Detecting Signatures of Convective Storm Events in GNSS‐SNR: Two Case Studies From Summer 2021 in Switzerland

Global Navigation Satellite Systems (GNSS) are not only a state-of-the-art sensor for positioning and navigation applications but also a valuable tool for remote sensing. Through the usage of L-band carrier frequencies, GNSS acts as an all-weather-operation system, offering substantial benefits compared to optical systems. Nevertheless, severe weather can still have an impact on the strength of signals received at a ground station, as we show in this study. We investigate GNSS Signal-to-Noise Ratio (SNR) observations during two severe convective storm events over the city of Zurich, Switzerland. We make use of a GNSS-SNR-based algorithm originally developed for the detection of hail particles from volcanic eruptions. Results indicate that, although GNSS observations are considered to be fairly insensitive to the presence of hydrometeors, convective storm events are visible in SNR observations. SNR levels of affected satellites show a significant drop during event periods, which are determined by weather radar observation

Deep ensemble geophysics-informed neural networks for the prediction of celestial pole offsets

Celestial Pole Offsets (CPO), denoted by dX and dY, describe the differences in the observed position of the pole in the celestial frame with respect to a certain precession-nutation model. Precession and nutation components are part of the transformation matrix between terrestrial and celestial systems. Therefore, various applications in geodetic science such as high-precision spacecraft navigation require information regrading precession and nutation. For this purpose, CPO can be added to the precession-nutation model to precisely describe the motion of the celestial pole. However, as Very Long Baseline Interferometry (VLBI) – currently the only technique providing CPO – requires long data processing times resulting in several weeks of latency, predictions of CPO become necessary. Here we present a new methodology named Deep Ensemble Geophysics-Informed Neural Networks (DEGINNs) to provide accurate CPO predictions. The methodology has three main elements: (1) deep ensemble learning to provide the prediction uncertainty; (2) broad-band Liouville equation as a geophysical constraint connecting the rotational dynamics of CPO to the atmospheric and oceanic Effective Angular Momentum functions (EAM); and (3) coupled oscillatory recurrent neural networks to model the sequential characteristics of CPO time series, also capable of handling irregularly-sampled time series. To test the methodology, we use the newest version of the final CPO time series of International Earth Rotation and Reference Systems Service (IERS), namely IERS 20 C04. We focus on a forecasting horizon of 90 days, the practical forecasting horizon needed in space-geodetic applications. Furthermore, for validation purposes we generate an independent global VLBI solution for CPO since 1984 up to the end of 2022 and analyze the series. We draw the following conclusions. First, the prediction performance of DEGINNs demonstrates up to 25% and 33% improvement respectively for dX and dY, with respect to the rapid data provided by IERS. Second, predictions made with the help of EAM are more accurate compared to those without EAM, thus providing a clue to the role of atmosphere and ocean on the excitation of CPO. Finally, free core nutation period shows temporal variations with a dominant periodicity of around one year, partially excited by EAM.Santiago Belda was partially supported by Generalitat Valenciana (SEJIGENT/2021/001), the European Union—NextGenerationEU (ZAMBRANO 21-04) and Ministerio de Ciencia e Innovación (MCIN/AEI/10.13039/501100011033/). Maria Karbon was supported by PROMETEO/2021/030 funded by Generalitat Valenciana

VGOS VLBI Intensives between MACGO12M and WETTZ13S for the rapid determination of UT1-UTC

In this work, we present a status update and results of the designated research and development VLBI Intensive program VGOS-INT-S, observed between MACGO12M and WETTZ13S for the rapid determination of the Earth's phase of rotation, expressed via UT1-UTC. The main novelty of these sessions is the use of a special observation strategy, rapidly alternating between high- and low-elevation scans, enabling an improved determination of delays caused by the neutral atmosphere. Since 2021, 25 Intensive sessions have been observed successfully. In early 2022, VGOS-INT-S was among the most accurate Intensive programs with an average formal error $\sigma_{UT1-UTC}$ of 3.1 $\mu$s and a bias w.r.t. IERS C04 of 1.1 $\mu$s. Later, the session performance decreased due to multiple technical difficulties.Comment: 8 pages, 5 figure

Social exclusion and transportation in Peachtree City, Georgia

This paper will discuss how, in a small American city, Peachtree City (43km south of Atlanta), the flexibility and relative affordability of electric golf carts, as a viablealternative to the automobile, means that the level at which families and individuals are disadvantaged through their lack of access to public/private transport is effectively lowered. Economic access to golf carts, in of itself, would not be sufficient if it were not for the extensive, highly penetrative and 'ringy' spatial structure of the cart path system, a mostly-segregated, 150 kilometre network. A spatial analysis of this dual transportation system is presented and its implications discussed. The conclusion of this paper is that the duality of the effective spatial structure of the cart path networkand the relative low cost and inherent flexibility of the golf carts combine to reduce transportation-linked social exclusion in Peachtree City. This argument is substantiated, in the final section of the paper, through the evidence of a questionnairedistributed to a random sampling of 1,038 property owners and renters in the city

Evaluating the feasibility of short-integration scans based on the 2022 VGOS-R&D program

In this work, we report on activities focusing on improving the observation strategy of the Very Long Baseline Interferometry (VLBI) Global Observing System (VGOS). During six dedicated 24-hour Research and Development (R&D) sessions conducted in 2022, the effectiveness of a signal-to-noise ratio (SNR)-based scheduling approach with observation times as short as 5-20 seconds was explored. The sessions utilized a full 8 Gbps observing mode and incorporated elements such as dedicated calibration scans, a VGOS frequency source-flux catalog, improved sky-coverage parameterization, and more. The number of scans scheduled per station increased by 2.34 times compared to operational VGOS-OPS sessions, resulting in a 2.58 times increase in the number of observations per station. Remarkably, the percentage of successful observations per baseline matched the fixed 30-second observation approach employed in VGOS-OPS, demonstrating the effectiveness of the SNR-based scheduling approach. The impact on the geodetic results was examined based on statistical analysis, revealing a significant improvement when comparing the VGOS-R\&D program with VGOS-OPS. The formal errors in estimated station coordinates decreased by 50 %. The repeatability of baseline lengths improved by 30 %, demonstrating the enhanced precision of geodetic measurements. Furthermore, Earth orientation parameters exhibited substantial improvements, with a 60 % reduction in formal errors, 27 % better agreement w.r.t. IVS-R1/R4, and 13 % better agreement w.r.t. IERS EOP 20C04. Overall, these findings strongly indicate the superiority of the VGOS-R&D program, positioning it as a role model for future operational VGOS observations

Observations of radio sources near the Sun

Geodetic Very Long Baseline Interferometry (VLBI) data are capable of measuring the light deflection caused by the gravitational field of the Sun and large planets with high accuracy. The parameter $\gamma$ of the parametrized Post-Newtonian (PPN) formalism estimated using observations of reference radio sources near the Sun should be equal to unity in the general relativity. We have run several VLBI experiments tracking reference radio sources from 1 to 3 degrees from the Sun. The best formal accuracy of the parameter $\gamma$ achieved in the single-session mode is less than 0.01 percent, or better than the formal accuracy obtained with a global solution included all available observations at arbitrary elongation from the Sun. We are planning more experiments starting from 2020 using better observing conditions near the minimum of the Solar activity cycle.Comment: Proceeding of the EVGA 2019 Meeting. arXiv admin note: substantial text overlap with arXiv:1806.1129

Current Status of the EU-VGOS Project

The EU-VGOS project began in 2018 with\ua0the aim of using the VGOS infrastructure in Europe\ua0to investigate methods for VGOS data processing. The\ua0project is now structured into Working Groups dealing\ua0with operations (stations), e-transfer, correlation and\ua0post-processing, and analysis. This is a report on the\ua0status of the project

Application of Kalman Filtering in VLBI Data Analysis

Zusammenfassung in englischer SpracheAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersVery Long Baseline Interferometry (VLBI) ist eines der fundamentalen geodätischen Weltraumverfahren. Wichtige Ziele für die nächste Generation an VLBI-Technologie sind die kontinuierliche Durchführung von Beobachtungen und eine automatische Datenverarbeitung. Zu diesem Zwecke ist es notwendig, echtzeitfähige Parameterschätzungsalgorithmen, wie das Kalman-Filter, in der VLBI-Auswertung einzuführen. Im Rahmen dieser Arbeit wurde ein solches Filter in die VLBI-Software VieVS@GFZ implementiert und verschiedenste Aspekte in Bezug auf die Prozessierung von VLBI-Daten untersucht. Innerhalb des entsprechenden Moduls VIE_KAL ist es u.a. möglich, alle in der VLBI-Auswertung gängigen Parameter zu schätzen, deren stochastische Modelle anzupassen, flexibel das Datum zu definieren, externe Daten zu integrieren sowie datumsfreie Normalgleichungen zu extrahieren. Der Fokus der Untersuchungen wurde auf den Einfluss der Troposphäre, der wichtigsten Fehlerquelle in der VLBI-Auswertung, und auf die Bestimmung von Stationspositionen, welche in der Geodäsie von wesentlicher Bedeutung sind, gelegt. Für die stochastische Modellierung der troposphärischen Laufzeitverzögerungen wurden stations- und zeitabhängige Unterschiede berücksichtigt. In Vergleichen mit troposphärischen Parametern aus GNSS, Wasserdampfradiometern und numerischen Wettermodellen wies die Kalman-Filter-Lösung um 5 bis 15% geringere Differenzen als eine Kleinste-Quadrate-Lösung auf, die auf denselben Modellen und VLBI-Daten basierte. Auch in Bezug auf geschätzte Stationskoordinaten wies die Kalman-Filter-Lösung bessere Basislinienlängen- und Koordinatenwiederholbarkeiten auf. Die Anwendung des stationsabhängigen Prozessrauschens brachte eine zusätzliche Verbesserung. Des Weiteren wurde das Kalman-Filter dazu verwendet, subtägliche Stationskoordinatenvariation aufgrund von Gezeiten und Auflasteffekten zu bestimmen. Schließlich wurden die gewonnenen Erkenntnisse dazu verwendet, Kalman-Filter-basierte globale terrestrische Referenzrahmen (TRF) zu bestimmen. Für die stochastische Modellierung der Koordinatenvariationen einzelner Stationen wurden Auflastdeformationszeitreihen herangezogen. Durch den nichtdeterministischen Ansatz des Filters war es möglich, nichtlineare Positionsbewegungen, verursacht z.B. durch unregelmäßige saisonale Effekte oder postseismische Deformationen, zu berücksichtigen. In Vergleichen mit einer VLBI-TRF-Lösung mittels einer klassischen Ausgleichung und dem ITRF2008 zeigten sich gute Übereinstimmungen in Bezug auf die Transformationsparameter und Stationsgeschwindigkeiten. Das Testen verschiedener Optionen bezüglich der Parametrisierung und stochastischen Modellierung führte zu Erkenntnissen, wie zukünftige Referenzrahmen verbessert werden können.Very long baseline interferometry (VLBI) is one of the fundamental space geodetic techniques. Important goals for the next generation of VLBI technology are continuous operations as well as automated data processing. For this reason, it is necessary to introduce real time capable parameter estimation algorithms, such as Kalman filters, to VLBI data analysis. In this study, such a filter was implemented in the VLBI software VieVS@GFZ, and several aspects related to VLBI data processing were investigated. Within the corresponding module VIE_KAL it is possible, for example, to estimate all parameters important in VLBI analysis, adapt their stochastic models, flexibly define the datum, integrate external data, as well as extract datum free normal equations. The foci of the investigations were on the effects of the troposphere, the most important error source in VLBI analysis, and on the determination of station positions, which are of great importance in geodesy. For the stochastic model of the tropospheric delays, station- and time-dependent differences were considered. In comparisons with tropospheric parameters from GNSS, water vapor radiometers and numerical weather models, the Kalman filter solution yielded 5 to 15% smaller differences than a least squares solution based on the same models and VLBI data. Also in the case of estimated station coordinates, the Kalman filter solution exhibited better baseline length and station coordinate repeatabilities. The application of station-based process noise led to additional improvements. Furthermore, the Kalman filter was used to estimate subdaily station coordinate variations caused by tidal and loading effects. Finally, the findings were used to determine Kalman-filter-based global terrestrial reference frames (TRFs). For the stochastic model of the coordinate variations of particular stations, loading deformation time series were utilized. The non-deterministic approach of the Kalman filter allowed the consideration of non-linear station movement, for example, due to irregular seasonal effects or post-seismic deformations. In comparisons with a VLBI TRF solution from a classical adjustment and ITRF2008, a good agreement in terms of transformation parameters and station velocities was achieved. The findings from testing different options related to the parameterization and to the stochastic model will help to improve future reference frames.15