Abstract
We present a new method to estimate common-mode components (CMC) in global positioning system (GPS) position time-series. The method (‘CMC Imaging’) is fully automated, relies entirely on robust statistics, and exploits the recent proliferation of GPS stations by allowing stations with relatively short time-series to be considered as filter stations as well. The spatial extent of the CMC is purposely defined as local as possible and constrained by the proximity of nearby GPS stations. Our approach also avoids the need for subjective assignment of filter stations; every station is considered and those stations that deviate significantly from the local CMC are flagged and excluded as filter stations. We study thousands of GPS position time-series in the intraplate area of western Europe, and we show that CMC Imaging method is superior to other approaches in terms of noise reduction: we obtain an RMS reduction of 50%, 44% and 39% in the residual time-series in vertical, east, and north components, respectively. We show the importance of using filter stations that are as local as possible, because of systematic lateral variations in inter-station correlations and indeed in CMC, particularly in the vertical component. Those spatial variations are largest for continental stations, particularly those around the Baltic Sea, and could be due to atmospheric and nontidal ocean loading. CMC filtering has a large influence on reducing the temporal trend variability and approximately doubles the trend accuracy (by comparing variability in short-term trends with the long-term estimate).
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Acknowledgements
We are extremely grateful to the many agencies, companies, and networks that have made GPS RINEX data available. We specifically thank the following networks and institutions for raw RINEX data: EUREF (Bruyninx et al. 2019), IGS and the many agencies working under its umbrella (Johnston et al. 2017), CDDIS (Noll 2010),Technical University Delft (The Netherlands), NETPOS (The Netherlands), 06-GPS (The Netherlands), LNR Globalcom (The Netherlands), NAM/Shell (The Netherlands), ESTEC (The Netherlands), Royal Observatory of Belgium (Bruyninx and Defraigne 2018), FLEPOS (Belgium), WALCORS (Belgium), Institut Géographique National (France), Réseau GNSS Permanent (France), ORPHEON (France), RENAG (France) (RESIF 2017), SONEL (France) (Gravelle et al. 2019), SPSLux (Luxembourg), Natural Environment Research Council’s British Isles continuous GNSS Facility (United Kingdom), Leica Smartnet (Poland), ASG-EUPOS (Poland), Pecny Observatory (Czech Republic), GEONAS (Czech Republic) (Schenk et al. 2010), VESOG (Czech Republic) (Kostelecký and Kostelecký 2005), CZEPOS (Czech Republic) (Kostelecký and Kostelecký 2005), FreDNet (Italy), Regione Autonoma Friuli Venezia Giulia (Italy), STPOS Bolzano (Italy), Politecnico di Torino (Italy), ARPA Piemonte (Italy), Rete GPS Veneto (Italy), Rete Dinamica Nazionale (Italy), Instituto Nazionale di Geofisica e Vulcanologia (Italy), ITALPOS (Italy), Geodetski Institute (Slovenia), NIEP (Romania), ROMPOS (Romania), ESTPOS (Estonia) (Metsar et al. 2018), LATPOS (Latvia), LITPOS (Lithuania), Danish GPS Center Aalborg (Denmark), Danish Geodata Agency (Denmark), Instituto Geográfico Nacional (Spain), Topcon (Spain), CATNET Catalonia (Spain), GNSS Activa del Principado de Asturias (Spain), Red Activa de Estaciones GNSS de Cantabria (Spain), Red de estaciones GNSS de Castilla y León (Spain), Red Extremeña de Posicionamiento (Spain), Carto Galicia (Spain), REGAM Murcia (Spain), Meristenum Murcia (Spain), Government of La Rioja (Spain), Universidad de Oviedo (Spain), Ayuntamiento de Leganes (Spain), ETSI Topografia Geodesia y Cartografia (Spain), Leica Smartnet (Poland), ASG-EUPOS (Poland), ReNEP—Instituto Geografico Portugues (Portugal), SERVIR (Portugal), Norwegian Mapping Authority (Norway), Norwegian Metrology Service (Norway), SWEPOS (Sweden), Finnish Geodetic Institute (Finland), LIPOS (Austria), SIGNAL (Slovenia), Bundesamt für Kartographie und Geodäsie (Germany), GFZ German Research Centre for Geosciences (Germany) (Uhlemann et al. 2016), Bundesanstalt für Gewässerkunde (Germany), Deutsches Geodätisches Forschungsinstitut (Germany) (Seitz et al. 2014), ASCOS (Germany), and SAPOS networks operated by various German States (i.e., State Office for Spatial Information and Land Development Baden-Württemberg, Hessian State Office for Land Management and Geoinformation, Landesamt für innere Verwaltung Mecklenburg-Vorpommern, Landesvermessung und Geobasisinformation Brandenburg, Staatsbetrieb Geobasisinformation und Vermessung Sachsen, Landesamt für Vermessung und Geoinformation Thüringen, Landesamt für Vermessung und Geobasis Information Rheinland-Pfalz, Bezirksregierung Köln, Geoinformation und Landentwicklung Saarland, and Landesamt für Digitalisierung, Breitband und Vermessung Bayern). ORPHEON GNSS data were provided to the authors for scientific use in the framework of the GEODATA-INSU-CNRS convention. The services of the UK Natural Environment Research Council (NERC) British Isles continuous GNSS Facility (BIGF), www.bigf.ac.uk, in providing archived GNSS data to this project, are gratefully acknowledged. A special thanks to H. van der Marel for helping to make the Kadaster/06-GPS/NAM data in the Netherlands available, A. Araszkiewicz for helping to make the Polish Smartnet data available, E. Nastase for helping to make the Romanian ROMPOS data available, and T. van Dam for helping to make the SPSLux data in Luxembourg available. We are deeply indebted to Yunfeng Tian for applying the CWSF approach to our data set.
Funding
This work got started through financial support to CK from the Royal Dutch Academy of Sciences. Additional support for CK was provided by United States Geological Survey National Earthquake Hazard Reduction Program award G18AP00019 and to CK and GB from National Space and Aeronautics Administration grants NNX16AK89G and 80NSSC19K1044.
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CK designed the study and did all the time-series analysis. GB processed all the RINEX data and produced the time-series. CK wrote the paper, with contributions of GB.
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Time-series before and after filtering (minus outliers and corrected for offsets) can be found at 10.7910/DVN/ONATFP, and the original time-series at http://geodesy.unr.edu (Blewitt et al. 2018).
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Kreemer, C., Blewitt, G. Robust estimation of spatially varying common-mode components in GPS time-series. J Geod 95, 13 (2021). https://doi.org/10.1007/s00190-020-01466-5
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DOI: https://doi.org/10.1007/s00190-020-01466-5