Articles | Volume 9, issue 11
https://doi.org/10.5194/amt-9-5385-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/amt-9-5385-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe
Guergana Guerova
CORRESPONDING AUTHOR
Department Meteorology and Geophysics, Sofia University St. Kliment Ohridski, 1164 Sofia, Bulgaria
Jonathan Jones
Met Office, EX1 3PB Exeter, UK
Jan Douša
New Technologies for the Information Society, Geodetic Observatory Pecný, RIGTC, 25066 Zdiby, Czech Republic
Galina Dick
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Siebren de Haan
Koninklijk Nederlands Meteorologisch Instituut, 3732 GK De Bilt, the Netherlands
Eric Pottiaux
Royal Observatory of Belgium, 1180 Brussels, Belgium
Olivier Bock
Institut National de l'Information Géographique et Forestière, 94160 Saint-Mande, France
Rosa Pacione
E-geos s.p.a ASI/CGS, 75100 Matera, Italy
Gunnar Elgered
Department of Earth and Space Sciences, Chalmers University of Technology, 43992 Onsala, Sweden
Henrik Vedel
Danish Meteorological Institute, 2100 Copenhagen, Denmark
Michael Bender
Deutscher Wetterdienst, 63067 Offenbach, Germany
Related authors
No articles found.
Jana Fischereit, Bjarke T. E. Olsen, Marc Imberger, Henrik Vedel, Kristian H. Møller, Andrea N. Hahmann, and Xiaoli Guo Larsén
EGUsphere, https://doi.org/10.5194/egusphere-2025-5407, https://doi.org/10.5194/egusphere-2025-5407, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We evaluated how operating wind farms influence the atmosphere in numerical weather prediction using two wind farm parameterizations in the HARMONIE-AROME model, applied by over 10 European weather services. Accurate yield forecasts require including both onshore and offshore turbines. Wind turbines slightly alter near-surface temperature (<1 K on average). We also present an open-access European wind turbine dataset combining multiple data sources.
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
Short summary
Short summary
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.
Tanguy Jonville, Maurus Borne, Cyrille Flamant, Juan Cuesta, Olivier Bock, Pierre Bosser, Christophe Lavaysse, Andreas Fink, and Peter Knippertz
Atmos. Chem. Phys., 25, 9765–9786, https://doi.org/10.5194/acp-25-9765-2025, https://doi.org/10.5194/acp-25-9765-2025, 2025
Short summary
Short summary
Tropical waves structure the atmosphere. Four types of tropical waves (equatorial Rossby – ER, Kelvin, MRG-TD1, and MRG-TD2 – mixed Rossby gravity–tropical depressions) are studied using filters, satellite measurements, and in situ data from the Clouds–Atmosphere Dynamics–Dust Interaction in West Africa (CADDIWA) campaign held in September 2021 in Cabo Verde. ER waves impact temperature and humidity above 2500 m, MRG-TD1 around 3500 m, and MRG-TD2 around 2000 m. Interactions between these waves favor tropical cyclone formation.
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
Short summary
Short summary
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.
Johanne Kristine Haandbæk Øelund, Jens Hesselbjerg Christensen, Rune Magnus Koktvedgaard Zeitzen, Henrik Vedel, and Henrik Feddersen
EGUsphere, https://doi.org/10.5194/egusphere-2025-3173, https://doi.org/10.5194/egusphere-2025-3173, 2025
Short summary
Short summary
This study explores how a powerful storm like Anatol, which hit Denmark in 1999, could change in a warmer future climate. Using a weather model, the storm was simulated under future temperature conditions. Results show stronger winds affecting larger areas for longer periods. A new index was introduced to measure storm severity. The findings highlight the growing risks to infrastructure and the need for better storm preparedness.
Siebren de Haan, Paul de Jong, Michal Koutek, Jan Sondij, and Lukas Strauss
Atmos. Meas. Tech., 18, 3341–3359, https://doi.org/10.5194/amt-18-3341-2025, https://doi.org/10.5194/amt-18-3341-2025, 2025
Short summary
Short summary
This paper describes the operational method of extracting meteorological information from all airborne aircraft in the European airspace every 20 s to 1 min. The methodology is described, and the quality of the wind and temperature observations is evaluated against radiosonde and other aircraft observations.
Tong Ning and Gunnar Elgered
Atmos. Meas. Tech., 18, 2069–2082, https://doi.org/10.5194/amt-18-2069-2025, https://doi.org/10.5194/amt-18-2069-2025, 2025
Short summary
Short summary
We analyzed signal delays in the atmosphere, using eight co-located global navigation satellite system (GNSS) stations, and compared linear horizontal gradients with microwave radiometer data. A weak constraint applied when estimating gradients in the GNSS data processing, enhancing short-lived gradient tracking but increasing noise, which can be mitigated by averaging gradients from the eight stations. This approach may enhance GNSS-based atmospheric monitoring for use in weather nowcasting.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Short summary
Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Olivier Bock, Pierre Bosser, and Carl Mears
Atmos. Meas. Tech., 15, 5643–5665, https://doi.org/10.5194/amt-15-5643-2022, https://doi.org/10.5194/amt-15-5643-2022, 2022
Short summary
Short summary
Integrated water vapour measurements are often compared for the calibration and validation of instruments or techniques. Measurements made at different altitudes must be corrected to account for the vertical variation of water vapour. This paper shows that the widely used empirical correction model has severe limitations that are overcome using the proposed model. The method is applied to the inter-comparison of GPS and satellite microwave radiometer data in a tropical mountainous area.
Siebren de Haan, Paul M. A. de Jong, and Jitze van der Meulen
Atmos. Meas. Tech., 15, 811–818, https://doi.org/10.5194/amt-15-811-2022, https://doi.org/10.5194/amt-15-811-2022, 2022
Short summary
Short summary
AMDAR temperatures suffer from a bias, which can be related to a difference in the timing of height and measurement and to internal corrections applied to pressure altitude. Based on NWP model temperature data, combined with Mach number and true airspeed, we could estimate corrections. Comparing corrected temperatures with (independent) radiosonde observations demonstrates a reduction in the bias, from 0.5 K to around zero, and standard deviation, of almost 10 %.
Karina Wilgan, Galina Dick, Florian Zus, and Jens Wickert
Atmos. Meas. Tech., 15, 21–39, https://doi.org/10.5194/amt-15-21-2022, https://doi.org/10.5194/amt-15-21-2022, 2022
Short summary
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.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
Short summary
Short summary
The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Tong Ning and Gunnar Elgered
Atmos. Meas. Tech., 14, 5593–5605, https://doi.org/10.5194/amt-14-5593-2021, https://doi.org/10.5194/amt-14-5593-2021, 2021
Short summary
Short summary
We have estimated horizontal gradients of the propagation delay caused by water vapour in the atmosphere using two independent techniques, namely global navigation satellite systems (GNSS) and microwave radiometry. The highest resolution was 5 min. We found that the sampling of the atmosphere in different directions is an important factor for high correlations between the two techniques and that GNSS data can be used to detect large short-lived gradients, however, with increased formal errors.
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
Short summary
Short summary
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).
Olivier Bock, Pierre Bosser, Cyrille Flamant, Erik Doerflinger, Friedhelm Jansen, Romain Fages, Sandrine Bony, and Sabrina Schnitt
Earth Syst. Sci. Data, 13, 2407–2436, https://doi.org/10.5194/essd-13-2407-2021, https://doi.org/10.5194/essd-13-2407-2021, 2021
Short summary
Short summary
Measurements from a network of Global Navigation Satellite System (GNSS) receivers operated from the eastern Caribbean islands are used to monitor the total water vapour content in the atmosphere during the EUREC4A field campaign. These data help describe the moisture environment of mesoscale cloud patterns in the trade winds with high temporal sampling. They are also useful to assess the accuracy of collocated radiosonde measurements and numerical weather model reanalyses.
Pierre Bosser, Olivier Bock, Cyrille Flamant, Sandrine Bony, and Sabrina Speich
Earth Syst. Sci. Data, 13, 1499–1517, https://doi.org/10.5194/essd-13-1499-2021, https://doi.org/10.5194/essd-13-1499-2021, 2021
Short summary
Short summary
In the framework of the EUREC4A campaign, water vapour measurements were retrieved over the tropical west Atlantic Ocean from GNSS data acquired from three research vessels (R/Vs Atalante, Maria S. Merian and Meteor). The retrievals from R/Vs Atalante and Meteor are shown to be of high quality unlike the results for the R/V Maria S. Merian. These ship-borne retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign.
Pierre Bosser and Olivier Bock
Adv. Geosci., 55, 13–22, https://doi.org/10.5194/adgeo-55-13-2021, https://doi.org/10.5194/adgeo-55-13-2021, 2021
Short summary
Short summary
For the documentation of time and space variations of water vapor in atmosphere during the Nawdex campaign (fall 2016), a ground network of more than 1200 continuously operation reference GNSS stations has been analyzed. This network spreads from Caribbeans to Morocco through Greenland. This study presents the retrieval of Integrated Water Vapor content from GNSS measurements and their use in the evaluation of the European Centre for Medium-Range Weather Forecasts reanalyses ERAI and ERA5.
Cited articles
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and the hydrologic cycle, Nature, 419, 224–232, https://doi.org/10.1038/nature01092, 2002.
Arriola, J. S., Lindskog, M., Thorsteinsson, S., and Bojarova, J.: Variational Bias Correction of GNSS ZTD in the HARMONIE Modeling System, J. App. Meteorol. Clim., 55, 1259–1276, https://doi.org/10.1175/JAMC-D-15-0137.1, 2016.
Baelen, J. V., Reverdy, M., Tridon, F., Labbouz, L., Dick, G., Bender, M., and Hagen, M.: On the relationship between water vapour field evolution and the life cycle of precipitation systems, Q. J. Roy. Meteor. Soc., 137, 204–223, https://doi.org/10.1002/qj.785, 2011.
Bar-Sever, Y.: Reprocessed IGS Trop Product now available with Gradients, IGSMAIL-6298, available at: http://igscb.jpl.nasa.gov/pipermail/igsmail/2010/007488.html, 2012.
Bauer, H.-S., Wulfmeyer, V., Schwitalla, T., Zus, F., and Grzeschik, M.: Operational assimilation of GPS slant path delay measurements into the MM5 4DVAR system, Tellus A, 63, 263–282, https://doi.org/10.1111/j.1600-0870.2010.00489.x, 2011.
Behrend, D., Haas, R., D. Pino, L. P. G., Keihm, S. J., Schwarz, W., Cucurull, L., and Rius, A.: MM5 derived ZWDs compared to observational results from VLBI, GPS and WVR, Phys. Chem. Earth Pt. A, 27, 301–308, https://doi.org/10.1016/S1474-7065(02)00004-9, 2002.
Bender, M., Dick, G., Ge, M., Deng, Z., Wickert, J., Kahle, H.-G., Raabe, A., and Tetzlaff, G.: Development of a GNSS Water Vapor Tomography System Using Algebraic Reconstruction Techniques, Adv. Space Res., 47, 1704–1720, https://doi.org/10.1016/j.asr.2010.05.034, 2011a.
Bender, M., Stosius, R., Zus, F., Dick, G., Wickert, J., and Raabe, A.: GNSS water vapour tomography – Expected improvements by combining GPS, GLONASS and Galileo observations, Adv. Space Res., 47, 886–897, https://doi.org/10.1016/j.asr.2010.09.011, 2011b.
Benevides, P., Catalao, J., and Miranda, P. M.: Experimental GNSS tomography study in Lisbon (Portugal), Fisica de la Tierra, 26, 65–79, https://revistas.ucm.es/index.php/FITE/article/view/46972, 2014.
Bengtsson, L.: The global atmospheric water cycle, Environ. Res. Lett., 5, 025202, https://doi.org/10.1088/1748-9326/5/2/025202, 2010.
Bennitt, G. V. and Jupp, A.: Operational Assimilation of GPS Zenith Total Delay Observations into the Met Office Numerical Weather Prediction Models, Mon. Weather Rev., 140, 2706–2719, https://doi.org/10.1175/MWR-D-11-00156.1, 2012.
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware, R. H.: GPS Meteorology: Remote Sensing of Atmospheric Water Vapour Using the Global Positioning System, J. Geophys. Res., 97, 15787–15801, https://doi.org/10.1029/92JD01517, 1992.
Bevis, M., Businger, S., Chiswell, S., Herring, T. A., Anthes, R. A., Rocken, C., and Ware, R. H.: GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water, J. App. Meteorol., 33, 379–386, https://doi.org/10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2, 1994.
Bock, O., Keil, C., Richard, E., Flamant, C., and Bouin, M.-N.: Validation of precipitable water from ECMWF model analyses with GPS and radiosonde data during the MAP SOP, Q. J. Roy. Meteorol. Soc., 131, 3013–3036, https://doi.org/10.1256/qj.05.27, 2005.
Bock, O., Bouin, M. N., Walpersdorf, A., Lafore, J. P., Janicot, S., Guichard, F., and Agusti-Panareda, A.: Comparison of ground-based GPS precipitable water vapour to independent observations and NWP model reanalyses over Africa, Q. J. Roy. Meteor. Soc., 133, 2011–2027, https://doi.org/10.1002/qj.185, 2007.
Bock, O., Willis, P., Lacarra, M., and Bosser, P.: An inter-comparison of zenith tropospheric delays derived from DORIS and GPS data, Adv. Space Res., 46, 1648–1660, https://doi.org/10.1016/j.asr.2010.05.018, 2010.
Bock, O., Willis, P., Wang, J., and Mears, C.: A high-quality, homogenized, global, long-term (1993–2008) DORIS precipitable water data set for climate monitoring and model verification, J. Geophys. Res., 119, 7209–7230, https://doi.org/10.1002/2013JD021124, 2014.
Böhm, J. and Schuh, S.: Vienna Mapping Function in VLBI Analysis, Geophys. Res. Lett., 31, L01603, https://doi.org/10.1029/2003GL018984, 2004.
Böhm, J. and Schuh, S.: Troposheric gradients from the ECMWF in VLBI analysis, J. Geod., 81, 403–408, https://doi.org/10.1007/s00190-007-0144-2, 2007.
Braun, J., Rocken, C., Meertens, C., and Ware, R.: Development of a Water Vapor Tomography System Using Low Cost L1 GPS Receivers, 9th ARM Science Team Meeting Proceedings, San Antonio, Texas, 22–26 March, 1999.
Brenot, H., Ducrocq, V., Walpersdorf, A., Champollion, C., and Caumont, O.: GPS zenith delay sensitivity evaluated from high-resolution numerical weather prediction simulations of the 8–9 September 2002 flash flood over southeastern France, J. Geophys. Res., 111, D15105, https://doi.org/10.1029/2004JD005726, 2006.
Brenot, H., Neméghaire, J., Delobbe, L., Clerbaux, N., De Meutter, P., Deckmyn, A., Delcloo, A., Frappez, L., and Van Roozendael, M.: Preliminary signs of the initiation of deep convection by GNSS, Atmos. Chem. Phys., 13, 5425–5449, https://doi.org/10.5194/acp-13-5425-2013, 2013.
Brenot, H., Walpersdorf, A., Reverdy, M., van Baelen, J., Ducrocq, V., Champollion, C., Masson, F., Doerflinger, E., Collard, P., and Giroux, P.: A GPS network for tropospheric tomography in the framework of the Mediterranean hydrometeorological observatory Cévennes-Vivarais (southeastern France), Atmos. Meas. Tech., 7, 553–578, https://doi.org/10.5194/amt-7-553-2014, 2014.
Buehler, S. A., Östman, S., Melsheimer, C., Holl, G., Eliasson, S., John, V. O., Blumenstock, T., Hase, F., Elgered, G., Raffalski, U., Nasuno, T., Satoh, M., Milz, M., and Mendrok, J.: A multi-instrument comparison of integrated water vapour measurements at a high latitude site, Atmos. Chem. Phys., 12, 10925–10943, https://doi.org/10.5194/acp-12-10925-2012, 2012.
Byram, S. and Hackman, C.: Computation of the IGS Final Troposphere Product by the USNO, IGS workshop poster presentation, 2012.
Byun, S. and Bar-Sever, Y.: A new type of troposphere zenith path delay product of the international GNSS service, J. Geod., 83, 367–373, https://doi.org/10.1007/s00190-008-0288-8, 2009.
Caissy, M., Agrotis, L., Weber, G., Hernandez-Pajares, M., and Hugentobler, U.: INNOVATION-Coming Soon-The International GNSS Real-Time Service, GPS World, 23, 52–58, 2012.
Champollion, C., Flamant, C., Bock, O., Masson, F., Turner, D., and Weckwerth, T.: Mesoscale GPS tomography applied to the 12 June 2002 convective initiation event of IHOP 2002, Q. J. Roy. Meteor. Soc., 135, 645–662, https://doi.org/10.1002/qj.386, 2009.
Chen, B. and Liu, Z.: Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model, J. Geod., https://doi.org/10.1007/s00190-014-0715-y, 2014.
Cucurull, L., Navascues, B., Ruffini, G., Elosegui, P., Rius, A., and Vila, J.: The use of GPS to validate NWP systems: the HIRLAM model, J. Atmos. Ocean. Tech., 17, 773–787, https://doi.org/10.1175/1520-0426(2000)017<0773:TUOGTV>2.0.CO;2, 2000.
Cucurull, L., van den Berghe, F., Barker, D., Vilaclara, E., and Rius, A.: Three-Dimensional Variational Data Assimilation of Ground-Based GPS ZTD and Meteorological Observations during the 14 December 2001 Storm Event over the Western Mediterranean Sea, Mon. Weather Rev., 132, 749–763, https://doi.org/10.1175/1520-0493(2004)132<0749:TVDAOG>2.0.CO;2, 2004.
Dach, R., Böhm, J., Lutz, S., Steigenberger, P., and Beutler, G.: Evaluation of the impact of atmospheric pressure loading modeling on GNSS data analysis, J. Geod., 85, 75–91, https://doi.org/10.1007/s00190-010-0417-z, 2011a.
Dach, R., Schmid, R., Schmitz, M., Thaller, D., Schaer, S., Lutz, S., Steigenberger, P., Wuebbena, G., and Beutler, G.: Improved antenna phase center models for GLONASS, J. Geod., 15, 49–65, https://doi.org/10.1007/s10291-010-0169-5, 2011b.
Davis, J., Herring, T., Shapiro, I., Rogers, A., and Elgered, G.: Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length, Radio Sci., 20, 1593–1607, https://doi.org/10.1029/RS020i006p01593, 1985.
de Haan, S.: Meteorological applications of a surface network of Global Positioning System receivers, PhD thesis, Wageningen Universiteit, the Netherlands, available at: https://www.knmi.nl/kennis-en-datacentrum/publicatie/meteorological-applications-of-a-surface-network-of-global-positioning-system-receivers, 2008.
de Haan, S.: Assimilation of GNSS ZTD and radar radial velocity for the benefit of very-short-range regional weather forecasts, Q. J. Roy. Meteor. Soc., 2097–2107, https://doi.org/10.1002/qj.2087, 2013.
de Haan, S., van der Marel, H., and Barlag, S.: Comparison of GPS slant delay measurements to a numerical model: case study for a cold front passage, Phys. Chem. Earth, 27, 317–322, https://doi.org/10.1016/S1474-7065(02)00006-2, 2002.
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.
Douša, J.: Towards an Operational Near-real Time Precipitable Water Vapor Estimation, Phys. Chem. Earth Pt. A, 26, 189–194, https://doi.org/10.1016/S1464-1895(01)00045-X, 2001a.
Douša, J.: The Impact of Ultra-Rapid Orbits on Precipitable Water Vapor Estimation using Ground GPS Network, Phys. Chem. Earth Pt. A, 26, 393–398, https://doi.org/10.1016/S1464-1895(01)00072-2, 2001b.
Douša, J.: Precise near real-time GNSS analyses at Geodetic Observatory Pecny – precise orbit determination and water vapour monitoring, Acta Geodyn. Geomater., 7, 7–18, 2010.
Douša, J. and Bennitt, G. V.: Estimation and evaluation of hourly updated global GPS Zenith Total Delays over ten months, GPS Solutions, 17, 453–464, https://doi.org/10.1007/s10291-012-0291-7, 2013.
Douša, J. and Vaclavovic, P.: Real-time zenith tropospheric delays in support of numerical weather prediction applications, Adv. Space Res., 53, 1347–1358, https://doi.org/10.1016/j.asr.2014.02.021, 2014.
Dow, J. M., Neilan, R. E., and Rizos, C.: The International GNSS Service in a Changing Landscape of Global Navigation Satellite Systems, J. Geod., 83, 191–198, https://doi.org/10.1007/s00190-008-0300-3, 2009.
Elgered, G., Plag, H., Marel, H. V. D., Barlag, S., and Nash, J.: Exploitation of Ground-based GPS for Operational Numerical Weather Prediction and Climate Applications – Final Report, EU Publications Office (OPOCE), 234 pp., ISBN: 92-898-0012-7, 2005.
Eresmaa, R., Järvinen, H., Niemelä, S., and Salonen, K.: Asymmetricity of ground-based GPS slant delay data, Atmos. Chem. Phys., 7, 3143–3151, https://doi.org/10.5194/acp-7-3143-2007, 2007.
Eresmaa, R., Salonen, K., and Järvinen, H.: An observing-system experiment with ground-based GPS zenith total delay data using HIRLAM 3D-Var in the absence of satellite data, Q. J. Roy. Meteor. Soc., 136, 1289–1300, https://doi.org/10.1002/qj.632, 2010.
Faccani, C., Ferretti, R., Pacione, R., Paolucci, T., Vespe, F., and Cucurull, L.: Impact of a high density GPS network on the operational forecast, Adv. Geosci., 2, 73–79, https://doi.org/10.5194/adgeo-2-73-2005, 2005.
Field, C., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M., Mach, K., Plattner, G., Allen, S., Tignor, M., and Midgley, P.: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, New York, NY, USA, available at: https://www.ipcc.ch/pdf/special-reports/srex/SREX_Full_Report.pdf, 2012.
Flores, A., Rius, A., de Arellano, J. V.-G., and Escudero, A.: Spatio-temporal tomography of the lower troposphere using GPS signals, Phys. Chem. Earth Pt. A, 26, 405–411, https://doi.org/10.1016/S1464-1895(01)00074-6, 2001.
Foelsche, U. and Kirchengast, G.: Tropospheric water vapor imaging by combination of ground-based and spaceborne GNSS sounding data, J. Geophys. Res., 106, 221–27, https://doi.org/10.1029/2001JD900230, 2001.
Gaffen, D. J., Elliott, W., and Robock, A.: Relationships between tropospheric water vapor and surface temperature as observed by radiosondes, Geophys. Res. Lett., 19, 1839–1842, https://doi.org/10.1029/92GL02001, 1992.
Gendt, G.: IGS combination of tropospheric estimates – The pilot experiment, 1997 technical reports, IGS, JPL, Pasadena, California, USA, available at: ftp://igscb.jpl.nasa.gov/igscb/resource/pubs/pil_proj255-292.pdf, 1998.
Gendt, G., Reigber, C., and Dick, G.: Near Real-Time Water Vapor Estimation in a German GPS Network – Results from the Ground Program of HGF GASP Project, Phys. Chem. Earth Pt. A, 26, 413–416, https://doi.org/10.1016/S1464-1895(01)00075-8, 2001.
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. Meteor. Soc. Jpn., 82, 361–370, https://doi.org/10.2151/jmsj.2004.361, 2004.
Gradinarsky, L. P., Johansson, J., Bouma, H. R., Scherneck, H. G., and Elgered, G.: Climate monitoring using GPS, Phys. Chem. Earth, 27, 335–340, https://doi.org/10.1016/S1474-7065(02)00009-8, 2002.
Graham, E., Koffi, E. N., and Matzler, C.: An observational study of air and water vapour convergence over the western Alps during summer and the development of isolated thunderstorms, Meteorol. Z., 21, 1–13, https://doi.org/10.1127/0941-2948/2012/0347, 2012.
Guerova, G. and Tomassini, M.: Monitoring IWV from GPS and limited – area forecast model, Tech. Rep. 2003–15, University of Bern, Switzerland, available at: http://www.iap.unibe.ch/publications/pub-detail.php?lang=en&id=729, September 2003.
Guerova, G., Brockmann, E., Quiby, J., Schubiger, F., and Matzler, C.: Validation of NWP mesoscale models with Swiss GPS Network AGNES, J. App. Meteorol., 42, 141–150, https://doi.org/10.1175/1520-0450(2003)042<0141:VONMMW>2.0.CO;2, 2003.
Guerova, G., Bettems, J.-M., Brockmann, E., and Matzler, C.: Assimilation of the GPS-derived Integrated Water Vapour (IWV) in the MeteoSwiss Numerical Weather Prediction model – a first experiment, Phys. Chem. Earth, 29, 177–186, https://doi.org/10.1016/j.pce.2004.01.009, 2004.
Guerova, G., Brockmann, E., Schubiger, F., Morland, J., and Matzler, C.: An integrated assessment of measured and modeled IWV in Switzerland for the period 2001–2003, J. App. Meteorol., 44, 1033–1044, https://doi.org/10.1175/JAM2255.1, 2005.
Guerova, G., Bettems, J.-M., Brockmann, E., and Matzler, C.: Assimilation of COST 716 Near Real Time GPS data in the nonhydrostatic limited area model used at MeteoSwiss, Meteorol. Atmos. Phys., 91, 149–164, https://doi.org/10.1007/s00703-005-0110-6, 2006.
Haase, J., Calais, E., Talaya, J., Rius, A., Vespe, F., Santangelo, R., Huang, X.-Y., Davila, J. M., Ge, M., Cucurull, L., Flores, A., Sciarretta, C., Pacione, R., Boccolari, M., Pugnaghi, S., Vedel, H., Mogensen, K., Yang, X., and Garate, J.: The contributions of the MAGIC project to the COST 716 objectives of assessing the operational potential of ground-based GPS meteorology on an international scale, Phys. Chem. Earth Pt. A, 26, 433–437, https://doi.org/10.1016/S1464-1895(01)00079-5, 2001.
Haase, J., Ge, M., Vedel, H., and Calais, E.: Accuracy and variability of GPS tropospheric delay measurements of water vapor in the western Mediterranean, J. App. Meteorol., 42, 1547–1568, https://doi.org/10.1175/1520-0450(2003)042<1547:AAVOGT>2.0.CO;2, 2003.
Heise, S., Dick, G., Gendt, G., Schmidt, T., and Wickert, J.: Integrated water vapor from IGS ground-based GPS observations: initial results from a global 5-min data set, Ann. Geophys., 27, 2851–2859, https://doi.org/10.5194/angeo-27-2851-2009, 2009.
Held, I. M. and Soden, B. J.: Robust responses of the hydrological cycle to global warming, J. Clim., 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1, 2006.
Henriksen, S. W., Mancini, A., and Chovitz, B. H. (Eds.): The Use of Artificial Satellites for Geodesy, American Geophysical Union, Washington, DC, https://doi.org/10.1029/GM015p0247, 1972.
Jarlemark, P., Emardson, R., Johansson, J., and Elgered, G.: Ground-Based GPS for Validation of Climate Models: The Impact of Satellite Antenna Phase Center Variations, IEEE T. Geosci. Remote, 48, 3847–3854, https://doi.org/10.1109/TGRS.2010.2049114, 2010.
Järvinen, H., Eresmaa, R., Vedel, H., Salonen, K., Niemelä, S., and de Vries, J.: A variational data assimilation system for ground-based GPS slant delays, Q. J. Roy. Meteor. Soc., 133, 969–980, https://doi.org/10.1002/qj.79, 2007.
Jin, S. G., Luo, O. F., and Gleason, S.: Characterization of diurnal cycles in ZTD from a decade of global GPS observation, J. Geod., 83, 537–545, https://doi.org/10.1007/s00190-008-0264-3, 2009.
Johansson, J. M., Emardson, T. R., Jarlemark, P. O. J., Gradinarsky, L. P., and Elgered, G.: The atmospheric influence on the results from the Swedish GPS network, Phys. Chem. Earth, 23, 107–112, https://doi.org/10.1016/S0079-1946(97)00251-6, 1998.
Kačmařík, M. and Rapant, L.: New GNSS tomography of the atmosphere method – proposal and testing, in: Geoinformatics, edited by: Čepek, A., Czech Technical University, 9, 63–76, http://geoinformatics.fsv.cvut.cz/pdf/geoinformatics-fce-ctu-2012-09.pdf, 2012.
Kačmařík, M., Douša, J., and Zapletal, J.: Comparison of GPS slant wet delays acquired by different techniques, Acta Geodyn. Geomater., 9, 427–433, 2012.
Keernik, H., Ohvrila, H., Jakobsona, E., Rannat, K., and Luhamaaa, A.: Column water vapour: an intertechnique comparison of estimation methods in Estonia, P. Est. Acad. Sci., 63, 37–47, https://doi.org/10.3176/proc.2014.1.07, 2014.
Kehrer, K., Graf, B., and Roeder, W. P.: Global positioning system (GPS) precipitable water in forecasting lightning at spaceport Canaveral, Weather Forecast., 23, 219–232, https://doi.org/10.1175/2007WAF2006105.1, 2008.
Köpken, C.: Validation of integrated water vapor from numerical models using ground-based GPS, SSM/I, and water vapor radiometer measurements, J. App. Meteorol., 40, 1105–1117, https://doi.org/10.1175/1520-0450(2001)040<1105:VOIWVF>2.0.CO;2, 2001.
Labbouz, L., Baelen, J. V., Tridon, F., Reverdy, M., Hagen, M., Bender, M., Dick, G., and Gorgas, T.: Precipitation on the lee side of the Vosges Mountains: Multi-instrumental study of one case from the COPS campaign, Meteorol. Z., 22, 413–432, https://doi.org/10.1127/0941-2948/2013/0413, 2013.
Li, M., Li, W., Shi, C., Zhao, Q., Su, X., Qu, L., and Liu, Z.: Assessment of Precipitable Water Vapor Derived from Ground-based BeiDou Observations with Precise Point Positioning Approach, Adv. Space Res., https://doi.org/10.1016/j.asr.2014.10.010, 2014a.
Li, X., Dick, G., Ge, M., Heise, S., Wickert, J., and Bender, M.: Real-time GPS sensing of atmospheric water vapor: precise point positioning with orbit, clock and phase delay corrections, Geophys. Res. Lett., 41, 3615–3621, https://doi.org/10.1002/2013GL058721, 2014b.
Mahfouf, J.-F., Boullut, N., Moll, P., Payan, C., Wattrelot, E., Augros, C., and Caumont, O.: Observation usage in Meteo France data assimilation systems: Current status and planned evolutions, Aladin workshop presentation, available at: http://www.cnrm.meteo.fr/aladin/IMG/pdf/mahfouf.pdf, 2012.
Manning, T., Zhang, K., Rohm, W., Choy, S., and Hurter, F.: Detecting Severe Weather using GPS Tomography: An Australian Case Study, J. Glob. Positioning Syst., 11, 58–70, available at: http://www.gnss.com.au/JoGPS/v11n1/JoGPS_v11n1p58-70.pdf, 2012.
Mazany, R., Businger, S., and Gutman, S.: A lightning prediction index that utilizes GPS integrated precipitable water vapor, Weather Forecast., 17, 1034–1047, https://doi.org/10.1175/1520-0434(2002)017<1034:ALPITU>2.0.CO;2, 2002.
Miidla, P., Rannat, K., and Uba, P.: Tomographic approach for tropospheric water vapour detection, Comput. Meth. Appl. Math., 8, 263–278, https://doi.org/10.2478/cmam-2008-0019, 2008.
Moll, P., Poli, P., and Ducrocq, V.: Use of ground based GNSS data in NWP at Météo-France, e-GVAP expert team meeting, html://egvap.dmi.dk/workshop/5-assimilation-meteo-france-Moll.pdf, 2008.
Morland, J., Collaud Coen, M., Hocke, K., Jeannet, P., and Mätzler, C.: Tropospheric water vapour above Switzerland over the last 12 years, Atmos. Chem. Phys., 9, 5975–5988, https://doi.org/10.5194/acp-9-5975-2009, 2009.
Nilsson, T. and Elgered, G.: Long-term trends in the atmospheric water vapor content estimated from ground-based GPS data, J. Geophys. Res., 113, D19101, https://doi.org/10.1029/2008JD010110, 2008.
Nilsson, T., Gradinarsky, L., and Elgered, G.: Water vapour tomography using GPS phase observations: Results from the ESCOMPTE experiment, Tellus A, 59, 674–682, https://doi.org/10.1111/j.1600-0870.2007.00247.x, 2007.
Ning, T. and Elgered, G.: Trends in the atmospheric water vapor content from ground-based GPS: the impact of the elevation cutoff angle, IEEE J. Sel. Top. Appl., 5, 744–751, https://doi.org/10.1109/JSTARS.2012.2191392, 2012.
Ning, T., Haas, R., Elgered, G., and Willén, U.: Multi-technique comparisons of ten years of wet delay estimates on the west coast of Sweden, J. Geod., 86, 565–575, https://doi.org/10.1007/s00190-011-0527-2, 2012.
Ning, T., Elgered, G., Willen, U., and Johansson, J. M.: Evaluation of the atmospheric water vapor content in a regional climate model using ground-based GPS measurements, J. Geophys. Res., 118, 1–11, https://doi.org/10.1029/2012JD018053, 2013.
Notarpietro, R., Cucca, M., Gabella, M., Venuti, G., and Perona, G.: Tomographic reconstruction of Wet and Total Refractivity fields from GNSS receiver networks, Adv. Space Res., 47, 769–912, https://doi.org/10.1016/j.asr.2010.12.025, 2011.
Perler, D., Geiger, A., and Hurter, F.: 4D GPS water vapor tomography: new parameterized approaches, J. Geod., 85, 539–550, https://doi.org/10.1007/s00190-011-0454-2, 2011.
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 Meteo France 4DVAR, J. Geophys. Res., 112, D06114, https://doi.org/10.1029/2006JD007430, 2007.
Pottiaux, E., Bruyninx, C., Brockmann, E., and Söhne, W.: The EUREF-EUMETNET Collaboration: First experience and Potential Benefits, Bollettino di geodesia e scienze affini, 68, 269–286, ISBN: 0006-6710, 2009.
Reverdy, M., Baelen, J. V., Walpersdorf, A., Dick, G., Hagen, M., and Richard, E.: Water vapor fields retrieve with tomography software, Ann. Meteo., 44, 144–145, 2009.
Rohm, W.: The ground GNSS tomography – unconstrained approach, Adv. Space Res., 51, 501–513, https://doi.org/10.1016/j.asr.2012.09.021, 2013.
Rohm, W., Zhang, K., and Bosy, J.: Limited constraint, robust Kalman filtering for GNSS troposphere tomography, Atmos. Meas. Tech., 7, 1475–1486, https://doi.org/10.5194/amt-7-1475-2014, 2014.
Ross, R. J. and Elliott, W. P.: Tropospheric water vapor climatology and trends over North America: 1973–93, J. Climate, 9, 3561–3574, https://doi.org/10.1175/1520-0442(1996)009<3561:TWVCAT>2.0.CO;2, 1996.
Ross, R. J. and Elliott, W. P.: Radiosonde based northern hemisphere tropospheric water vapor trends, J. Climate, 14, 1602–1612, https://doi.org/10.1175/1520-0442(2001)014<1602:RBNHTW>2.0.CO;2, 2001.
Saqellari-Likoka, A. and Karathanassi, V.: An Approach for Solving Rank-Deficient Systems That Enable Atmospheric Path Delay and Water Vapor Content Estimation, IEEE T. Geosci. Remote, 46, 3187–3195, https://doi.org/10.1109/TGRS.2008.921744, 2008.
Schwitalla, T., Bauer, H.-S., Wulfmeyer, V., and Zängl, G.: Systematic errors of QPF in low-mountain regions as revealed by MM5 simulations, Meteorol. Z., 17, 903–919, https://doi.org/10.1127/0941-2948/2008/0338, 2008.
Schwitalla, T., Bauer, H.-S., Wulfmeyer, V., and Aoshima, F.: High-resolution simulation over central Europe: assimilation experiments during COPS IOP 9c, Q. J. Roy. Meteor. Soc., 137, 156–175, https://doi.org/10.1002/qj.721, 2011.
Seco, A., Ramirez, F., Serna, E., Prieto, E., Garcia, R., Moreno, A., Cantera, J. C., Miqueleiz, L., and Priego, J. E.: Rain pattern analysis and forecast model based on GPS estimated atmospheric water vapor content, Atmos. Environ., 49, 85–93, https://doi.org/10.1016/j.atmosenv.2011.12.019, 2012.
Sohn, D.-H. and Cho, J.: Trend Analysis of GPS Precipitable Water Vapor Above South Korea Over the Last 10 years, J. Astron. Space Sci., 27, 231–238, https://doi.org/10.5140/JASS.2010.27.3.231, 2010.
Spänkuch, D., Gueldner, J., Steinhagen, H., and Bender, M.: Analysis of a dryline-like feature in northern Germany detected by ground-based microwave profiling, Meteorol. Z., 20, 409–421, https://doi.org/10.1127/0941-2948/2011/0222, 2011.
Steigenberger, P., Tesmer, V., Krügel, M., Thaller, D., Schmid, R., Vey, S., and Rothacher, M.: Comparisons of homogeneously reprocessed GPS and VLBI long time-series of troposphere zenith delays and gradients, J. Geod., 81, 503–514, https://doi.org/10.1007/s00190-006-0124-y, 2007.
Stocker, T. F., Qin, D., Plattner, G., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. (Eds.): Climate change 2013: the physical science basis. Intergovernmental panel on climate change, working group I Contribution to the IPCC fifth assessment report (AR5), Cambridge University Press Cambridge, UK, New York, https://doi.org/10.1017/CBO9781107415324, 2013.
Suparta, W. and Ali, M.: Nowcasting the lightning activity in Peninsular Malaysia using the GPS PWV during the 2009 inter-monsoons, Ann. Geophys., 57, A0217, https://doi.org/10.4401/ag-6373, 2014.
Szintai, S. and Mile, M.: Update of current status and plans of C-SRNWP, EUMETNET Obset expert team meeting, ECMWF, Reading, UK, 15–17 April, 2015.
Teke, K., Nilsson, T., Böhm, J., Hobiger, T., Steigenberger, P., Garcia-Espada, S., Haas, R., and Willis, P.: Troposphere delays from space geodetic techniques, water vapor radiometers, and numerical weather models over a series of continuous VLBI campaigns, J. Geod., 87, 981–1001, https://doi.org/10.1007/s00190-013-0662-z, 2013.
Tomassini, M., Gendt, G., Dick, G., Ramatschi, M., and Schraff, C.: Monitoring of integrated water vapor from ground-based GPS observations and their assimilation in a limited-area model, Phys. Chem. Earth, 27, 341–346, https://doi.org/10.1016/S1474-7065(02)00010-4, 2002.
Tralli, D. M. and Lichten, S. M.: Stochastic estimation of tropospheric path delays in Global Positioning System geodetic measurements, B. Géodésique, 64, 127–152, https://doi.org/10.1007/BF02520642, 1990.
Trenberth, K. E., Fasullo, J., and Smit, L.: Trends and variability in column-integrated atmospheric water vapor, Clim. Dynam., 24, 741–758, https://doi.org/10.1007/s00382-005-0017-4, 2005.
Troller, M., Geiger, A., Brockmann, E., Bettems, J.-M., Bürki, B., and Kahle, H.-G.: Tomographic determination of the spatial distribution of water vapor using GPS observations, Adv. Space Res., 37, 2211–2217, https://doi.org/10.1016/j.asr.2005.07.002, 2006a.
Troller, M., Geiger, A., Brockmann, E., and Kahle, H.-G.: Determination of the spatial and temporal variation of tropospheric water vapour using CGPS networks, Geophys. J. Int., 167, 509–520, https://doi.org/10.1111/j.1365-246X.2006.03101.x, 2006b.
Urquhart, L., Nievinski, F. G., and Santos, M. C.: Assessment of troposphere mapping functions using three-dimensional ray-tracing, GPS Solutions, 18, 345–354, https://doi.org/10.1007/s10291-013-0334-8, 2014.
van Baelen, J., Reverdy, M., Tridon, F., Labbouz, L., Dick, G., Bender, M., and Hagen, M.: On the relationship between water vapour and evolution and the life cycle of precipitation systems, Q. J. Roy. Meteor. Soc., 137, 204–223, https://doi.org/10.1002/qj.785, 2011.
van der Marel, H., Brockmann, E., de Haan S, S., Douša, J., Johansson, J., Gendt, G., Kristiansen, O., Offiler, D., Pacione, R., Rius, A., and Vespe, F.: COST-716 demonstration project for the near real-time estimation of integrated water vapour from GPS, Phys. Chem. Earth, 29, 187–199, https://doi.org/10.1016/j.pce.2004.01.001, 2004.
Van Malderen, R., Brenot, H., Pottiaux, E., Beirle, S., Hermans, C., De Mazière, M., Wagner, T., De Backer, H., and Bruyninx, C.: A multi-site intercomparison of integrated water vapour observations for climate change analysis, Atmos. Meas. Tech., 7, 2487–2512, https://doi.org/10.5194/amt-7-2487-2014, 2014.
Vedel, H. and Huang, X.-Y.: Impact of ground based GPS data on numerical weather prediction, J. Meteor. Soc. Jpn., 82, 459–472, https://doi.org/10.2151/jmsj.2004.459, 2004.
Vedel, H., Mogensen, K. S., 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.
Vedel, H., Huang, X-Y and, J., Ge, M., and Calais, E.: Impact of GPS zenith tropospheric delay data on precipitation forecasts in Mediterranean France and Spain, Geophys. Res. Lett., 31, https://doi.org/10.1029/2003GL017715, 2004.
Vey, S., Dietrich, R., Fritsche, M., Ruelke, A., Steigenberger, P., and Rothacher, M.: On the homogeneity and interpretation of precipitable water time series derived from global GPS observations, J. Geophys. Res., 114, D10101, https://doi.org/10.1029/2008JD010415, 2009.
Vey, S., Dietrich, R., Rlke, A., Fritsche, M., Steigenberger, P., and Rothacher, M.: Validation of precipitable water vapor within the NCEP/DOE reanalysis using global GPS observations from one decade, J. Climate, 23, 1675–1695, https://doi.org/10.1175/2009JCLI2787.1, 2010.
Walpersdorf, A., Calais, E., Haase, J., Eymard, L., Desbois, M., and Vedel, H.: Atmospheric gradients estimated by GPS compared to a high resolution numerical weather prediction (NWP) model, Phys. Chem. Earth Pt. A, 26, 147–152, https://doi.org/10.1016/S1464-1895(01)00038-2, 2001.
Wang, J. and Zhang, L.: Systematic errors in global radiosonde precipitable water data from comparisons with ground-based GPS measurements, J. Climate, 21, 2218–2238, https://doi.org/10.1175/2007JCLI1944.1, 2008.
Wang, J., Zhang, L., Dai, A., van Hove, T., and van Baelen, J.: A near-global, 2-hourly data set of atmospheric precipitable water from ground-based GPS measurements, J. Geophys. Res., 112, D11107, https://doi.org/10.1029/2006JD007529, 2007.
Wang, J., Zhang, L., Dai, A., Immler, F., Sommer, M., and Voemel, H.: Radiation dry bias correction of Vaisala RS92 humidity data and its impacts on historical Radiosonde data, J. Atmos. Ocean. Tech., 30, 197–214, https://doi.org/10.1175/JTECH-D-12-00113.1, 2013.
Ware, R. H., Fulker, D. W., Stein, S. A., Anderson, D. N., Avery, S. K., Droegemeier, R. D. C. K. K., Kuettner, J. P., Minster, J., and Sorooshian, S.: Real-time national GPS networks: Opportunities for atmospheric sensing, Earth Planets Space, 52, 901–905, https://doi.org/10.1186/BF03352303, 2000.
Wentz, F. J. and Schabel, M.: Precise climate monitoring using complementary satellite data sets, Nature, 403, 414–416, https://doi.org/10.1038/35000184, 2000.
Yan, X., Ducrocq, V., Jaubert, G., Brousseau, P., Poli, P., Champollion, C., Flamant, C., and Boniface, K.: The benefit of GPS zenith delay assimilation to high-resolution quantitative precipitation forecasts: a case-study from COPS IOP 9, Q. J. Roy. Meteol. Soc., 135, 1788–1800, https://doi.org/10.1002/qj.508, 2009a.
Yan, X., Ducrocq, V., Poli, P., Hakam, M., Jaubert, G., and Walpersdorf, A.: Impact of GPS zenith delay assimilation on convective-scale prediction of Mediterranean heavy rainfall, J. Geophys. Res., 114, D03104, https://doi.org/10.1029/2008JD011036, 2009b.
Yang, H., Sass, B. H., Elgered, G., Johansson, J. M., and Emardson, T. R.: A comparison of precipitable water vapor estimates by an NWP simulation and GPS observations, J. App. Meteorol., 38, 941–956, https://doi.org/10.1175/1520-0450(1999)038<0941:ACOPWV>2.0.CO;2, 1999.
Yuan, Y., Zhang, K., Rohm, W., Choy, S., Norman, R., and Wang, C. S.: Real-time retrieval of precipitable water vapor from GPS precise point positioning, J. Geophys. Res., 119, 10044–10057, https://doi.org/10.1002/2014JD021486, 2014.
Zumberge, J. F., Heflin, M. B., Jefferson, D. C., Watkins, M. M., and Webb, F. H.: Precise Point Positioning for the efficient and robust analysis of GPS data from large networks, J. Geophys. Res., 102, 5005–5017, https://doi.org/10.1029/96JB03860, 1997.
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, https://doi.org/10.1127/0941-2948/2011/0232, 2011.
Zus, F., Dick, G., Heise, S., and Wickert, J.: A forward operator and its adjoint for GPS slant total delays, Radio Sci., 50, 393–405, https://doi.org/10.1002/2014RS005584, 2015.
Short summary
Application of global navigation satellite systems (GNSSs) for atmospheric remote sensing (GNSS meteorology) is a well-established field in both research and operation in Europe. This review covers the state of the art in GNSS meteorology in Europe. It discusses 1) advances in GNSS processing techniques and tropospheric products, 2) use in numerical weather prediction and nowcasting, and 3) climate research.
Application of global navigation satellite systems (GNSSs) for atmospheric remote sensing (GNSS...