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

Troposphärische Parameterbestimmung auf Basis von GNSS Messdaten von Zügen

Abweichender Titel nach Übersetzung der Verfasserin/des VerfassersWater vapour denotes one of the most important parameters utilized for describing the state and evolution of the atmosphere. Therefore, detailed knowledge of its distributionis of immense importance for weather forecasting. Furthermore, it is also the most effective greenhouse gas and highly variable in both space and time.Thus, it is obvious that high resolution observations are crucial for accurate precipitation forecasts,especially for short-term prediction of severe weather. Although not intentionally built for this purpose, Global Navigation Satellite Systems (GNSS) have proven to meet those requirements. The derivation of meteorological parameters from GNSS observations is based on the fact that electromagnetic signals are delayed when travelling through the atmosphere. Parametrisations of these delays, most prominently the Zenith Total Delay (ZTD) parameter, have been studied extensively and proven to provide substantial benefits for atmospheric research and especially Numerical Weather Prediction (NWP) model performance. Typically, regional/global networks of static reference stations are utilized to derive ZTD along with other parameters of interest in GNSS analysis (e.g.station coordinates). Results are used as a contributing data source for determining the initial conditions of NWP models, a process referred to as Data Assimilation (DA).This thesis goes beyond the approach outlined above, showing how reasonable tropospheric parameters can be derived from highly-kinematic, single-frequency (SF) GNSS data. This data was gathered on trains of the Austrian Federal Railways ( ̈OBB)and processed using the Precise Point Positioning (PPP) technique.The specialnature of the observations yields a number of additional challenges, from appropriatepre-processing and extended outlier detection, to advanced strategies in the PPPsetup and for usage in DA procedures. Furthermore, since only SF data is provided,the treatment of the ionosphere represents one of the major challenges of this study.Therefore different strategies have been investigated and shown to provide satisfactory results, suitable for ZTD estimation.Despite these challenging circumstances, reasonable results for ZTD estimates could be obtained for the analysed test cases investigating different PPP processing strategies.For validation of the results, comparisons with ZTD calculated using data from ERA5,the latest reanalys is of the European Centre for Medium-Range Weather Forecasts (ECMWF), were carried out. They yield very high correlation and an overall agreement from the low millimetre-range up to 5 centimetre, depending on solution and analysed travelling track. First tests of assimilation into a NWP model again show the reasonable quality of the results as between 30-100 % of the observations are accepted by the model. Furthermore guidelines to an operational processing and possible extensionsto advanced tropospheric parameters were outlined, in order to exploit the huge benefits (horizontal/temporal resolution) of this specific dataset for operational weather forecasting.18

Atmosphärische Anregung der mehrjährigen Polbewegung aus meteorologischen Reanalysen des 20. Jahrhunderts

Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheDer Ursprung von dekadischen Variationen in der Anregung der Polbewegung stellt eines der letzten großen Mysterien auf dem Gebiet der Erdrotation dar. Seit William Markowitz den multi-dekadischen Wobble entdeckte, welcher später nach ihm benannt wurde, versuchen Geodäten dessen Ursache auf den Grund zu gehen. Auch wenn heutzutage eine Kombination aus mehreren Prozessen am wahrscheinlichsten erscheint und die Prozesse im Erdkern wohl den Hauptanteil bilden so fehlt immer noch das vollkommene Verständnis für alle Zusammenhänge. Atmosphärische Prozesse stellen dabei auch einen kleinen Anteil dar. Diese Arbeit beschäftigt sich mit dem atmosphärischen Einfluss auf die dekadische Anregung der Polbewegung und bedient sich dabei meteorologischer Daten aus zwei verschiedenen Reanalysemodellen des 20. Jahrhunderts. Einerseits soll die Größenordnung des atmosphärischen Anteils geschätzt werden, andererseits werden im Zuge dieses Prozesses die verwendeten Modelle auf ihre Konsistenz und ihre Einsetzbarkeit in geophysikalischen Studien zur Erdrotation getestet. Schlussendlich soll damit eine objektive Bewertung möglich sein und eventuelle Vor- und Nachteile der einzelnen Modelle sollen aufgedeckt werden.The origin of decadal variations in the excitation of polar motion occurs to be one of the remaining open questions in Earth rotation studies. SinceWilliam Markowitz discovered the multi-decadal wobble that has been named after him, geodetic science has been in search of the possible underlying physical mechanisms for it. Although a combination of different processes is the most likely scenario and most studies suggest processes in the core to account for the main contribution, a complete picture of the whole phenomenon is still missing. Atmospheric processes, although of subordinate magnitude, also take part in decadal polar motion excitation. The present study investigates this decadal-scale atmospheric excitation over the whole twentieth century by using meteorological data from two different reanalysis systems. On one hand the thesis estimates the atmospheric contribution to decadal-scale wobbles by comparing geophysical excitation measures to geodetic observations of polar motion variations. On the other hand two reanalysis models are tested for their rational skill and consistency trough the angular momentum budget equation, i.e., the mathematical framework that is the foundation of a reliable estimation of the atmospheric contribution. In the end, a objective judgement on the usability of the reanalyses for Earth rotation studies is given, and the possible superiority of one of the probed datasets is pointed out. The atmospheric contribution is found to be small but not negligible. Beside that, good results in the angular momentum budget check justify the usage of both reanalyses models.7

Usability of high-resolution GNSS-ZTD data in the AROME model

by Matthias Aichinger-RosenbergerUniversity of Innsbruck, Masterarbeit, 2018(VLID)282533

Detection of Alpine Foehn in GNSS-ZWD time series: An innovative application of GNSS Meteorology

The atmospheric delay experienced by a signal of the Global Navigation Satellite System (GNSS) is proportional to the water vapour content along the signal path. This fact is typically exploited in GNSS Meteorology by introducing GNSS derived atmospheric parameters like the Zenith Wet Delay (ZWD) in data assimilation schemes. In numerous studies, the positive impact on the (especially precipitation) forecast has been demonstrated. However, while mostly precipitation-related studies represent the current focus of research, other meteorological phenomena might also be investigated by means of GNSS. The present study represents an initial investigation on the detection of another important meteorological phenomena using GNSS time series: Foehn winds. Foehn denotes a gusty, warm fall wind occurring in mountainous regions worldwide, leading to a relatively mild climate in affected areas. On the other hand, Foehn can also be characterized as severe weather leading to disasters, due to the high wind speeds frequently encountered. The proposed detection method of Foehn in ZWD time series is based on the significant drying/wetting effects on the lee/luv side of an affected mountain range associated with Foehn. The comparison of ZWD from stations on both sides of the main Alpine ridge reveals characteristic features like distinctive ZWD minima/maxima and significant decrease in correlation between the stations. In this study we investigate a number of well-documented Foehn events in the Swiss Alps (therefore called Alpine Foehn) using ZWD time series from the Automated GNSS Network Switzerland (AGNES) station network, operated by the Swiss Federal Office of Topography (swisstopo). Based on these case studies, an assessment of the usability of GNSS-ZWD for Foehn detection is presented and possible strengths and weaknesses will be analysed. Finally, an outlook on possible improvements and innovative extensions to the presented approach is given. These range from embedment of ZWD data in operational Foehn classification and the application of Machine-Learning techniques for detection, to the establishment of collocated GNSS/weather stations, which come with a number of scientific benefits - not only for Foehn investigations but GNSS Meteorology in general.ISSN:1812-705

Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains

Electromagnetic signals, as broadcasted by Global Navigation Satellite Systems (GNSS), are delayed when travelling through the Earth’s atmosphere due to the presence of water vapour. Parametrisations of this delay, most prominently the Zenith Total Delay (ZTD) parameter, have been studied extensively and proven to provide substantial benefits for atmospheric research and especially the Numerical Weather Prediction (NWP) model performance. Typically, regional/global networks of static reference stations are utilized to derive ZTD along with other parameters of interest in GNSS analysis (e.g. station coordinates). Results are typically used as a contributing data source for determining the initial conditions of NWP models, a process referred to as Data Assimilation (DA). This contribution goes beyond the approach outlined above as it shows how reasonable tropospheric parameters can be derived from highly kinematic, single-frequency (SF) GNSS data. The utilized data was gathered at trains by the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. Although the special nature of the observations yields several challenges concerning data processing, we show that reasonable results for ZTD estimates can be obtained for all four analysed test cases by using different PPP processing strategies. Comparison with ZTD calculated from ERA5 reanalysis data yields a very high correlation and an overall agreement from the low millimetre-range up to 5 cm, depending on solution and analysed travelling track. We also present the first tests of assimilation into a numerical weather prediction (NWP) model which show the reasonable quality of the results as between 30-100 % of the observations are accepted by the model. Furthermore, we investigate means to transfer the developed ideas to an operational stage in order to exploit the huge benefits (horizontal/temporal resolution) of this special dataset for operational weather forecasting

Kinematic ztd estimation from train-borne single-frequency gnss: Validation and assimilation

Water vapour is one of the most important parameters utilized for the description of state and evolution of the Earth’s atmosphere. It is the most effective greenhouse gas and shows high variability, both in space and time. Thus, detailed knowledge of its distribution is of immense importance for weather forecasting, and therefore high resolution observations are crucial for accurate precipitation forecasts, especially for the short-term prediction of severe weather. Although not intentionally built for this purpose, Global Navigation Satellite Systems (GNSS) have proven to meet those requirements. The derivation of water vapour content from GNSS observations is based on the fact that electromagnetic signals are delayed when travelling through the atmosphere. The most prominent parameterization of this delay is the Zenith Total Delay (ZTD), which has been studied extensively as a major error term in GNSS positioning. On the other hand, the ZTD has also been proven to provide substantial benefits for atmospheric research and especially Numerical Weather Prediction (NWP) model performance. Based on these facts, the scientific area of GNSS Meteorology has emerged. The present study goes beyond the current status of GNSS Meteorology, showing how reasonable estimates of ZTD can be derived from highly-kinematic, single-frequency (SF) GNSS data. This data was gathered from trains of the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. The special nature of the observations yields a number of additional challenges, ranging from appropriate pre-processing and parameter settings in PPP to more sophisticated validation and assimilation methodologies . The treatment of the ionosphere for SF-GNSS data represents one of the major challenges of this study. Two test cases (train travels) were processed using different strategies and validated using ZTD calculated from ERA5 reanalysis data. The validation results indicate a good overall agreement between the GNSS-ZTD solutions and ERA5-derived ZTD, although substantial variability between solutions was still observed for specific sections of the test tracks. The bias and standard deviation values ranged between 1 mm and 8 cm, heavily depending on the utilized processing strategy and investigated train route. Finally, initial experiments for the assimilation of GNSS-ZTD estimates into a NWP model were conducted, and the results showed observation acceptance rates of 30–100% largely depending on the test case and processing strategy.ISSN:2072-429

Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland

Remote sensing of water vapour using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for numerical weather prediction (NWP). However, one of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima and maxima as well as a significant decrease in the correlation between the stations. However, detecting such signals becomes increasingly difficult for large datasets. Therefore, we suggest the application of machine learning algorithms for the detection and prediction of foehn events by means of GNSS troposphere products. This initial study develops a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products. Data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries as well as records of an operational foehn index are used to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine tuned and tested in four dedicated experiments using different feature setups. The results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used. Detection- and alarm-based measures reach levels between 66 %-80 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extent by provision of additional NRT products and improved data processing in the future. Results also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets.ISSN:1867-1381ISSN:1867-854