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Journal Article

Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data

Authors

Pan,  Yuanxin
External Organizations;

Kłopotek,  Grzegorz
External Organizations;

Crocetti,  Laura
External Organizations;

Weinacker,  Rudi
External Organizations;

Sturn,  Tobias
External Organizations;

See,  Linda
External Organizations;

/persons/resource/dick

Dick,  G.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Möller,  Gregor
External Organizations;

Rothacher,  Markus
External Organizations;

McCallum,  Ian
External Organizations;

Navarro,  Vicente
External Organizations;

Soja,  Benedikt
External Organizations;

External Ressource
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Fulltext (public)

5027740.pdf
(Publisher version), 4MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Pan, Y., Kłopotek, G., Crocetti, L., Weinacker, R., Sturn, T., See, L., Dick, G., Möller, G., Rothacher, M., McCallum, I., Navarro, V., Soja, B. (2024): Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data. - Atmospheric Measurement Techniques, 17, 14, 4303-4316.
https://doi.org/10.5194/amt-17-4303-2024


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5027740
Abstract
The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Given that nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately 20 000 raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free precise point positioning (PPP)-based zenith total delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of millimeter-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding satellite positioning service of the German National Survey (SAPOS) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study demonstrates high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations.