Global Navigation Satellite System Stations Research Articles

Validation of regional and global ionosphere maps from GNSS measurements versus IRI2016 during different magnetic activity

Abstract This manuscript explores the divergence of the Vertical Total Electron Content (VTEC) estimated from Global Navigation Satellite System (GNSS) measurements using global, regional, and International Reference Ionosphere (IRI) models over low to high latitude regions during various magnetic activity. The VTEC is estimated using a territorial network consisting of 7 GNSS stations in Egypt and 10 GNSS stations from the International GNSS Service (IGS) Global network. The impact of magnetic activity on VTEC is investigated. Due to the deficiency of IGS receivers in north Africa and the shortage of GNSS measurements, an extra high interpolation is done to cover the deficit of data over North Africa. A MATLAB code was created to produce VTEC maps for Egypt utilizing a territorial network contrasted with global maps of VTEC, which are delivered by the Center for Orbit Determination in Europe (CODE). Thus we can have genuine VTEC maps estimated from actual GNSS measurements over any region of North Africa. A Spherical Harmonics Expansion (SHE) equation was modelled using MATLAB and called Local VTEC Model (LVTECM) to estimate VTEC values using observations of dual-frequency GNSS receivers. The VTEC calculated from GNSS measurement using LVTECM is compared with CODE VTEC results and IRI-2016 VTEC model results. The analysis of outcomes demonstrates a good convergence between VTEC from CODE and estimated from LVTECM. A strong correlation between LVTECM and CODE reaches about 96 % and 92 % in high and low magnetic activity, respectively. The most extreme contrasts are found to be 2.5 TECu and 1.3 TECu at high and low magnetic activity, respectively. The maximum discrepancies between LVTECM and IRI-2016 are 9.7 TECu and 2.3 TECu at a high and low magnetic activity. Variation in VTEC due to magnetic activity ranges from 1–5 TECu in moderate magnetic activity. The estimated VTEC from the regional network shows a 95 % correlation between the estimated VTEC from LVTECM and CODE with a maximum difference of 5.9 TECu.

Atmospheric modes excited by the 2021 August eruption of the Fukutoku-Okanoba volcano, Izu\u2013Bonin Arc, observed as harmonic TEC oscillations by QZSS

Continuous Plinian eruptions of volcanoes often excite atmospheric resonant oscillations with several distinct periods of a few minutes. We detected such harmonic oscillations by the 2021 August eruption of the Fukutoku-Okanoba volcano, a submarine volcano in the Izu–Bonin arc, in ionospheric total electron content (TEC) observed from Global Navigation Satellite System (GNSS) stations deployed on three nearby islands, Chichijima, Hahajima, and Iwojima. Continuous records with the geostationary satellite of Quasi-Zenith Satellite System (QZSS) presented four frequency peaks of such atmospheric modes. The harmonic TEC oscillations commenced at ~ 5:16 UT with a large amplitude but decayed in a few hours.Graphical

The 95% Confidence Interval for GNSS-Derived Site Velocities

Linear trends, or site velocities, derived from global navigation satellite system (GNSS) positional time series have been commonly applied to site stability assessments, structural health monitoring, sea-level rise, and coastal submergence studies. The uncertainty of the velocity has become a big concern for stringent users targeting structural or ground deformation at a few millimeters per year. GNSS-derived positional time series are autocorrelated. Consequently, conventional methods for calculating the standard errors of the linear trends result in unrealistically small uncertainties. This article presents an approach to accounting for the autocorrelation with an effective sample size (Neff). A robust methodology has been developed to determine the 95% confidence interval (95%CI) for the site velocities. It is found that the 95%CI fits an inverse power-law relationship over the time span of the time series (vertical direction: 95%CI=5.2T−1.25; east–west or north–south directions: 95%CI=1.8T−1.0). For static GNSS monitoring projects, continuous observations longer than 2.5 and 4 years are recommended to achieve a 95%CI below 1 mm/year for the horizontal and vertical site velocities, respectively; continuous observations longer than 7 years are recommended to achieve a 95%CI below 0.5 mm/year for the vertical land movement rate (subsidence or uplift). The 95%CI from 7-year GNSS time series is equivalent to the 95%CI of the sea-level trend derived from 60-year tide gauge observations. The method and the empirical formulas developed through this study have the potential for broad applications in geosciences, sea-level and coastal studies, and civil and surveying engineering.

Signatures of Equatorial Plasma Bubbles and Ionospheric Scintillations from Magnetometer and GNSS Observations in the Indian Longitudes during the Space Weather Events of Early September 2017

Scintillation due to ionospheric plasma irregularities remains a challenging task for the space science community as it can severely threaten the dynamic systems relying on space-based navigation services. In the present paper, we probe the ionospheric current and plasma irregularity characteristics from a latitudinal arrangement of magnetometers and Global Navigation Satellite System (GNSS) stations from the equator to the far low latitude location over the Indian longitudes, during the severe space weather events of 6–10 September 2017 that are associated with the strongest and consecutive solar flares in the 24th solar cycle. The night-time influence of partial ring current signatures in ASYH and the daytime influence of the disturbances in the ionospheric E region electric currents (Diono) are highlighted during the event. The total electron content (TEC) from the latitudinal GNSS observables indicate a perturbed equatorial ionization anomaly (EIA) condition on 7 September, due to a sequence of M-class solar flares and associated prompt penetration electric fields (PPEFs), whereas the suppressed EIA on 8 September with an inverted equatorial electrojet (EEJ) suggests the driving disturbance dynamo electric current (Ddyn) corresponding to disturbance dynamo electric fields (DDEFs) penetration in the E region and additional contributions from the plausible storm-time compositional changes (O/N2) in the F-region. The concurrent analysis of the Diono and EEJ strengths help in identifying the pre-reversal effect (PRE) condition to seed the development of equatorial plasma bubbles (EPBs) during the local evening sector on the storm day. The severity of ionospheric irregularities at different latitudes is revealed from the occurrence rate of the rate of change of TEC index (ROTI) variations. Further, the investigations of the hourly maximum absolute error (MAE) and root mean square error (RMSE) of ROTI from the reference quiet days’ levels and the timestamps of ROTI peak magnitudes substantiate the severity, latitudinal time lag in the peak of irregularity, and poleward expansion of EPBs and associated scintillations. The key findings from this study strengthen the understanding of evolution and the drifting characteristics of plasma irregularities over the Indian low latitudes.

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

Densely distributed Global Navigation Satellite System (GNSS) stations can invert the terrestrial water storage anomaly (TWSA) with high precision. However, the uneven distribution of GNSS stations greatly limits the application of TWSA inversion. The purpose of this study was to compensate for the spatial coverage of GNSS stations by simulating the vertical deformation in unobserved grids. First, a new deep learning weight loading inversion model (DWLIM) was constructed by combining the long short-term memory (LSTM) algorithm, inverse distance weight, and the crustal load model. DWLIM is beneficial for improving the inversion accuracy of TWSA based on the GNSS vertical displacement. Second, the DWLIM-based and traditional GNSS-derived TWSA methods were utilized to derive TWSA over mainland China. Furthermore, the TWSA results were compared with the TWSA solutions of the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) model. The results indicate that the maximum Pearson’s correlation coefficient (PCC), Nash–Sutcliffe efficiency (NSE) coefficient, and root mean square error (RMSE) equal 0.81, 0.61, and 2.18 cm, respectively. The accuracy of DWLIM was higher than that of the traditional GNSS inversion method according to PCC, NSE, and RMSE, which were increased by 67.11, 128.15, and 22.75%. The inversion strategy of DWLIM can effectively improve the accuracy of TWSA inversion in regions with unevenly distributed GNSS stations. Third, this study investigated the variation characteristics of TWSA based on DWLIM in 10 river basins over mainland China. The analysis shows that the TWSA amplitudes of Songhua and Liaohe River basins are significantly higher than those of the other basins. Moreover, TWSA sequences in each river basin contain annual seasonal signals, and the wave peaks of TWSA estimates emerge between June and July. Overall, DWLIM provides a useful measure to derive TWSA in regions where GNSS stations are uneven or sparse.

Spatiotemporal functional modeling of postseismic deformations after the 2011 Tohoku-Oki earthquake

Postseismic deformations continue to occur for a long period after major earthquakes. Temporal changes in postseismic deformations can be approximated using simple functions. Since the 2011 Tohoku-Oki earthquake, operating global navigation satellite system stations have been continuously accumulating a remarkable amount of relevant data. To verify the functional model, we performed statistical data processing on postseismic deformations due to this earthquake and obtained their spatiotemporal distribution. Moreover, we approximated the postseismic deformations over a relatively wide area with a standard deviation of residuals of 1 cm for approximately 10 years using a combined functional model of two logarithmic and one exponential functions; however, the residuals from the functional model exhibited a marked deviation since 2015. Although the pattern of postseismic deformations remained unaltered after the earthquake, a change in the linear deformation occurred from 2015 to date. We reduced the overall standard deviation of the residuals of > 200 stations distributed over > 1000 km to < 0.4 cm in the horizontal component by enhancing the functional model to incorporate this linear deformation. Notably, time constants of the functions were similarly applicable for all stations and components. Furthermore, the spatial distribution of the coefficients of each time constant were nonrandom, and the distribution was spatially smooth, with minute changes in the short wavelengths in space. Thus, it is possible to obtain a gridded model in terms of a spatial function. The spatial distributions of short- and long-period components of the functional model and afterslip and viscoelastic relaxation calculated using the physical model were similar to each other, respectively. Each time function revealed a connotation regarding the physical processes, which provided an understanding of the physical phenomena involved in seismogenesis. The functional model can be used to practical applications, such as discerning small variations and modeling for precise positioning.Graphical

Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica

Antarctica has a significant impact on global climate change. However, to draw climate change scenarios, there is a need for meteorological data, such as water vapor content, which is scarce in Antarctica. Global navigation satellite system (GNSS) networks can play a major role in overcoming this problem as the tropospheric delay that can be derived from GNSS measurements is an important data source for monitoring the variation of water vapor content. This work intends to be a contribution for improving the estimation of the zenith tropospheric delay (ZTD) obtained with the latest global pressure–temperature (GPT3) model for Antarctica through the use of long short-term-memory (LSTM) and radial basis function (RBF) neural networks for modifying GPT3_ZTD. The forecasting ZTD model is established based on the GNSS_ZTD observations at 71 GNSS stations from 1 January 2018 to 23 October 2021. According to the autocorrelation of the bias series between GNSS_ZTD and GPT3_ZTD, we predict the LSTM_ZTD for each GNSS station for period from October 2020 to October 2021 using the LSTM day by day. Based on the bias between LSTM_ZTD and GPT3_ZTD of the training stations, the RBF is adopted to estimate the LSTM_RBF_ZTD of the verified station, where the LSTM_ZTD represents the temporal forecasting ZTD at a single station, and the LSTM_RBF_ZTD represents the predicted ZTD obtained from space. Both the daily and yearly RMSE are calculated against the reference (GNSS_ZTD), and the improvement of predicted ZTD is compared with GPT3_ZTD. The results show that the single-station LSTM_ZTD series has a good agreement with the GNSS_ZTD, and most daily RMSE values are within 20 mm. The yearly RMSE of the 65 stations ranges from 6.4 mm to 32.8 mm, with an average of 10.9 mm. The overall accuracy of the LSTM_RBF_ZTD is significantly better than that of the GPT3_ZTD, with the daily RMSE of LSTM_RBF_ZTD significantly less than 30 mm, and the yearly RMSE ranging from 5.6 mm to 50.1 mm for the 65 stations. The average yearly RMSE is 15.7 mm, which is 10.2 mm less than that of the GPT3_ZTD. The LSTM_RBF_ZTD of 62 stations is more accurate than GPT3_ZTD, with the maximum improvement reaching 76.3%. The accuracy of LSTM_RBF_ZTD is slightly inferior to GPT3_ZTD at three stations located in East Antarctica with few GNSS stations. The average improvement across the 65 stations is 39.6%.

Development of InSAR Neutral Atmospheric Delay Correction Model by Use of GNSS ZTD and Its Horizontal Gradient

Interferometric Synthetic Aperture Radar (InSAR) often suffers from atmospheric disturbances due to the microwave propagation delay effect, which limits the surface displacement detection accuracy to an order of centimeters or more. Here I developed a new neutral atmospheric delay correction model for InSAR by using the global navigation satellite system (GNSS) zenith total delay (ZTD) and its horizontal gradient data. The proposed model at first retrieves the regularly gridded ZTD distribution at sea level and the linear height dependence from GNSS ZTD and gradient observations by the least squares method. Then, the gridded ZTD is projected onto the InSAR coordinate to correct the neutral atmospheric delay. I evaluated the correction model performance by applying it to L-band ALOS-2/PALSAR-2 ScanSAR interferograms over the Kanto plain in Japan. The correction result showed that by applying the proposed delay correction the phase standard deviation decreased by 33.87 % on average. By comparing it with the generic atmospheric correction online service for InSAR (GACOS) model and the correction by the Japanese regional mesoscale weather model (MSM), the proposed GNSS-based model outperformed others in my test case. The sensitivity test indicated that including the delay gradient could improve delay reproducibility under situations with fewer available GNSS stations. Although the proposed correction model&#x2019;s applicability depends on the number of available GNSS stations at the area of interest, the proposed model has a potential to effectively mitigate the neutral atmospheric delay and to improve the detection ability for smallamplitude surface displacements.

Assessment of tropospheric modeling on PPP-AR performances under brazilian atmospheric condition

In the Global Navigation Satellite System (GNSS), ambiguity resolution (AR) can shorten observation time and increase the positioning quality. The correct tropospheric modeling is intrinsically related to the ability to perform AR. Here, we assessed the influence of different tropospheric correction alternatives on AR for static Precise Point Positioning (PPP) in Brazilian territory. Our goal was to provide directions to users when choosing a suitable tropospheric model for application in PPP-AR under Brazilian atmospheric conditions. Thus, this study was carried out using well-known models such as the Saastamoinen model and the Zenith Tropospheric Delay (ZTD) Estimation and Numerical Weather Prediction (NWP) model from CPTEC/INPE. Six GNSS stations from the Brazilian Network for Continuous Monitoring (RBMC) (BRAZ, UFPR, RNNA, POVE, SMAR, and SAGA) were selected. Different GNSS processing setups were considered for GNSS data registered at selected stations during summer and winter. The assessment was based on a statistical analysis of positioning accuracy during one-hour sessions. The results indicated that such as the ZTD Estimation, the NWP model provides an accuracy of a few centimeters. On the other hand, the Saastamoinen model provided decimeter level accuracy, thus it is not the recommended choice for PPP-AR in the experimental conditions.

Undersampled Ionospheric Irregularity Threat Parameterization Using a Three-Dimensional Model for Satellite-Based Augmentation Systems

Single-frequency Satellite-based Augmentation Systems (SBASs) compute and broadcast estimates of vertical ionospheric delays and integrity bounds on the estimates called the Grid Ionospheric Vertical Errors (GIVEs) at Ionospheric Grid Points (IGPs). The dominant contribution of the GIVE comes from the undersampled ionospheric irregularity threat model. This paper presents a methodology for the undersampled ionospheric threat parameterization to reduce the magnitude of the GIVE. A threat model metric that measures the uniformity of angular separation of measurements was designed and incorporated into the current metric set, the fit radius and the relative centroid metric (RCM), such that threat geometries were parameterized rigorously based on the extended metric set. An undersampled threat model was constructed with the proposed three-dimensional metric set and historical ionospheric storm data from the global navigation satellite system stations in South Korea. We also simulated SBAS availability in the Korean region to demonstrate the benefit of the presented threat model methodology. In our preliminary assessment, the implementation of the proposed method was found to improve the 99.9&#x0025; availability of the approach procedure with vertical guidance I by up to 12&#x0025; when the approach was applied to the GIVE monitor algorithm.

Flash Floods Prediction Using Precipitable Water Vapor Derived From GPS Tropospheric Path Delays Over the Eastern Mediterranean

A flash flood is a rapid and intense response of a drainage area to heavy rainfall events. In the arid and semi arid parts of the Eastern Mediterranean (EM) region, the spatio-temporal distribution of rainfall is the most important factor for flash flood generation. A possible precursor to heavy rainfall events is the rise in tropospheric water vapor amount, which can be remotely sensed using ground based Global Navigation Satellite Systems (GNSS) stations. Here, we use the Precipitable Water Vapor (PWV) derived from 9 GNSS ground based stations in the arid part of the EM region in order to predict flash floods. Our approach includes using three types of Machine Learning (ML) models in a binary classification task which predicts whether a flash flood will occur given 24 hours of PWV data. We train our models with 107 unique flash flood events and vigorously test them using a nested cross validation technique. The results indicate a good agreement between all three types of models and across various score metrics. In addition, the models are further improved by adding more features such as surface pressure measurements. Finally, a feature importance analysis shows that the most important features are the PWV values from 2 to 6 hours prior a flash flood. These promising results indicate that it is possible to augment the current flash flood warning systems with a near real-time GNSS ground based data driven approach as demonstrated in this work.

A Two-Step Projected Iterative Algorithm for Tropospheric Water Vapor Tomography

The tropospheric tomography is an ill-posed inversion problem due to the sparsity of Global Navigation Satellite Systems (GNSS) stations and the limitation on the projection angles of GNSS signals, which in turn affects the stability and robustness of the tomographic solution. To address this, a new tomographic algorithm, named two-step projected iterative algorithm (TSPIA), is proposed. The wet refractivity (WR) field was constructed in two steps: first, an iterative preprocessing for the initial input values was performed, then its resultant solution was input into the projected iterative method, in which a hypothesis convex set was constructed to constrain the reconstruction based on the classical algebraic iterative reconstruction (AIR) methods. In addition, a two-dimensional normalized cumulative periodogram (2D-NCP) termination criterion was investigated since the traditional criteria for judging the convergence of iterations use pre-fixed empirical thresholds, which may lead to excessive iterations and need complicated work. The TSPIA was tested using GNSS data in Hong Kong over a wet period and a dry period. Statistical results showed that, compared to the classical AIR methods, the accuracy of the reconstructed WR field of the TSPIA were improved by about 10&#x0025; and 15&#x0025; when radiosonde and ECMWF data were used as the reference, respectively. Moreover, experiments for the proposed 2D-NCP criterion demonstrated noticeable computational efficiency. These results suggest that the new approaches proposed in this study can improve the performance of the iterative methods for GNSS tropospheric tomography.

Sea Surface States Detection in Polar Regions Using Measurements of Ground-Based GNSS Interferometric Reflectometry

This article analyzes the interferometric measurements of ground-based global navigation satellite systems (GNSSs) stations and proposes a novel method for sea surface states detection. The novel technique benefits from a cost-effective data collection from a large number of global GNSS stations. In this study, we extend a traditional GNSS interferometry reflectometry (GNSS-IR) model so that it can be applied to a multilayer surface by considering the surface roughness, total reflectivity, and penetration loss in multilayer situations. Based on this model, the wavelet analysis is used to perform parameterization on the interferometric observations represented by the signal to noise ratio (SNR). An integration factor and power curve are also proposed to characterize the surface state transition. One-year data from an Arctic geodetic GNSS station in the north of Canada are collected for analysis to validate the proposed approach in comparison with the existing methods based on the amplitude and damping factors. The results show that the new method demonstrates good usability and sensitivity to detect surface state transitions, e.g., icing, snowfall, and snow melting. However, the amplitude and damping factor-based methods derived from the single-layer model are only able to detect the pure ice surface and cannot respond to thick snow conditions. Finally, the high-resolution spaceborne images confirm the reliability of this method, exhibiting a great potential for long-term coastal sea surface detection based on the global geodetic GNSS stations and later being expected to be applied to sense cryosphere surface states.

Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

Precipitable water vapor (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations acquisition signal of a navigation satellite system provides high spatial and temporal resolution atmospheric water vapor. In this paper, an observation-nudging-based real-time four-dimensional data assimilation (RTFDDA) approach was used to assimilate the PWV estimated from GNSS observation into the WRF (Weather Research and Forecasting) modeling system. A landfall typhoon, “Mangkhut”, is chosen to evaluate the impact of GNSS PWV data assimilation on its track, intensity, and precipitation prediction. The results show that RTFDDA can assimilate GNSS PWV data into WRF to improve the water vapor distribution associated with the typhoon. Assimilating the GNSS PWV improved the typhoon track and intensity prediction when and after the typhoon made landfall, correcting a 5–10 hPa overestimation (too deep) of the central pressure of the typhoon at landfall. It also improved the occurrence and the intensity of the major typhoon spiral rainbands.

Adaptive Aerosol Optical Depth Forecasting Model Using GNSS Observation

As one of the important factors in atmospheric physical and chemical processes, aerosol optical depth (AOD) has an important impact on regional and global climate. Therefore, monitoring and predicting the temporal and spatial changes of AOD is of considerable significance. Existing methods mainly use a large number of meteorological parameters and ground observations to forecast AOD. However, modeling data are numerous and difficult to obtain practically. In this study, an adaptive AOD forecasting (AAF) model is proposed using the zenith total delay (ZTD) derived from global navigation satellite system (GNSS). This model only uses the ZTD as the external input parameter and considers the time autocorrelation of AOD for the previous epoch. In addition, AAF can adaptively adjust the model coefficients and has high accuracy. The AOD data derived from the Second Modern-era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Aerosol Robotic Network in the Beijing–Tianjin–Hebei (BTH, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$113^{\circ } 27^{\prime }$ </tex-math></inline-formula> E– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$119^{\circ } 50^{\prime }$ </tex-math></inline-formula> E, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$36^{\circ } 05^{\prime }$ </tex-math></inline-formula> N– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$42^{\circ } 40^{\prime }$ </tex-math></inline-formula> N) region over the period of 2015–2017 are used to perform the experiment. In addition, ZTD data of 16 GNSS stations in BTH region from the Crustal Movement Observation Network of China are selected to establish the AAF model. Experimental result reveals good performance of the proposed AAF model for internal and external validations. The difference in root mean square (rms), mean absolute error, and Bias of AOD between the AAF model and MERRA-2 are 0.11, 0.08, and 0.03, respectively. Compared with the existing AOD forecast models, the proposed AAF model is superior in terms of time resolution, rms, and correlation.

Monitoring the Water Vapor Content at High Spatio-Temporal Resolution Using a Network of Low-Cost Multi-GNSS Receivers

The Global Navigation Satellite System (GNSS) has the capacity for remote sensing of water vapor content in the atmosphere. Post-processing of GNSS data can provide Integrated Water Vapor (IWV) with accuracies comparable to measurements of traditional sensors, i.e. water vapor radiometers. While GNSS meteorology benefits from thousands of permanent GNSS stations operating worldwide the spatial resolution of GNSS-derived IWV is limited to tens of kilometers. Further densification of GNSS networks is achievable with low-cost GNSS receivers. We investigated the feasibility of low-cost multi-GNSS receivers for monitoring IWV. The post-processing and the real-time solution are validated against 1) the results from a geodetic-grade GNSS receiver, 2) co-located water vapor radiometer, and 3) numerical weather model (NWM). Despite the high variability of the IWV during the validation period, the standard deviation of IWV differences with respect to the water vapor radiometer was 1.0 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 1.5 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in post-processing and real-time, respectively. The city-scale variability of water vapor content in the atmosphere was monitored by a network of 16 low-cost GNSS receivers deployed in the city of Wroclaw, Poland. During rapidly changing weather conditions the disagreement between the low-cost GNSS-derived IWV field and the NWM reached up to 5.4 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and inter-station IWV differences exceeded 5 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It has been demonstrated that low-cost GNSS receivers are reliable tools for precise determination of IWV, also in real-time. This study is the first to measure the water vapor content with a spatial resolution of single kilometers and to present a significantly diversified city-scale IWV field.

Real-Time Rainfall Nowcast Model by Combining CAPE and GNSS Observations

Precipitable water vapor (PWV), derived from the Global Navigation Satellite System (GNSS), has contributed significantly to rainfall forecasting. However, another key parameter, convective available potential energy (CAPE), is strongly correlated with increases in extreme rainfall under the background of global warming but has rarely been investigated for rainfall forecasting. Therefore, a real-time rainfall nowcast (RRN) model that combines CAPE and PWV is proposed in this study. In addition, seasonal factors and the time autocorrelation of the predictors were considered. Here, the previous hourly PWV, CAPE, temperature, and rainfall were used to establish the RRN model and simulate/nowcast the next hourly rainfall based on the support vector regression, which was performed in a time span of five years at 23 GNSS stations in Taiwan Province under four designed schemes to validate the performance of the proposed RRN model. The average root mean square (RMS) and correlation coefficients of the proposed RRN model reached 0.34 mm/h and 0.96, respectively. Additionally, the linear relationships between the daily CAPE/PWV and extreme rainfall were investigated, revealing that PWV may contribute more to trigger extreme rainfall than CAPE. Finally, compared to existing quantitative rainfall forecast studies, the RRN model achieved significantly improved rainfall nowcast accuracy and thus has more potential applications in rainfall forecasting.

Hourly Rainfall Forecast Model Using Supervised Learning Algorithm

Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy.

PPK Kit를 활용한 드론 측량 분석

With the popularization of drones and the ease of use of the Global Navigation Satellite System (GNSS), drone photogrammetry for terrain information has been widely used. Drone photogrammetry enables the realization of high-accuracy three-dimensional topography for the entire area with less effort and time compared to the past direct survey using GNSS or total station. From 3-D topographic data, various topographical analysis is possible. To improve the accuracy of drone photogrammetry, direct GCP surveying in the field is essential, and the numbers and reasonable positioning of GCPs are very important. In the case of beaches or tidal flats on the west coast of Korea, the numbers and location of GCPs are important factors in efficient drone photogrammetry because of the size of the area, difficulties of movement, and the risk from tides. If the RTK (Real-time kinematic) or PPK (Post-processed kinematic) method is used, the increased accuracy of the drone''s location enables high-accuracy photogrammetry with a small number of GCPs. This study presents an efficient drone photogrammetry method in terms of time and economy by comparing and analyzing the results of drone photogrammetry using Non-PPK with low-cost PPK-Kit, based on the tests of various numbers and locations of GCPs in the university field including various slopes and structures like coastal terrain.

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical models. This work aims to find a model that connects raw meteorological parameters with the GNSS residuals. The approach is to train a Temporal Convolutional Network (TCN) on 206 GNSS stations in central Europe, after which the resulting model is applied to 68 test stations in the same area. When comparing the Root Mean Square (RMS) error reduction of the time series reduced by physical models, and, by the TCN model, the latter reduction rate is, on average, 0.8% lower. In a second experiment, the TCN is utilized to further reduce the RMS of the time series, of which the loading models were already subtracted. This yields additional 2.7% of RMS reduction on average, resulting in a mean RMS reduction of 28.6% overall. The results suggests that a TCN, using meteorological features as input data, is able to reconstruct the reductions almost on the same level as physical models. Trained on the residuals, reduced by environmental loadings, the TCN is still able to slightly increase the overall reduction of variations in the GNSS station position time series.