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COST ES1206: Contribution of the Vienna University of Technology Möller Gregor, Böhm Johannes, Umnig Elke, Hinterberger Fabian, Weber Robert Vienna University of Technology Department of Geodesy and Geoinformation (GEO), Research Group Advanced Geodesy, Vienna, Austria e-mail: [email protected] References • Amanatides, J., Woo, A., A Fast Voxel Traversal Algorithm for Ray Tracing, Proc. Eurographics ‚87, Amsterdam, The Netherlands, August 1987, pp1-10. • Karabatic A., Weber R., Haiden T., Near real-time estimation of tropospheric water vapour content from ground based GNSS data and its potential contribution to weather now-casting in Austria, Advances in Space Research, 47, 2011, pp. 1691-1703 • Moeller, G., Weber, R., Boehm. J., GNSS Tomography of the Atmosphere - Expectation from Galileo FOC, 4th International Galileo Science Colloquium, Praque, Czech Republic, 4.-6. December 2013 • Moeller, G., Weber, R., Boehm, J., Improved troposphere blind models based on numerical weather data, ION GNSS+ 2013 Proceedings, Nashville, Tennessee, 16.-20. September 2013, ISSN 2331-5911, Pages 2489 - 2495 • Notarpietro, R., Cucca, M., Gabella, M., Venuti, G., Perona, G., Tomographic reconstruction of wet and total refractivity fields from GNSS receiver networks, Advances in Space Research 47 (2011) 898- 912 Introduction Atmospheric research is one of the major activities at the Research Group Ad- vanced Geodesy. Hence we are particularly keen to be able to contribute to the European COST Action (European Cooperation in Science and Technology) ES1206 which started in May 2013 with the aim to investigate advanced GNSS tropospheric products for monitoring severe weather events and climate. We are presenting an excerpt of our research activities which are related to Working Group 1 - GNSS processing techniques. This includes the wide field from tropo- spheric a priori models to real time GNSS analysis. GNSS analysis for NRT ZTDs The aim is to set up an automatic processing to estimate the tropospheric total delay in zenith direction (ZTD) over Austria in near real-time (NRT) with an formal error better than 2 mm and a temporal resolution of 15 min. The processing scheme was developed within the framework of the Project GNSSMET-AUSTRIA in 2010 and was used again to reprocess ZTDs over a time span of three month in summer 2011 in order to study its impact on the numerical weather model AROME. Now we are going to apply the processing scheme to 90 GNSS sites mostly situated in Austria and some in neighbouring countries; see Figure 4. The GNSS sites are allocated with a horizontal distance of 5-80 kilometres and a maximum height difference of up to 2200 meters. To define the datum, observation data of adjacent IGS and EUREF sites are implemented. The ZTDs, based on a double difference approach, are computed by means of the BERNESE V5.2 package. In Table 2 the input data and main parameters set up in BERNESE are listed. GNSS Tomography - Expectations from Galileo FOC GNSS observation data allow recovering the structure of the atmosphere. In particular the temporal and spatial distribution of the highly variable water vapour in the lower layers of the atmosphere (up to 12 km) is of interest for regional weather forecasting and precise positioning. In order to obtain this information the atmosphere is divided into a 3D voxel-model (see Figure 2) and the wet refractivity in each voxel is determined by applying a tomography approach as described in Notarpietro (2011). Input data are Slant Wet Delays (SWD) derived by GNSS observations. Figure 2: 3D voxel structure of the atmosphere above Austria with a spatial resolution of ~18.0 km and 16 pressure levels. A pressure value of 200 hPa corresponds to a height of about 11800 m. Each blue dot represents a GNSS reference site. Independent from the reconstruction technique a high station density and a large number of GNSS observations are necessary to obtain an enhanced resolution (especially in vertical direction) and stable tomography results. Upcoming satellite systems like Galileo will push on the tomography approch. In order to study its impact on GNSS tomography a full operational capability (FOC) of Galileo satellites is simulated and analysed. For GPS and GLONASS the constellation that was operational in September 2013 is assumed. Figure 3 shows the lines-of-sight to be observed at a site near Vienna, Austria. Figure3:Lines-of-SighttoallGPS(redline),GLONASS (green line) and Galileo (blue line) satellites in view at GNSS site Baden (near Vienna) - plotted for a single epoch. The directions to the satellites are calculated from broadcast ephemeris. Both, the site coordinates and the directions are input for the voxel traversal algorithm; see Amanatides and Woo (1987). This algorithm is used to detect voxel traversals and to determine the number of empty voxels (voxels which are not passed by any line-of-sight). Galileo provides about 50% additional observations in new azimuth and elevation angles (compared to GPS+GLONASS). Thus the distribution of slant path delays, especially the horizontal coverage, becomes more homogeneous and the number of empty voxels can be significantly reduced. We analysed the observation of all three satellite systems and found that after 15 minutes in average 50% of the voxels remain empty if only GPS observations are used. If GLONASS and Galileo observations are added, the number of empty voxels can be reduced by 35% and its temporal variation (due to the changing number of satellites in view over time) is widely compensated. Infurtherconsequencethetomographyapproachwillbenefitfromtheseadditional data and its solution will become more stable. In our cooperative project GNSS- ATom we continue our studies in order to find the „best“ approach to obtain SWD (differenced / undifferenced) from GNSS observations, to reconstruct N wet from SWD and to assimilate it into high-resolution weather models. Real Time GNSS processing Recently the work on the project PPP Serve has been finished. Within the framework of the project we developed a real time capable software for the estimation of so-called satellite-phase-biases which allow for integer ambiguity resolution in PPP. The processing scheme of the software also includes the estimation of the ZTD at every station of a chosen network. Currently our model uses the Niell MF, that considers different obliquity factors for the wet and dry components. The wet tropospheric delay is estimated on top of an a priori model as a random walk process within a Kalman filter together with the other parameters. First comparisons between our ZTDs, estimated within the fixed PPP solution, with the ZTDs of a network solution indicated a very good agreement at the few millimeter-level. Tropospheric error correction models GPT2(w) In 2013 two updates to the Global Mapping Function (GMF) and the Global Pressure and Temperature model (GPT) have been released which are called GPT2 and GPT2w. Input parameters are just the date and the station coordinates (longitude, latitude and ellipsoidal height). GPT2 provides pressure, temperature, water vapour pressure, temperature lapse rate and mapping function coefficients on 5 degree grids as average values, annual and semi-annual variations. Therewith it is possible to calculate the zenith hydrostatic (ZHD) and wet delay (ZWD) and the mapping function for any receiver on earth at any time. Figure 1: Tropospheric delay in Zenith direction (ZTD) at GNSS site Malindi (Kenya, Africa) delivered by CDDIS / IGS (blue), the tropospheric delay model RTCA MOPS (red), GPT2 (black) and GPT2w (green). The ZTD course is dominated by semi-annual periods. GPT2w benefits from the new parameters and the improved wet delay model. The extension ‚w‘ in GPT2w is related to the additional parameters water vapour decrease factor and mean temperature which are provided on 1 degree grids in order to calculate the zenith tropospheric delay (ZWD) more reliably. The positive impact of the new parameters and the improved wet delay model is seen in the comparison with ZTDs provided by IGS in 2012 (see Figure 1). The bias and RMS could be reduced significantly - especially in comparison to common troposphe- re models for Satellite-based Augmentation Systems (SBAS) like RTCA MOPS. Model Bias (cm) RMS (cm) GPT2w -0.03 4.13 GPT2 -0.37 5.05 RTCA MOPS -2.60 6.39 COST ES1206 Workshop - GNSS4SWEC | 26. - 28.02.2014 | MUNICH Observation data GPS/GLONASS, L1 & L2, 30 sec sampling rate, 7 degree cut-off Orbits and ERP IGS Ultra Rapid Products Datum definition Constraints on IGS & EUREF sites (0.1 mm, ITRF2008, 2005.0) A priori model & MF GPT and wet GMF Parameter Spacing 15 min (ZTD), 12 hours (Gradients) Rel. a priori sigmas 2 mm (ZTD), 0.2 mm (Gradients) Ambiguity Solution Sigma-Strategie (L5/L3), L1/L2 Table 2: Processing parameters Table 1: Bias and standard deviation of the differen- ces between ZTDs delivered by CDDIS (IGS) and the three blind models RTCA MOPS, GPT2 and GPT2w - calculated for 320 GNSS sites over the period 2012. Acknowledgments The projects PPP Serve, GNSSMET-AUSTRIA and GNSS-ATom are funded by the Austrian Research Promotion Agency (FFG). GPT2w has been developed in the framework of the project TROPSY which is funded by the European Space Agency in the frame of the Technology Research Program. We would like to thank our project partners from TU Graz, TCA and ZAMG and in particular Antonio Martellucci and Raul Orus Perez at ESA/ESTEC for their valuable contribution. Real Time Near Real Time Post Processing / A priori model Figure 4: Distribution of the 90 sites providing GNSS observation in near real-time

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  • COST ES1206: Contribution of the Vienna University of TechnologyMöller Gregor, Böhm Johannes, Umnig Elke, Hinterberger Fabian, Weber Robert

    Vienna University of TechnologyDepartment of Geodesy and Geoinformation (GEO), Research Group Advanced Geodesy, Vienna, Austria

    e-mail: [email protected]

    References• Amanatides,J.,Woo,A.,AFastVoxelTraversalAlgorithmforRayTracing,Proc.Eurographics‚87,Amsterdam,TheNetherlands,August1987,pp1-10.

    • KarabaticA.,WeberR.,HaidenT.,Nearreal-timeestimationoftroposphericwatervapourcontentfromground basedGNSS data and its potential contribution toweather now-casting in Austria,AdvancesinSpaceResearch,47,2011,pp.1691-1703

    • Moeller,G.,Weber,R.,Boehm.J.,GNSSTomographyoftheAtmosphere-ExpectationfromGalileoFOC,4thInternationalGalileoScienceColloquium,Praque,CzechRepublic,4.-6.December2013

    • Moeller,G.,Weber,R.,Boehm,J.,Improvedtroposphereblindmodelsbasedonnumericalweatherdata,IONGNSS+2013Proceedings,Nashville,Tennessee,16.-20.September2013,ISSN2331-5911,Pages2489-2495

    • Notarpietro,R.,Cucca,M.,Gabella,M.,Venuti,G.,Perona,G.,TomographicreconstructionofwetandtotalrefractivityfieldsfromGNSSreceivernetworks,AdvancesinSpaceResearch47(2011)898-912

    IntroductionAtmospheric research isoneof themajoractivitiesat theResearchGroupAd-vanced Geodesy. Hence we are particularly keen to be able to contribute totheEuropeanCOSTAction(EuropeanCooperationinScienceandTechnology)ES1206whichstartedinMay2013withtheaimtoinvestigateadvancedGNSStropospheric products formonitoring severe weather events and climate.WearepresentinganexcerptofourresearchactivitieswhicharerelatedtoWorkingGroup1-GNSSprocessingtechniques.Thisincludesthewidefieldfromtropo-sphericapriorimodelstorealtimeGNSSanalysis.

    GNSS analysis for NRT ZTDsTheaimistosetupanautomaticprocessingtoestimatethetropospherictotaldelayinzenithdirection(ZTD)overAustriainnearreal-time(NRT)withanformalerror better than 2mm and a temporal resolution of 15min. The processingschemewasdevelopedwithintheframeworkoftheProjectGNSSMET-AUSTRIAin2010andwasusedagaintoreprocessZTDsoveratimespanofthreemonthin summer2011 inorder to study its impacton thenumericalweathermodelAROME.Nowwe aregoing to apply theprocessing scheme to 90GNSS sitesmostly situated inAustria and some in neighbouring countries; see Figure 4.

    TheGNSSsitesareallocatedwithahorizontaldistanceof5-80kilometresandamaximumheightdifferenceofupto2200meters.Todefinethedatum,observationdata of adjacent IGS and EUREF sites are implemented. The ZTDs, based ona double difference approach, are computed bymeans of the BERNESE V5.2package.InTable2theinputdataandmainparameterssetupinBERNESEarelisted.

    GNSS Tomography - Expectations from Galileo FOCGNSS observation data allow recovering the structure of the atmosphere. Inparticularthetemporalandspatialdistributionofthehighlyvariablewatervapourin the lower layersof theatmosphere (up to12km) isof interest for regionalweatherforecastingandprecisepositioning.Inordertoobtainthisinformationthe atmosphere is divided into a 3D voxel-model (see Figure 2) and thewetrefractivity ineachvoxel isdeterminedbyapplyingatomographyapproachasdescribedinNotarpietro(2011).InputdataareSlantWetDelays(SWD)derivedbyGNSSobservations.

    Figure 2: 3D voxel structure of theatmosphere above Austria with a spatialresolution of ~18.0 km and 16 pressurelevels. A pressure value of 200 hPacorrespondstoaheightofabout11800m.EachbluedotrepresentsaGNSSreferencesite.

    IndependentfromthereconstructiontechniqueahighstationdensityandalargenumberofGNSSobservationsarenecessarytoobtainanenhancedresolution(especiallyinverticaldirection)andstabletomographyresults.Upcomingsatellitesystems like Galileo will push on the tomography approch. In order to studyits impact on GNSS tomography a full operational capability (FOC) of Galileosatellitesissimulatedandanalysed.ForGPSandGLONASStheconstellationthatwasoperationalinSeptember2013isassumed.Figure3showsthelines-of-sighttobeobservedatasitenearVienna,Austria.

    Figure3:Lines-of-SighttoallGPS(redline),GLONASS(green line) and Galileo (blue line) satellites inviewatGNSS siteBaden (nearVienna) -plottedforasingleepoch.Thedirectionstothesatellitesare calculated from broadcast ephemeris. Both,thesitecoordinatesandthedirectionsareinputforthevoxeltraversalalgorithm;seeAmanatidesandWoo(1987).Thisalgorithmisusedtodetectvoxeltraversalsandtodeterminethenumberofemptyvoxels(voxelswhicharenotpassedbyanyline-of-sight).

    Galileoprovidesabout50%additionalobservationsinnewazimuthandelevationangles(comparedtoGPS+GLONASS).Thusthedistributionofslantpathdelays,especiallythehorizontalcoverage,becomesmorehomogeneousandthenumberofemptyvoxelscanbesignificantly reduced.Weanalysed theobservationofallthreesatellitesystemsandfoundthatafter15minutesinaverage50%ofthevoxelsremainemptyifonlyGPSobservationsareused.IfGLONASSandGalileoobservationsareadded, thenumberofemptyvoxels canbe reducedby35%anditstemporalvariation(duetothechangingnumberofsatellitesinviewovertime)iswidelycompensated.

    Infurtherconsequencethetomographyapproachwillbenefitfromtheseadditionaldataanditssolutionwillbecomemorestable.InourcooperativeprojectGNSS-ATomwecontinueour studies inorder tofind the „best“ approach toobtainSWD(differenced/undifferenced)fromGNSSobservations,toreconstructNwetfromSWDandtoassimilateitintohigh-resolutionweathermodels.

    Real Time GNSS processingRecentlytheworkontheprojectPPPServehasbeenfinished.Withintheframeworkoftheprojectwedevelopedarealtimecapablesoftwarefortheestimationofso-calledsatellite-phase-biaseswhichallowfor integerambiguity resolution inPPP.TheprocessingschemeofthesoftwarealsoincludestheestimationoftheZTDatevery stationofa chosennetwork.Currentlyourmodeluses theNiellMF, thatconsidersdifferentobliquity factors for thewetanddrycomponents.Thewettroposphericdelayisestimatedontopofanapriorimodelasarandomwalk process within a Kalman filter together with the other parameters. FirstcomparisonsbetweenourZTDs,estimatedwithin thefixedPPPsolution,withthe ZTDs of a network solution indicated a very good agreement at the fewmillimeter-level.

    Tropospheric error correction models GPT2(w)In2013twoupdatestotheGlobalMappingFunction(GMF)andtheGlobalPressureandTemperaturemodel (GPT)havebeen releasedwhichare calledGPT2andGPT2w.Inputparametersarejustthedateandthestationcoordinates(longitude,latitude and ellipsoidal height). GPT2 provides pressure, temperature, watervapourpressure,temperaturelapserateandmappingfunctioncoefficientson5degreegridsasaveragevalues,annualandsemi-annualvariations.Therewithitispossibletocalculatethezenithhydrostatic(ZHD)andwetdelay(ZWD)andthemappingfunctionforanyreceiveronearthatanytime.

    Figure 1: Tropospheric delay in Zenithdirection (ZTD) at GNSS site Malindi (Kenya,Africa) delivered by CDDIS / IGS (blue), thetropospheric delay model RTCA MOPS (red),GPT2 (black) and GPT2w (green). The ZTDcourse is dominatedby semi-annual periods.GPT2wbenefitsfromthenewparametersandtheimprovedwetdelaymodel.

    Theextension‚w‘inGPT2wisrelatedtotheadditionalparameterswatervapourdecreasefactorandmeantemperaturewhichareprovidedon1degreegridsinordertocalculatethezenithtroposphericdelay(ZWD)morereliably.ThepositiveimpactofthenewparametersandtheimprovedwetdelaymodelisseeninthecomparisonwithZTDsprovidedbyIGSin2012(seeFigure1).ThebiasandRMScouldbereducedsignificantly-especiallyincomparisontocommontroposphe-remodelsforSatellite-basedAugmentationSystems(SBAS)likeRTCAMOPS.

    Model Bias (cm) RMS (cm)GPT2w -0.03 4.13GPT2 -0.37 5.05

    RTCA MOPS -2.60 6.39

    COST ES1206 Workshop - GNSS4SWEC | 26. - 28.02.2014 | MUNICH

    Observation data GPS/GLONASS, L1 & L2, 30 sec sampling rate, 7 degree cut-off

    Orbits and ERP IGS Ultra Rapid Products

    Datum definition Constraints on IGS & EUREF sites (0.1 mm, ITRF2008, 2005.0)

    A priori model & MF GPT and wet GMF

    Parameter Spacing 15 min (ZTD), 12 hours (Gradients)

    Rel. a priori sigmas 2 mm (ZTD), 0.2 mm (Gradients)

    Ambiguity Solution Sigma-Strategie (L5/L3), L1/L2

    Table 2: Processing parameters

    Table 1: Bias and standard deviation of the differen-ces between ZTDs delivered by CDDIS (IGS) and the three blind models RTCA MOPS, GPT2 and GPT2w - calculated for 320 GNSS sites over the period 2012.

    AcknowledgmentsTheprojectsPPPServe,GNSSMET-AUSTRIAandGNSS-ATomarefundedbytheAustrianResearchPromotionAgency(FFG).GPT2whasbeendevelopedintheframeworkoftheprojectTROPSYwhichisfundedbytheEuropeanSpaceAgencyin theframeof theTechnologyResearchProgram.Wewould like to thankourprojectpartnersfromTUGraz,TCAandZAMGandinparticularAntonioMartellucciandRaulOrusPerezatESA/ESTECfortheirvaluablecontribution.

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    Figure4:Distributionofthe90sitesprovidingGNSSobservationinnearreal-time


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