Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4303-2024
© Author(s) 2024. 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-17-4303-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland
Grzegorz Kłopotek
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland
Laura Crocetti
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland
Rudi Weinacker
International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Tobias Sturn
International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Linda See
International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Galina Dick
GFZ German Research Centre for Geosciences, Potsdam, Germany
Gregor Möller
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Markus Rothacher
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland
Ian McCallum
International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Vicente Navarro
European Space Agency, European Space Astronomy Center, Madrid, Spain
Benedikt Soja
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland
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Myroslava Lesiv, Steffen Fritz, Martina Duerauer, Ivelina Georgieva, Marcel Buchhorn, Luc Bertels, Nandika Tsendbazar, Ruben Van De Kerchove, Daniele Zanaga, Dmitry Schepaschenko, Linda See, Martin Herold, Bruno Smets, Michael Cherlet, and Ian Mccallum
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-468, https://doi.org/10.5194/essd-2025-468, 2025
Preprint under review for ESSD
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This paper presents a unique global reference data set for land cover mapping at a 10 m resolution, aligned with Sentinel-2 imagery for the year 2015. It contains more than 16.5 million data records at a 10 m resolution (or 165 K data records at 100 m) and information on 12 different land cover classes. The data set was collected by a group of experts through visual interpretation of very high resolution imagery, along with other sources of information provided in the Geo-Wiki platform.
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.
Nicolas Lampach, Jon Olav Skøien, Helena Ramos, Julien Gaffuri, Renate Koeble, Linda See, and Marijn van der Velde
Earth Syst. Sci. Data, 17, 3893–3919, https://doi.org/10.5194/essd-17-3893-2025, https://doi.org/10.5194/essd-17-3893-2025, 2025
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Eurostat and the Joint Research Centre developed a new methodology to make geospatial data from agricultural census available to users while ensuring that no confidential information from individuals is disclosed. The geospatial data presented in the article correspond to the contextual indicators of the monitoring framework of the Common Agricultural Policy. Our exploratory analysis reveals several interesting patterns which contribute to the broader debate on the future of European agriculture.
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Juliana Chew, and Benedikt Soja
EGUsphere, https://doi.org/10.5194/egusphere-2025-1516, https://doi.org/10.5194/egusphere-2025-1516, 2025
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This work investigates the capability of machine learning models, trained with space and ground GNSS observations, to produce fields of refractivity and IWV that describe the spatiotemporal morphology of atmospheric rivers (ARs) and quantify moisture associated with them to a degree sufficient for atmospheric studies. The reconstructed fields can be used to monitor ARs. It studies what LEO radio occultation (RO) constellation is appropriate to quantify the structure, location and timing of ARs.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
EGUsphere, https://doi.org/10.5194/egusphere-2025-19, https://doi.org/10.5194/egusphere-2025-19, 2025
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Tropospheric gradients provide information on the moisture distribution, whereas ZTDs provide the absolute amount of moisture through integrated water vapor. When TGs are assimilated with ZTDs, it helps the model actuate the moisture fields, correcting its dynamics. In our research, we show evidence that in particular regions with very few GNSS stations, the assimilation of gradients on top of ZTDs can provide the same impact as the assimilation of only ZTDs with dense coverage of GNSS stations.
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher
Atmos. Meas. Tech., 18, 57–72, https://doi.org/10.5194/amt-18-57-2025, https://doi.org/10.5194/amt-18-57-2025, 2025
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This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.
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.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
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In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Matthias Aichinger-Rosenberger, Elmar Brockmann, Laura Crocetti, Benedikt Soja, and Gregor Moeller
Atmos. Meas. Tech., 15, 5821–5839, https://doi.org/10.5194/amt-15-5821-2022, https://doi.org/10.5194/amt-15-5821-2022, 2022
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This study develops an innovative approach for the detection and prediction of foehn winds. The approach uses products generated from GNSS (Global Navigation Satellite Systems) in combination with machine learning-based classification algorithms to detect and predict foehn winds at Altdorf, Switzerland. Results are encouraging and comparable to similar studies using meteorological data, which might qualify the method as an additional tool for short-term foehn forecasting in the future.
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
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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.
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).
Roberto Villalobos-Herrera, Emanuele Bevacqua, Andreia F. S. Ribeiro, Graeme Auld, Laura Crocetti, Bilyana Mircheva, Minh Ha, Jakob Zscheischler, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 21, 1867–1885, https://doi.org/10.5194/nhess-21-1867-2021, https://doi.org/10.5194/nhess-21-1867-2021, 2021
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Climate hazards may be caused by events which have multiple drivers. Here we present a method to break down climate model biases in hazard indicators down to the bias caused by each driving variable. Using simplified fire and heat stress indicators driven by temperature and relative humidity as examples, we show how multivariate indicators may have complex biases and that the relationship between driving variables is a source of bias that must be considered in climate model bias corrections.
Michele Ferri, Uta Wehn, Linda See, Martina Monego, and Steffen Fritz
Hydrol. Earth Syst. Sci., 24, 5781–5798, https://doi.org/10.5194/hess-24-5781-2020, https://doi.org/10.5194/hess-24-5781-2020, 2020
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As part of the flood risk management strategy of the
Brenta-Bacchiglione catchment (Italy), a citizen observatory for flood risk management is currently being implemented. A cost–benefit analysis of the citizen observatory was undertaken to demonstrate the value of this approach in monetary terms. Results show a reduction in avoided damage of 45 % compared to a scenario without implementation of the citizen observatory. The idea is to promote this methodology for future flood risk management.
Miao Lu, Wenbin Wu, Liangzhi You, Linda See, Steffen Fritz, Qiangyi Yu, Yanbing Wei, Di Chen, Peng Yang, and Bing Xue
Earth Syst. Sci. Data, 12, 1913–1928, https://doi.org/10.5194/essd-12-1913-2020, https://doi.org/10.5194/essd-12-1913-2020, 2020
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Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.
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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.
Crowdsourced smartphone GNSS data were processed with a dedicated data processing pipeline and...