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Tropospheric Parameter Estimation Based on GNSS Tracking Data of A DISSERTATION Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains Ausgeführt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Wissenschaften unter der Leitung von Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Robert Weber am E 120-4 Department für Geodäsie und Geoinformation eingereicht an der Technischen Universität Wien Fakultät für Mathematik und Geoinformation von Matthias Aichinger-Rosenberger Matr.Nr: 00926550 Ried 14/7 6306 Söll Wien, Februar 2021 (Unterschrift Verfasser/in) (Unterschrift Betreuer/in) i “Wonach du sehnlich ausgeschaut, Es wurde dir beschieden. Du triumphierst und jubelst laut Jetzt hab ich endlich Frieden! Ach, Freundchen, rede nicht so wild, Bez¨ahme deine Zunge! Ein jeder Wunsch, wenn er erf¨ullt, Kriegtaugenblicklich Junge.” —Wilhelm Busch (1832-1908) ii Acknowledgements Theverse on the previous page, composed by thefamousGermanwriterWilhelm Busch, Ihad stumbled upon awhile agoinsomeold bookathome. From there on,I knew thatIneeded to add it to this thesis as an opening line since there’spractically no better way of describing myacademiccareer over the years. And as I’mwriting this and therebyfinish this chapter,there’s already somethingnew and exciting coming up forthe future. Nevertheless before that is nowthe time to thank everybodywho made this thesis and my PhD studies possible. First and foremost, Iwouldliketothank Prof. Robert Weber, who served as my supervisorand mentor not only forthis thesis but at allaspectsofuniversitylife. Thanks forgiving me the opportunity to jointhe research divisionand pursue such an interesting PhDstudy. Furthermore thanks to: Johannes B¨ohm,again forthe chance to start an academic career at TU Wien and forcompetent advice with his expertknowledge in atmosphericmodelling and space geodesy. MarcusFranzGlaner,for thegreatcamaraderieand collaborationatworkand numerous precious advices and discussions on GNSS,PPP andeven notvery ”work-specific passions” likedistance running. GregorM¨o ller, foralot of expert advice on all thingsconcerning GNSS (and GNSS Meteorology in particular) as well as the greatcollaboration not limitedbyour relatively short time at theinstitutetogether. Natalia Hanna, foralot of expertadvice on dataassimilationand carrying out the assimilation testsfor thisthesis. Zoreh Adavi,for providing ray-traced troposphericdelays forthe validation of the results. In generaltoall other colleagues at the research divisionand the institutewho Ihad thepleasuretoknowand workwith. Although my time thereended already ayearago, Ireally enjoyedevery dayofitand will alwaysbegrateful forthisexperience. iii iv Finally Iwould liketothankmyfamily: Eva, foryourlove andcontinuous support, withoutwhich this thesis would not have been possible/finished in such ashort timeframe. Ilove you. Paula and Fiona, the greatest giftsofmylife. Iloveyou too. My parents, brothers and all other relatives who supportedmeand kept me motivated over all those yearsI’ve nowspent studying. Abstract Watervapourdenotes one of the most importantparametersutilizedfor describing the stateand evolution of the atmosphere. Therefore, detailed knowledgeofits distribution is of immense importance forweather forecasting. Furthermore, it is also themost effective greenhouse gas and highlyvariable in both space andtime. Thus, it is obvious that high resolutionobservations arecrucialfor accurate precipitation forecasts, especially forshort-termpredictionofsevereweather. Althoughnot intentionally built forthis purpose, GlobalNavigation Satellite Systems(GNSS)have proventomeet those requirements. The derivation of meteorologicalparameters fromGNSSobservations is based on the fact that electromagnetic signals aredelayedwhen travelling through the atmosphere. Parametrisations of these delays, most prominentlythe Zenith Total Delay (ZTD) parameter, havebeen studied extensively and proventoprovide substantial benefits foratmospheric research and especially NumericalWeather Prediction(NWP) model performance.Typically,regional/globalnetworks of static reference stationsare utilizedtoderiveZTD alongwith other parametersofinterest in GNSS analysis (e.g. station coordinates).Results areused as acontributing data source fordetermining the initialconditions of NWP models, aprocess referred to as Data Assimilation (DA). Thisthesisgoesbeyond the approach outlined above, showing howreasonable troposphericparameters canbederived from highly-kinematic, single-frequency (SF) GNSSdata. This data wasgathered on trains ofthe Austrian FederalRailways(OBB¨ ) and processed using the Precise Point Positioning (PPP) technique. The special natureofthe observations yieldsanumber of additionalchallenges, from appropriate pre-processing and extended outlier detection, to advanced strategies in the PPP setup and forusage in DA procedures. Furthermore, since only SF dataisprovided, the treatmentofthe ionosphere represents one of the major challenges of thisstudy. Therefore different strategies havebeeninvestigated and shown to provide satisfactory results, suitable forZTD estimation. Despite thesechallenging circumstances, reasonable results forZTD estimatescould be v vi obtained forthe analysed test cases investigating different PPP processing strategies. For validation of the results, comparisons with ZTD calculated using data from ERA5, the latest reanalysis of the European Centre forMedium-RangeWeather Forecasts (ECMWF),werecarried out. They yield very highcorrelation and an overall agreement fromthe lowmillimetre-rangeupto5centimetre, depending on solution and analysed travelling track. First tests of assimilation into aNWP modelagain showthe reasonable qualityofthe resultsasbetween 30-100 %ofthe observations areaccepted by the model. Furthermore guidelines to an operationalprocessingand possibleextensions to advancedtroposphericparameters were outlined, in order to exploitthe huge benefits (horizontal/temporal resolution)ofthis specific dataset foroperational weather forecasting. Kurzfassung Atmosph¨a rischem Wasserdampf kommtdurch seineRolle als wichtigstesTreibhausgas und seine gleichzeitig r¨aumlichund zeitlich hoch variable Verteilung eine besondere Bedeutung inder Meteorologie zu.Dadurch ist auch verst¨a ndlich dassein m¨oglichst dichtes Beobachtungsnetz f¨ur die Niederschlagsvorhersage von entscheidender Bedeu- tung ist.Wenn auch urspr¨unglich nicht zu diesem Zweck entwickelt,k¨o nnen Global Navigation SatelliteSystems (GNSS)hier Abhilfe schaffen. Die Bestimmung von atmosph¨a rischen Wasserdampfmengen mit GNSS basiert dabei auf dem Sachverhalt dass elektromagnetische Signaleauf ihremWeg durch die Atmosph¨a re durch die Pr¨a senz vonWasserdampf verz¨o gert werden. Parametrisierungen dieser Verz¨ogerung, wie der Zenith Total Delay(ZTD),k¨o nnen aus Netzwerken fixer Stationen in der GNSSAuswertung, zusammen mit z.B. Stationskoordinaten, bestimmt werden. Die gewonnenen Resultatewerden typischerweise alskomplement¨a rer Datensatzzur Bestimmung der Anfangsbedingungen in numerischen Wettermodellen verwendet, ein Prozessder Datenassimilation bezeichnet wird. Die vorliegende Arbeit geht ¨u berdiese Ans¨a tze hinausindem sie die Nutzbarkeit von Einfrequenz-GNSS-Daten, welche in einem hoch kinematischen Umfeld beobachtet wurden, zur Bestimmung vonZTD zeigt. Dabei handelt es sich um Lokalisierungs- daten vonZ¨u gen der Oster¨ reichischen Bandesbahnen (OBB¨ ), welche mittels Precise PointPositioning (PPP) ausgewertet wurden. Die große Anzahl der bereitsmit GNSSEmpf¨a ngernausgestatteten Z¨uge verspricht dabei eine noch nieerreichte r¨aumliche (und auch zeitliche) Aufl¨osung vonZTD Daten ¨u berganz Oster¨ reich. Die speziellen Eigenschaften der Daten bringen allerdings eine Reihe vonHerausforderungen mitsich, voneiner deutlich aufwendigeren Ausreißerkorrektur bishin zu speziellen Prozessierungs- und Assimilierungsans¨a tzenwelche in dieser Arbeit getestet wurden. Durch die Verwendung vonEinfrequenz-Daten kommt der Korrektur von ionosph¨a rischen Laufzeitverz¨ogerungen eine besondershohe Bedeutung zu.Aus diesem Grund wurden hierf¨u rmehrere verschiedene Ans¨a tze getestet, welche alle zufriedenstellende Ergebnisse vii viii liefern konnten. Trotz dieser herausfordernden Voraussetzungenkonnten durchwegs realistische Ergeb- nisse f¨ur dieanalysierten Testf¨alleerzielt werden. Vergleiche mit ausatmosph¨arsichen Reanalyse-Daten (ERA5)des European Centre forMedium-Range Weather Forecasts (ECMWF) gerechneten ZTDs zeigen eine sehr hohe Korrelation und mittlere Abwe- ichungen vomunteren mm-Bereichbis zu 5cm, abh¨a ngigvon verwendeterSoftwareund Teststrecke. Erste Tests von Assimilation in ein numerisches Wettermodell zeigendass, abh¨a ngig vonL¨o sung und Strecke, 30 -100%der Beobachtungen vomModell akzeptiert werden.Dar¨u berhinaus wurde auch noch eine Strategief¨u rdie Implementierung dieser Idee im operationellen Betrieb entwickelt bzw. vorgeschlagen, um die großen Vorteile dieser Datens¨a tze (sehr hohe r¨aumliche/zeitliche Aufl¨osung)f¨u rdie operationelle Wettervorhersage nutzbar zu machen. Contents Acknowledgements iii Abstract v Kurzfassung vii Contents viii 1Introduction 1 1.1Thesis contribution. ............................ 3 1.1.1 Goals ................................ 4 1.2 Thesis outline ............................... 5 2GlobalNavigation Satellite Systems 7 2.1Principles of GNSS positioning ...................... 8 2.2 GNSS types................................ 8 2.2.1 GPS ................................ 9 2.2.2 GLONASS ............................. 12 2.2.3 Galileo ..............................
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