The provided dataset consists of double differential slant delays and absolute zenith wet delays in the region of the Upper Rhine Graben. Basis is the SLC data from Sentinel 1A+B satellites provided by the Copernicus program. 169 scenes were processed which had been acquired between April 2015 and July 2019, including data of four specific study events (11 – 22 Apr 2016, 13 – 24 Jul 2018, 16 – 31 Oct 2018, 06 – 21 Jan 2017). Interferometric processing was performed using the software SNAP, continued by a Persistent Scatterer Interferometric SAR (PS-InSAR) processing, using the program StaMPS. The first product are double differential slant delays which represent the phase delay in radiant in the satellites line of sight between the master acquisition (17 Mar 2012) and each acquisition-date respectively. Further processing uses ERA5 zenith wet delay (ZWD) and mean temperature to infer absolute zenith wet delays. A mean value is subtracted for each scene, resulting in an absolute value correction. In addition, long wavelength components are corrected by fitting the trend over the scene for each date to a 2D polynomial approximation from the ERA5 data, as those parts cannot reliably be estimated solely from the SAR data. The final product for every scene is the integrated water vapor (IWV) in kg/m² for each acquisition date at the distributed PS-points – on average about 50 points per square kilometer.
The collocation method was used to compute water vapor fields for the Upper Rhine Graben (URG) region from GNSS zenith total delays (ZTDs) and InSAR double difference slant delays (ddSTDs). Furthermore, mean temperature from ERA data was used for the conversion of GNSS ZTDs into IWV. The input data are hourly GNSS tropospheric parameters from the GURN (GNSS Upper Rhine Graben network) network for 4 different seasons in the period 2016-2018, as well as ddSTDs for 168 InSAR acquisition epochs of the Sentinel 1A+B satellites. In total, our dataset includes 2D fields of integrated water vapor (IWV) and zenith total delays (ZTDs) as well as 3D 'tomographic' products in form of refractivity fields. For 4 specific seasonal periods, also hourly water vapor density fields are provided by exploiting the relations between IWV and water vapor density in the collocation scheme. The tropospheric fields are provided for the horizontal WRF grid of data assimilation subset of this joint data collection, whereas the 3D fields are computed up to 8 km height for 16 equally distributed layers.
The ground-based global navigation satellite system (GNSS) technic was employed to retrieve the integrated water vapor (IWV) at 66 stations of the GNSS Upper Rhine Graben network (GURN). The GNSS IWV dataset is synchronous with the associated InSAR dataset, with 219 days available during the period March 2015 – July 2019. GNSS zenith total delay (ZTD) estimates are calculated every one hour and then converted to IWV with additional meteorological parameters from ERA5. The GNSS IWV of all the stations are saved in daily files in the second version of the Solution (Software/Technique) Independent Exchange (SINEX) format for TROpospheric parameters. GNSS station information is given in the file headers. In addition, the associated meteorological parameters from ERA5 are also provided, such as station pressure and weighted mean temperature.
Convection-permitting simulations with the Weather Research and Forecasting Modeling System (WRF) were carried out in order to provide improved water vapor fields for the Upper Rhine Valley in the border region of Germany, Switzerland and France. Hourly ERA5 reanalysis data served as input for three different simulations with (1) open loop, (2) assimilation of GNSS ZTD, InSAR ZTD and synoptic station data and (3) assimilation of tomography ZTD fields. The three-dimensional variation data assimilation (3D-VAR) configuration with hourly resolution was used. The simulations were performed for four events, one in each season (April 11-22, 2016, July 13-23, 2018, October 16-31, 2018, January 6-21, 2017). Surface pressure, temperature (2m) and integrated water vapor are provided in 2D as well as pressure, temperature and water vapor density for each of the 72 vertical levels (3D).
The TectoVision GNSS network in Greece was set up using European Research Council funding in partnership between German and Greek institutions. The project aims to deepen our understanding of suspected microplate motions in Greece. A total of 72 GNSS stations are planned for the TectoVision network. Two types of GNSS station equipment is used, reflected by the 4 character station ID. Stations with ID beginning with 'TT' were installed using the tinyBlack receiver. The stations with ID beginning 'TM' were set up using the Minimum Cost GNSS System (MCGS) design. RINEX (v3.05 as of May 2024) data at 30 seconds sampling interval are provided. Most of the data are sent over mobile internet via routers that are connected to the receivers via LAN cable. If required, the RINEX files can be converted to other versions using the GFZ software GFZRNX (Nischan, 2016). Raw observation data can be made available upon specific request. Hardware: The GFZ developed tinyBlack receiver combines cost efficient L2C GNSS receivers (here Swiftnav Piksi) with PC-based data logger package, internal storage, and interfaces. The control software is designed for remote operation ensuring long-term continuous tracking. The tinyBlack receivers provided by the GFZ spin-off maRam UG (Germany) are installed in combination with Harxon GPS500 survey antennas. The tinyBlack stations provide GPS (L1/L2), GLONASS (G1/G2), and Galileo (E1/E5b) data. The low-cost MCGS stations are coming in 2 versions from a GNSS technology transfer project at GFZ. Both versions operate a ublox F9P receiver and an integrated chip-antenna with a pyramidal antenna radome. One version provides GPS (L1/L2), Glonass (G1/G2), Galileo (E1/E5b) and also Beidou (B1/B2) data. The other version (currently only 3 systems installed) is designed for low power operation in remote areas with data telemetry over a narrrow bandwidth radio link. This version delivers only GPS (L1/L2) data without doppler observations at a reduced data rate of 60 seconds. Monumentation: There is a variety of monumentation for these stations, with the design of the monumentation being low-cost. Most are connected to a thread that is attached to a stainless steel pin which is glued into masonry or bedrock. Most sites are installed on rooftops of public buildings. The MCGS is sometimes clamped to an existing sturdy pole connected to the roof of the building. Some stations are connected to an extending stainless-steel arm that we have drilled into the side of a building. Photos of the station are provided with the standard GNSS station log-files (as metadata). If the instrumentation at existing monuments is later changed to other hardware types, the station ID retain the original TT and TM 4-character IDs. Metadata: Station-specific metadata records are stored in IGS sitelog files available via ftp.
Different observation and modeling techniques were used to derive integrated water vapor (IWV) fields for the Upper Rhine Graben in the border region of Germany, Switzerland, and France. The dataset features 1) point-scale IWV and zenith total delay (ZTD) derived for 66 stations of the global navigation satellite system (GNSS) Upper Rhine Graben network (GURN), 2) area-distributed IWV and differential slant path delays from space-borne Interferometric synthetic aperture radar (InSAR) observations, 3) IWV, ZTD, refractivity (3D), and water vapor density (3D) from tomography, obtained by collocation of GNSS and InSAR products, and 4) IWV, precipitation and water vapor density (3D) simulated with the Weather Research and Forecasting Modeling system (WRF) with free run (open-loop) and three-dimensional variational data-assimilation (3D-VAR) configuration. All data products cover 4 seasonal epochs (11 – 22 Apr 2016, 13 – 24 Jul 2018, 16 – 31 Oct 2018, 06 – 21 Jan 2017). GNSS, InSAR, and tomography data are additionally available for the period Jan 2015 – Jun 2019.
The Early-Warning and Rapid Impact Assessment with real-time GNSS in the Mediterranean (EWRICA) is a federal Ministry of Education and Research funded project (funding period: 2020-2023) that aims to develop fast kinematic and point source inversion and modeling tools combining GNSS-based near field data with traditional broadband ground velocity and accelerometer data. Fast and robust estimates of seismic source parameters are essential for reliable hazard estimates, e.g. in the frame of tsunami early warning. Hence, EWRICA aims for the development and testing of new real time seismic source inversion techniques based on local surface displacements. The resulting methods shall be applied for tsunami early warning purposes in the Mediterranean area. In this framework, this repository is a suite of four packages that can be used and combined in different ways and are ewricacore, ewricasiria, ewricagm and ewricawebapp. These four packages can be deployed in a docker container (see instructions below) to demonstrate a possible output of Early-Warning and Rapid Impact Assessment. In the Docker, a probabilistic earthquake source inversion report (ewricasiria) and a Neural network based Shake map (ewricagm) are generated for two past earthquakes whose data (event and waveform) is continuously served by GEOFON servers at regualr intervals to produce and test a real case scenario. The whole workflow is managed by ewricacore, a central unit of work that first fetches the waveform data via the seedlink protocol and event data via event bus or FDSN web service, then collects and cuts waveforms segments according to a custom configuration, and eventually triggers custom processing (ewricasiria and ewricagm in the docker, but any processing can be implemented) whenever configurable conditions are met. The final package, ewricawebapp is a web-based graphical user interface that can be opened in your local browser or deployed on your web server in order to visualize and check all output produced by the docker workflow in form of HTML pges, images and data in various formats (e.g., JSON, log text files). The EWRICA Docker package includes the following tools: ewricacore: Central unit for all Ewrica components and event/data listener ewricagm: Create ground motion maps via pre-trained Neural Network ewricasiria: Ewrica Source Inversion and Rapid Impact Assessment Python package ewricawebapp: Ewrica web portal and GUI demo grond: A probabilistic earthquake source inversion framework (Heimann et al., 2018) stationsxml-archive: Storage repository for synchronizing Station XMLs
We created a 3D GNSS surface velocity field to estimate tectonic plate motion and test the effect of a set of 1D and 3D Glacial Isostatic Adjustment (GIA) models on tectonic plate motion estimates. The main motivation for creating a bespoke 3D velocity field is to include a larger number of GNSS sites in the GIA-affected areas of investigation, namely North America, Europe, and Antarctica. We created the GNSS surface velocity field using the daily network solutions submitted to the International GNSS Service (IGS) “repro2” data processing campaign, and other similarly processed GNSS solutions. We combined multiple epoch solutions into unique global epoch solutions of high stability. The GNSS solutions we used were processed with the latest available methods and models at the time: all the global and regional solutions adhere to IGS repro2 standards. Every network solution gives standard deviations of site position coordinates and the correlations between the network sites. We deconstrained and combined the global networks and aligned them to the most recent ITRF2014 reference frame on a daily level. Additionally, several regional network solutions were deconstrained and aligned to the unique global solutions. The process was performed using the Tanya reference frame combination software (Davies & Blewitt, 1997; doi:10.1029/2000JB900004) which we updated to facilitate changes in network combination method and ITRF realisation. This resulted in 57% reduction of the WRMS of the alignment post-fit residuals compared to the alignment to the previous ITRF2008 reference frame for an overlapping period. We estimated linear velocities from the time series of GNSS coordinates using the MIDAS trend estimator (Blewitt et al., 2016; doi:10.1002/2015JB012552). The sites selected through multiple steps of quality control constitute a final GNSS surface velocity field which we denote NCL20. This velocity field has horizontal uncertainties mostly within 0.5 mm/yr, and vertical uncertainties mostly within 1 mm/yr, which make it suitable for testing GIA models and estimating plate motion models.
Long-term tide gauge records provide valuable insights to sea level variations, but interpretation requires an accurate determination of the associated vertical land motion. Within the Tide Gauge Benchmark Monitoring Working Group of the International GNSS Service, we performed a dedicated reprocessing (1994-2020) for GNSS stations co-located with tide gauges. Based on 341 stations the GFZ contribution to the third TIGA reprocessing provides vertical land motion rates for 230 stations at or close to recently active tide gauges. We limited the processing to GPS observations.
This supplement contains GNSS displacement time series, fluid loading displacement time series predictions, and trajectory models for these time series. The time series are for the study regions of the paper: "Months-Long thousand-km-scale wobbling before great subduction earthquakes". These study regions are (1) Japan and surrounding countries and (2) Chile and surrounding countries. Network solution daily GNSS time series displacements in Chile and surrounding countries in the South American network have been produced by GFZ. Network solution daily GNSS time series of displacements in Japan have been produced by the Geospatial Information Authority of Japan (GSI). PPP daily GNSS time series of displacements in Japan and surrounding countries have been produced by the Nevada Geodetic Laboratory, Nevada Bureau of Mines and Geology, University of Nevada, Reno. Fluid loading predictions have been made using the HYDL, NTOL, NTAL, and SLEL products of the ESMGFZ. Readme ascii files in this data supplement contain instructions on how the data are ordered. Furthermore, the Readme file contains the relevant references and acknowledgments for readers who want to use these data in their own published studies.
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