after the mainshock day. Moreover, this study demonstrates that EQ induced VTEC anomalies occurred more frequently with 3 or more GNSS stations around the epicenter in Dobrovolsky’s region within a 5–10-day window before the EQs. Furthermore, the anomalies with a low deviation also initiated after the mainshock day within 5–10-day window. On the other hand, the deviations are more clear for Mw > 6.0 EQs, where positive anomalies were observed after excluding false anomalies in the corresponding IGS stations, showing a higher percentage of positive than negative anomalies and also more positive (negative) anomalies occur in solar maximum (minimum) years of the solar cycles 23 and 24. The false anomalies were the ionospheric perturbations associated with geomagnetic storms (Kp > 3) and VTEC values beyond upper/lower bounds outside the 5–10 days anomalous window. This statistical analysis assists to support the lithosphere and ionosphere coupling for future EQ ionospheric precursors.

Analysis of groundwater changes (2003–2020) in the North China Plain using geodetic measurements

Study regionNorth China Plain (NCP), China. Study focusThis paper investigates the spatiotemporal characteristics of groundwater storage (GWS) changes in the NCP during 2003–2020 using Gravity Recovery and Climate Experiment (GRACE) and Global Navigation Satellite System (GNSS) measurements with a focus on the subperiods separated by the implementation (December 2014) of the South-to-North Water Diversion Middle Route Project (MRP). New hydrological insights for the regionGRACE-derived results demonstrate that GWS depleted at rates of − 1.66 ± 0.17 cm/yr and − 2.76 ± 0.55 cm/yr for subperiods of 2003–2014 and 2015–2020, respectively, suggesting that GWS continued decreasing after the MRP. However, the spatial distribution of average monthly GWS shows an evident southwest-northeast band distribution, and the GWS depletion in the northwest has been alleviated compared with the southeast. The GNSS stations in the Beijing area show accelerated increasing rates of vertical displacements, while those in the plain areas of Hebei and Tianjin show continuous declining rates, implying that GWS in the Beijing area recovered faster after the MRP, while the GWS depletion in plain areas of Hebei and Tianjin continued. In addition, precipitation is the main driving factor to GWS variability during the whole study period, with grey correlation coefficients of 0.66–0.75. This strong correlation explains the significant reduction in GWS caused by the large precipitation deficit in 2019, which leads to a larger GWS depletion rate for the 2015–2020 period.

Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China

Precipitable water vapor can be estimated from the Global Navigation Satellite System (GNSS) signal’s zenith wet delay (ZWD) by multiplying a conversion factor, which is a function of weighted mean temperature (Tm) over the GNSS station. Obtaining Tm is an important step in GNSS precipitable water vapor (PWV) conversion. In this study, aiming at the problem that Tm is affected by space and time, observations from seven radiosonde stations in the Yangtze River Delta region of China during 2015−2016 were used to establish both linear and nonlinear multifactor regional Tm model (RTM). Compared with the Bevis model, the results showed that the bias of yearly one-factor RTM, two-factor RTM and three-factor RTM was reduced by 0.55 K, 0.68 K and 0.69 K, respectively. Meanwhile, the RMSE of yearly one-factor, two-factor and three-factor RTM was reduced by 0.56 K, 0.80 K and 0.83 K, respectively. Compared with the yearly three-factor linear RTM, the mean bias and RMSE of the linear seasonal three-factor RTMs decreased by 0.06 K and 0.10 K, respectively. The precision of nonlinear seasonal three-factor RTMs is comparable to linear seasonal three-factor RTMs, but the expressions of the linear RTMs are easier to use. Therefore, linear seasonal three-factor RTMs are more suitable for calculating Tm and are recommended to use for PWV conversion in the Yangtze River Delta region.

The 21 May 2021 Mw 6.1 Yangbi Earthquake—A Unilateral Rupture Event with Conjugately Distributed Aftershocks

Abstract An Mw 6.1 earthquake occurred in Yangbi, Yunnan province, China, at 13:48:34 (UTC time) on 21 May 2021. It exhibited a typical foreshock–mainshock–aftershock sequence in the northwestern Yunnan rifting area. We used the first P-arrival time from regional broadband seismic stations to check the location of the mainshock’s hypocenter. The updated location of the epicenter is 99.88° E, 25.66° N. The focal mechanisms of nine earthquakes, including the mainshock and the largest foreshock and aftershock, were investigated using the general cut-and-paste method based on regional seismic waveforms to set up the fault geometry and to calculate the static coulomb stress change. The rupture process of the mainshock was constrained by 33 unclipped regional broadband seismic records from 15 stations and nine static displacements from three Global Navigation Satellite System stations. The results showed that the Yangbi earthquake was a unilateral rupture event propagating toward the southeast along-strike direction. The moment was 1.81×1018 N·m, and the corresponding magnitude was Mw 6.1. The peak slip value, the weighted rupture velocity, and the duration time of the mainshock were 0.95 m, 2.2 km/s, and 9.1 s, respectively. At 10 km southeast of the mainshock’s epicenter, the conjugately distributed aftershocks were observed, and the moment density of the mainshock along the strike direction decreased sharply (78% of the total moment released in the first 10 km), suggesting that the sharply decelerating rupture could be the reason for the conjugately distributed aftershocks. The changes in static coulomb stress due to the largest foreshock and mainshock were calculated. The results showed positive Coulomb stress on the major slip zone in the mainshock fault plane triggered by the largest foreshock. Moreover, the 0.01 MPa contour due to the mainshock radiated out of the bend zone of the Weixi–Qiaohou–Weishan fault.

The Crustal Vertical Deformation Driven by Terrestrial Water Load from 2010 to 2014 in Shaanxi–Gansu–Ningxia Region Based on GRACE and GNSS

The terrestrial water resources in Shaanxi–Gansu–Ningxia (SGN) region are relatively scarce, and its climate change is unstable. Research on the deformation driven by terrestrial water load is of great significance to the dynamic maintenance of reference station networks. In this paper, data derived from Gravity Recovery and Climate Experiment (GRACE) and Global Navigation Satellite System (GNSS) from 2010 to 2014 were combined to monitor the spatiotemporal characteristics of surface vertical deformation caused by terrestrial water load change. The single scale factor was calculated by comparing CPC, WGHM, and GLDAS hydrological model to restore filtering leakage signal. The singular spectrum analysis (SSA) method was used to extract the principal component of temporal vertical deformation, and its spatial distribution was analyzed. At the same time, in order to study the relationship between the terrestrial water load deformation from GRACE and that from GNSS, the first-order term correction, the Atmosphere and Ocean De-aliasing Level-1B product (GAC) correction, and the first-order load LOVE number correction for GRACE were adopted in this paper. In addition, a quantitative comparative analysis of both the monitoring results was carried out. The results show that the time-variable characteristics of surface vertical deformation characterized by the filtered three hydrological models were consistent with those of GRACE. The correlation coefficient and Nash–Sutcliffe efficiency coefficient (NSE) values were the highest in the GLDAS model and the GRACE model, respectively; the former index is 0.93, while the latter is 0.85. The crustal vertical deformation from terrestrial water load showed a declining rate from 2010 to 2014. Its spatial change rate showed an obvious ladder distribution, with the surface subsidence rate gradually decreasing from south to north. In addition, weighted root mean square (WRMS) contribution rate of the crustal vertical deformation resulting from GRACE with GAC correction between the different GNSS stations ranged from 18.52% to 54.82%. The correlation coefficient between them was close to 0.70. After deducting the mass load impact of GRACE only, the WRMS contribution rate of the corresponding stations decreased from −8.42% to 21.18%. The correlation coefficient between them reduced noticeably. Adding GAC back can increase the comparability with GRACE and GNSS in terms of monitoring the crustal vertical deformation. The annual amplitude and phase of surface vertical deformation resulting from GRACE with GAC correction were close to those of GNSS. The research results can help to explore the motion mechanism between water migration and surface deformation, which is of benefit in the protection of the water ecological environment in the region.

Testing a Prototype Earthquake Early Warning System: A Retrospective Study of the 2021 Mw 7.4 Maduo, Tibet, Earthquake

Abstract On 21 May 2021, an Mw 7.4 earthquake occurred in Maduo County, Qinghai Province, China. A dense network of high-rate (1 Hz) Global Navigation Satellite System (GNSS) stations around the epicenter provided one of the most complete recordings of GNSS kinematic displacements in Tibet to date. We used these data retrospectively to test a prototype earthquake early warning (EEW) system. Here, we present the results of that test, in which the EEW system archived high-rate GNSS displacement streams, and then used them to track evolving magnitude and slip. The geodetic module in the EEW system issued the first alert 34 s after the earthquake onset. The first alert had a moment magnitude (Mw) of 6.65, which then increased until reaching a stable value (Mw 7.4) 61 s after the earthquake onset. Testing results show good agreement with rapid GNSS displacements and postprocessed GNSS displacements; moreover, the rapid finite-fault inversion is consistent with postinversion magnitude and rupture dimensions. Our findings confirm the viability and usefulness of this EEW system in quasi-real-time magnitude estimation and finite-fault slip inversion.

Temporal Analysis of GNSS-Based Precipitable Water Vapor during Rainy Days over the Philippines from 2015 to 2017

Precipitable water vapor (PWV) is a parameter used to estimate water vapor content in the atmosphere. In this study, estimates of PWV from PIMO, PLEG and PPPC global navigation satellite system (GNSS) stations are evaluated regarding the PWV obtained from its collocated radiosonde (RS) stations. GNSS PWV were highly correlated with RS PWV (R ~ 0.97). Mean bias error (MBE) between −0.18 mm and −13.39 mm, and root mean square error (RMSE) between 1.86 mm and 2.29 mm showed a good agreement between GNSS PWV and RS PWV. The variations of PWV are presented. Daily variations of PWV conformed to the daily data of rainfall which agrees to the climate types of Quezon City (Type I), Legaspi (Type II), and Puerto Princesa (Type III) based on the Coronas climate classification. Moreover, PWV monthly variation at all sites is high from May to October (~62 mm) and low from November to April (~57 mm). The relationship between PWV and rainfall at all stations showed positive correlation coefficients between +0.49 to +0.83. Meanwhile, it is observed that when PWV is high (low), its variability is low (high). This study shows the potential of GNSS to study water vapor and its contribution to weather analysis.

A New Spatial Filtering Algorithm for Noisy and Missing GNSS Position Time Series Using Weighted Expectation Maximization Principal Component Analysis: A Case Study for Regional GNSS Network in Xinjiang Province

Common Mode Error (CME) presents a kind of spatially correlated error that is widespread in regional Global Navigation Satellite System (GNSS) networks and should be eliminated during postprocessing of a GNSS position time series. Several spatiotemporal filtering methods have been developed to mitigate the effects of CME. However, such methodologies become inappropriate when missing and noisy data exists. In this research, we introduce a novel spatial filtering algorithm called Weighted Expectation Maximization Principal Component Analysis (WEMPCA) for detecting and removing CME from noisy GNSS position time series with missing values, among which formal errors of daily GNSS solutions are utilized to weight the input data. Compared with traditional PCA and the special case of EMPCA, simulation experiments demonstrate that the new WEMPCA algorithm always has outstanding performance over others. The WEMPCA algorithm was then successfully used to extract the CME from real noisy and missing GNSS position time series in Xinjiang province. Our results show that only the first principal component exhibits significant spatial response, with average values of 70.11%, 66.53%, and 52.45% for North, East, and Up (NEU) components, respectively, indicating that it represents the CME of this region. After removing CME, the canonical correlation coefficients and root mean square error of GNSS residual time series, as well as the amplitudes of power-law noises (PLN), are obviously decreased in all three directions. However, the white noise (WN) amplitudes are found to diminish exclusively in the North and East component, not in the Up components. Moreover, the average velocity differences before and after filtering CME are 0.19 mm/year, 0.03 mm/year, and −0.56 mm/year for the NEU components, respectively, indicating that CME has an influence on the GNSS station velocity estimation. The velocity uncertainty is also reduced by 43.51%, 38.64%, and 40.39% on average for the NEU components, respectively, implying that the velocity estimates are more reliable and accurate after removing CME. Therefore, we conclude that the new WEMPCA approach provides an efficient solution to detect and mitigate CME from the noisy and missing GNSS position time series.

Assessment and calibration of FY-4A AGRI total precipitable water products based on CMONOC

FengYun 4A (FY-4A) is the first flight unit of the second-generation geostationary meteorological satellite in China (FengYun 4 series, FY-4 series). The Advanced Geostationary Radiation Imager (AGRI) onboard it can provide high spatial resolution precipitable water vapor (PWV) products. This study used Global Navigation Satellite System (GNSS) observations from 230 ground-based GNSS stations within the Crustal Movement Observation Network of China (CMONOC) from March 1, 2019 to February 29, 2020 to obtain PWV, also called total precipitable water (TPW). The Chinese mainland was divided into 13 regions based on climate classification, latitude, and altitude, and GNSS PWV was used to verify the FY-4A AGRI TPW and make calibrations when necessary. The results reveal that the accuracy of FY-4A AGRI TPW shows significant spatial and temporal differences in mainland China. Overall, FY-4A AGRI TPW and GNSS PWV are strongly positively correlated over China, and the correlation coefficients (R) between the two PWV products are highest in autumn (0.78–0.972) while lowest in winter (0.556–0.956). FY-4A AGRI TPW is underestimated in most regions, and the mean bias (MB) values are higher in regions with high PWV content. The root means square error (RMSE) of FY-4A AGRI TPW in all regions are highest in summer (3.34–9.05 mm) and lowest in winter (0.87–3.83 mm). The accuracy of FY-4A AGRI TPW exhibits significant regional and seasonal differences, with higher R, RMSE, and MB in wet seasons and regions, while lower R, RMSE, and MB in dry seasons and regions. This study constructed the calibration model of FY-4A AGRI TPW was based on GNSS PWV in 13 regions. After calibration, the RMSEs can be reduced by 6.6% ~ 32.1%, 5.1% ~ 45%, 5.7% ~ 31.6%, and 7.1% ~ 30.8% in four seasons, respectively, and the MB mainly fluctuates around 0. The accuracy of generated FY-4A AGRI TPW is further improved on the basis of the high resolution.