Articles | Volume 15, issue 1
https://doi.org/10.5194/amt-15-21-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-21-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards operational multi-GNSS tropospheric products at GFZ Potsdam
Institute of Geodesy and Geoinformation Sciences, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Wroclaw University of Environmental and Life Sciences, C.K. Norwida 25, 50-375 Wroclaw, Poland
Invited contribution by Karina Wilgan, recipient of the EGU Geodesy Division Outstanding Early Career Scientists Award 2020.
Galina Dick
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Florian Zus
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Jens Wickert
Institute of Geodesy and Geoinformation Sciences, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
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Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Atmos. Meas. Tech., 18, 4907–4922, https://doi.org/10.5194/amt-18-4907-2025, https://doi.org/10.5194/amt-18-4907-2025, 2025
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Tropospheric gradients provide information on the moisture distribution, whereas zenith total delays provide the overall moisture information along the zenith. When both observations are used together, the model can actuate the moisture fields, correcting their dynamics. Our research shows that, in regions with very few stations, assimilating tropospheric gradients on top of zenith total delay observations can enhance the performance of existing improvements.
Florian Zus, Kyriakos Balidakis, Ali Hasan Dogan, Rohith Thundathil, Galina Dick, and Jens Wickert
Geosci. Model Dev., 18, 4951–4964, https://doi.org/10.5194/gmd-18-4951-2025, https://doi.org/10.5194/gmd-18-4951-2025, 2025
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Atmospheric signal propagation effects are one of the largest error sources in the analysis of space geodetic techniques. Inaccuracies in the modelling map into errors in positioning, navigation and timing. We describe the open-source ray-tracing tool DNS and show the two outstanding features of this tool compared to previous model developments: it can handle both the troposphere and the ionosphere, and it does so efficiently. This makes the tool perfectly suited for geoscientific applications.
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024, https://doi.org/10.5194/amt-17-4303-2024, 2024
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Crowdsourced smartphone GNSS data were processed with a dedicated data processing pipeline and could produce millimeter-level accurate estimates of zenith total delay (ZTD) – a critical atmospheric variable. This breakthrough not only demonstrates the feasibility of using ubiquitous devices for high-precision atmospheric monitoring but also underscores the potential for a global, cost-effective tropospheric monitoring network.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
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Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ladina Steiner, Holger Schmithüsen, Jens Wickert, and Olaf Eisen
The Cryosphere, 17, 4903–4916, https://doi.org/10.5194/tc-17-4903-2023, https://doi.org/10.5194/tc-17-4903-2023, 2023
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The present study illustrates the potential of a combined Global Navigation Satellite System reflectometry and refractometry (GNSS-RR) method for accurate, simultaneous, and continuous estimation of in situ snow accumulation, snow water equivalent, and snow density time series. The combined GNSS-RR method was successfully applied on a fast-moving, polar ice shelf. The combined GNSS-RR approach could be highly advantageous for a continuous quantification of ice sheet surface mass balances.
Benjamin Männel, Florian Zus, Galina Dick, Susanne Glaser, Maximilian Semmling, Kyriakos Balidakis, Jens Wickert, Marion Maturilli, Sandro Dahlke, and Harald Schuh
Atmos. Meas. Tech., 14, 5127–5138, https://doi.org/10.5194/amt-14-5127-2021, https://doi.org/10.5194/amt-14-5127-2021, 2021
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Within the MOSAiC expedition, GNSS was used to monitor variations in atmospheric water vapor. Based on 15 months of continuously tracked data, coordinates and hourly zenith total delays (ZTDs) were determined using kinematic precise point positioning. The derived ZTD values agree within few millimeters with ERA5 and terrestrial GNSS and VLBI stations. The derived integrated water vapor corresponds to the frequently launched radiosondes (0.08 ± 0.04 kg m−2, rms of the differences of 1.47 kg m−2).
Cited articles
Bar-Sever, Y. E., Kroger, P. M., and Borjesson, J. A.: Estimating horizontal gradients of tropospheric path delay with a single GPS receiver, J. Geophys. Res.-Sol. Ea., 103, 5019–5035, https://doi.org/10.1029/97jb03534, 1998. a, b, c
Bender, M., Dick, G., Wickert, J., Schmidt, T., Song, S., Gendt, G., Ge, M.,
and Rothacher, M.: Validation of GPS slant delays using water vapour
radiometers and weather models, Meteorol. Z., 17, 807–812,
https://doi.org/10.1127/0941-2948/2008/0341, 2008. a
Benevides, P., Catalao, J., and Miranda, P. M. A.: On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall, Nat. Hazards Earth Syst. Sci., 15, 2605–2616, https://doi.org/10.5194/nhess-15-2605-2015, 2015. a
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R., Kenyon, J. S., and Manikin, G. S.: A North American hourly assimilation and model forecast cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694,
https://doi.org/10.1175/MWR-D-15-0242.1, 2016. a
Bevis, M., Businger, S., Chiswell, S., Herring, T., Anthes, R., Rocken, C., and Ware, R.: GPS meteorology: Mapping zenith wet delays onto precipitable water, J. Appl. Meteorol., 33, 379–386,
https://doi.org/10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2, 1994. a
Bock, O. and Parracho, A. C.: Consistency and representativeness of integrated water vapour from ground-based GPS observations and ERA-Interim reanalysis, Atmos. Chem. Phys., 19, 9453–9468, https://doi.org/10.5194/acp-19-9453-2019, 2019. a
Böhm, J., Niell, A., Tregoning, P., and Schuh, H.: Global Mapping Function
(GMF): A new empirical mapping function based on numerical weather model
data, Geophys. Res. Lett., 33, 3–6, https://doi.org/10.1029/2005GL025546, 2006. a, b
Böhm, J., Heinkelmann, R., and Schuh, H.: Short note: A global model of
pressure and temperature for geodetic applications, J. Geodesy, 81,
679–683, https://doi.org/10.1007/s00190-007-0135-3, 2007. a
Boniface, K., Ducrocq, V., Jaubert, G., Yan, X., Brousseau, P., Masson, F., Champollion, C., Chéry, J., and Doerflinger, E.: Impact of high-resolution data assimilation of GPS zenith delay on Mediterranean heavy rainfall forecasting, Ann. Geophys., 27, 2739–2753, https://doi.org/10.5194/angeo-27-2739-2009, 2009. a
Bosser, P. and Bock, O.: IWV retrieval from ground GNSS receivers during NAWDEX, Adv. Geosci., 55, 13–22, https://doi.org/10.5194/adgeo-55-13-2021, 2021. a
Bradke, M.: SEMISYS – Sensor Meta Information System, V. 4.1, GFZ Data Services [data set], https://doi.org/10.5880/GFZ.1.1.2020.005, 2020. a
Chen, G. and Herring, T. A.: Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data, J. Geophys. Res.-Sol. Ea., 102, 20489–20502, https://doi.org/10.1029/97jb01739, 1997. a
Cucurull, L., Derber, J. C., Treadon, R., and Purser, R. J.: Assimilation of
Global Positioning System Radio Occultation Observations into NCEP's Global
Data Assimilation System, Mon. Weather Rev., 135, 3174–3193,
https://doi.org/10.1175/MWR3461.1, 2007. a
de Haan, S., van der Marel, H., and Barlag, S.: Comparison of GPS slant delay
measurements to a numerical model: case study of a cold front passage,
Phys. Chem. Earth Pt. A/B/C, 27, 317–322, https://doi.org/10.1016/S1474-7065(02)00006-2, 2002. a
Dick, G., Gendt, G., and Reigber, C.: First experience with near real-time
water vapor estimation in a German GPS network, J. Atmos. Sol.-Terr. Phy., 63, 1295–1304, https://doi.org/10.1016/S1364-6826(00)00248-0, 2001. a
Douša, J., Dick, G., Kačmařík, M., Brožková, R., Zus, F., Brenot, H., Stoycheva, A., Möller, G., and Kaplon, J.: Benchmark campaign and case study episode in central Europe for development and assessment of advanced GNSS tropospheric models and products, Atmos. Meas. Tech., 9, 2989–3008, https://doi.org/10.5194/amt-9-2989-2016, 2016. a, b
Dousa, J., Vaclavovic, P., and Elias, M.: Tropospheric products of the second GOP European GNSS reprocessing (1996–2014), Atmos. Meas. Tech., 10, 3589–3607, https://doi.org/10.5194/amt-10-3589-2017, 2017. a, b, c
Elgered, G., Ning, T., Forkman, P., and Haas, R.: On the information content in linear horizontal delay gradients estimated from space geodesy observations, Atmos. Meas. Tech., 12, 3805–3823, https://doi.org/10.5194/amt-12-3805-2019, 2019. a
EPN: Daily GNSS data, EUREF Permanent Network, available at: http://www.epncb.oma.be, last access: 5 November 2021. a
Essen, L. and Froome, K.: The refractive indices and dielectric constants of
air and its principal constituents at 24 000 Mc/s, P. Phys. Soc. Lond. B, 64, 862–875, https://doi.org/10.1038/167512a0, 1951. a
Gendt, G., Dick, G., Reigber, C., Tomassini, M., Liu, Y., and Ramatschi, M.:
Near real time GPS water vapor monitoring for numerical weather prediction in
Germany, J. Meteorol. Soc. Jpn., 82, 361–370, https://doi.org/10.2151/jmsj.2004.361, 2004. a
Hadaś, T., Hobiger, T., and Hordyniec, P.: Considering different recent
advancements in GNSS on real-time zenith troposphere estimates, GPS Solutions, 24, 1–14, https://doi.org/10.1007/s10291-020-01014-w, 2020. a
Healy, S. B., Jupp, A. M., and Marquardt, C.: Forecast impact experiment with
GPS radio occultation measurements, Geophys. Res. Lett., 32, 1–4,
https://doi.org/10.1029/2004GL020806, 2005. a
IGS: Daily GNSS data, International GNSS Service, available at: http://www.igs.org, last access: 5 November 2021. a
Johnston, G., Riddell, A., and Hausler, G.: The international GNSS service, in: Springer handbook of global navigation satellite systems, Springer, Cham, Switzerland, 967–982, 2017. a
Kačmařík, M., Douša, J., Zus, F., Václavovic, P., Balidakis, K., Dick, G., and Wickert, J.: Sensitivity of GNSS tropospheric gradients to processing options, Ann. Geophys., 37, 429–446, https://doi.org/10.5194/angeo-37-429-2019, 2019. a, b, c, d
Kawabata, T., Shoji, Y., Seko, H., and Saito, K.: A numerical study on a
mesoscale convective system over a subtropical island with 4D-var
assimilation of GPS slant total delays, J. Meteorol. Soc. Jpn., 91, 705–721, https://doi.org/10.2151/jmsj.2013-510, 2013. a
Lagler, K., Schindelegger, M., Böhm, J., Krásná, H., and Nilsson, T.: GPT2:
Empirical slant delay model for radio space geodetic techniques, Geophys.
Res. Lett., 40, 1069–1073, https://doi.org/10.1002/grl.50288, 2013. a
Li, X., Zus, F., Lu, C., Dick, G., Ning, T., Ge, M., Wickert, J., and Schuh,
H.: Retrieving of atmospheric parameters from multi-GNSS in real time:
Validation with water vapor radiometer and numerical weather model, J. Geophys. Res., 120, 7189–7204, https://doi.org/10.1002/2015JD023454,
2015a. a, b, c, d
Li, X., Zus, F., Lu, C., Ning, T., Dick, G., Ge, M., Wickert, J., and Schuh,
H.: Retrieving high-resolution tropospheric gradients from multiconstellation
GNSS observations, Geophys. Res. Lett., 42, 4173–4181,
https://doi.org/10.1002/2015GL063856, 2015b. a
Lindskog, M., Ridal, M., Thorsteinsson, S., and Ning, T.: Data assimilation of GNSS zenith total delays from a Nordic processing centre, Atmos. Chem. Phys., 17, 13983–13998, https://doi.org/10.5194/acp-17-13983-2017, 2017. a
Lu, C., Li, X., Li, Z., Heinkelmann, R., Nilsson, T., Dick, G., Ge, M., and
Schuh, H.: GNSS tropospheric gradients with high temporal resolution and
their effect on precise positioning, J. Geophys. Res., 121,
912–930, https://doi.org/10.1002/2015JD024255, 2016. a, b, c, d
Lu, C., Feng, G., Zheng, Y., Zhang, K., Tan, H., Dick, G., Wickert, J., and
Wickert, J.: Real-time retrieval of precipitable water vapor from Galileo
observations by using the MGEX network, IEEE T. Geosci. Remote, 58, 4743–4753, https://doi.org/10.1109/TGRS.2020.2966774, 2020. a
Petit, G. and Luzum, B.: IERS conventions, Tech. rep., International Earth Rotation and Reference Systems Service, Central Bureau, Frankfurt am Main, Germany, 2010. a
Poli, P., Moll, P., Rabier, F., Desroziers, G., Chapnik, B., Berre, L., Healy, S. B., Andersson, E., and Guelai, F.-Z. E.: Forecast impact studies of zenith total delay data from European near real-time GPS stations in Météo France 4DVAR, J. Geophys. Res., 112, D06114,
https://doi.org/10.1029/2006JD007430, 2007. a, b
Ramatschi, M., Bradke, M., Nischan, T., and Männel, B.: GNSS data of the
global GFZ tracking network, V. 1, GFZ Data Services [data set],
https://doi.org/10.5880/GFZ.1.1.2020.001, 2019. a, b
Rohm, W., Guzikowski, J., Wilgan, K., and Kryza, M.: 4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF, Atmos. Meas. Tech., 12, 345–361, https://doi.org/10.5194/amt-12-345-2019, 2019. a, b
Saito, K., Shoji, Y., Origuchi, S., and Duc, L.: GPS PWV assimilation with the JMA nonhydrostatic 4DVAR and cloud resolving ensemble forecast for the 2008 August Tokyo metropolitan area local heavy rainfalls, in: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, vol. III, 383–404, Springer, https://doi.org/10.1007/978-3-319-43415-5_17, 2017. a
Shehaj, E., Wilgan, K., Frey, O., and Geiger, A.: A collocation framework to
retrieve tropospheric delays from a combination of GNSS and InSAR,
Navigation, J. Inst. Navig., 67, 823–842,
https://doi.org/10.1002/navi.398, 2020. a
Smith, T. L., Benjamin, S. G., Schwartz, B. E., and Gutman, S. I.: Using
GPS-IPW in a 4-D data assimilation system, Earth Planets Space, 52,
921–926, https://doi.org/10.1186/BF03352306, 2000. a
Teke, K., Böhm, J., Nilsson, T., Schuh, H., Steigenberger, P., Dach, R.,
Heinkelmann, R., Willis, P., Haas, R., García-Espada, S., Hobiger, T.,
Ichikawa, R., and Shimizu, S.: Multi-technique comparison of troposphere
zenith delays and gradients during CONT08, J. Geodesy, 85, 395–413,
https://doi.org/10.1007/s00190-010-0434-y, 2011. a
Thayer, G. D.: An improved equation for the radio refractive index of air,
Radio Sci., 9, 803–807, https://doi.org/10.1029/RS009i010p00803, 1974. a
Vedel, H., Mogensen, K., and Huang, X.-Y.: Calculation of zenith delays from
meteorological data comparison of NWP model, radiosonde and GPS delays,
Phys. Chem. Earth Pt. A, 26, 497–502, https://doi.org/10.1016/S1464-1895(01)00091-6, 2001. a
Wickert, J., Dick, G., Schmidt, T., Asgarimehr, M., Antonoglou, N., Arras, C., Brack, A., Ge, M., Kepkar, A., Männel, B., Nguyen, C., Oluwadare, T. S., Schuh, H., Semmling, M., Simeonov, T., Vey, S., Wilgan, K., and Zus, F.: GNSS Remote Sensing at GFZ: Overview and Recent Results, ZfV: Zeitschrift für Geodäsie, Geoinformation und Landmanagement, 145, 266–278,
https://doi.org/10.12902/zfv-0320-2020, 2020. a
Wilgan, K., Rohm, W., and Bosy, J.: Multi-observation meteorological and GNSS
data comparison with Numerical Weather Prediction model, Atmos. Res., 156, 29–42, https://doi.org/10.1016/j.atmosres.2014.12.011, 2015. a
Zus, F., Wickert, J., Bauer, H. S., Schwitalla, T., and Wulfmeyer, V.:
Experiments of GPS slant path data assimilation with an advanced MM5
4DVAR system, Meteorol. Z., 20, 173–184, 2011. a
Zus, F., Bender, M., Deng, Z., Dick, G., Heise, S., Shang-Guan, M., and
Wickert, J.: A methodology to compute GPS slant total delays in a numerical
weather model, Radio Sci., 47, 1–15, https://doi.org/10.1029/2011RS004853, 2012. a, b
Zus, F., Dick, G., Douša, J., Heise, S., and Wickert, J.: The rapid and
precise computation of GPS slant total delays and mapping factors utilizing a
numerical weather model, Radio Sci., 49, 207–216,
https://doi.org/10.1002/2013RS005280, 2014. a, b
Zus, F.: Tropospheric parameters based on ERA5 data, ECMWF, available at: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5, last access: 5 November 2021.
a
Zus, F., Douša, J., Kačmařík, M., Václavovic, P., Dick, G., and Wickert,
J.: Estimating the impact of Global Navigation Satellite System horizontal
delay gradients in variational data assimilation, Remote Sens., 11, 41,
https://doi.org/10.3390/rs11010041, 2019. a, b
Short summary
The assimilation of GNSS data in weather models has a positive impact on the forecasts. The impact is still limited due to using only the GPS zenith direction parameters. We calculate and validate more advanced tropospheric products from three satellite systems: the US American GPS, Russian GLONASS and European Galileo. The quality of all the solutions is comparable; however, combining more GNSS systems enhances the observations' geometry and improves the quality of the weather forecasts.
The assimilation of GNSS data in weather models has a positive impact on the forecasts. The...