the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jan 2024
Determination of High-Precision Tropospheric Delays Using Crowdsourced Smartphone GNSS Data
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. Since 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 twenty thousand 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 mm-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 SAPOS (Satellite Positioning Service of the German State Survey) 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 marks the first successful demonstration of high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-66', Anonymous Referee #1, 19 Apr 2024
GENERAL COMMENTS
This is a very interesting manuscript that deserves publication on the potential of using ZTDs derived from raw GNSS data obtained from private citizens smartphones. Supplemented with an example of ZTD data from a smartphone providing GNSS data in an idealized setting and compared to ZTD derived from a professional grade GNSS receiver.
I congratulate the authors of having written a nice, easy to read manuscript almost ready to publish.
Compared to previous tests of usage of other crowsourced smartphone data, such as for example pressure (see e.g. Hintz et al,
https://doi.org/10.1002/met.1805) it is clear that for derivation of ZTD from raw GNSS smartphone data much longer un-interrupted timeseries are required for the mobile phone data to be useful. On top there is a benefit from obtaining the GNSS data, when the phone in an open environment.It would be interesting to include in the article information about:
1 The local time of day distribution of the full data volume versus the volume kept for analysis after filtering.
2 Were the volunteers providing the data given any information about how to use (e.g., how long in the proper mode) or place (indoor/outdoor/sky view) or not move their devices prior to the experiment?
3 Many Android phones contain also a pressure sensor, data from that could be collected simultanously, the two types of data potentially
improving usage when used together (just your thoughts on this).
SPECIFIC COMMENTSThe smartphone based curves in figure 11 appear to me surprisingly smooth. Is that due to constraints in the data processing of the raw GNSS data or subsequent smoothing of the ZTDs?
Citation: https://doi.org/10.5194/egusphere-2024-66-RC1 -
AC1: 'Reply on RC1', Yuanxin Pan, 18 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-66/egusphere-2024-66-AC1-supplement.pdf
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AC1: 'Reply on RC1', Yuanxin Pan, 18 May 2024
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RC2: 'Comment on egusphere-2024-66', Anonymous Referee #2, 03 May 2024
Tropospheric delay information is valuable to both spatial geodetic measuring and weather forecasting. Ground-based GNSS has advantages of high-accuracy, high temporal resolution and can be operated under all weather conditions. However, the number of traditional geodetic GNSS receivers is limited due to the cost. The use of crowdsourced smartphone GNSS data is promising to provide much denser tropospheric delay information due to the massive smartphone users. This paper carried out novel investigations on the ZTD estimation based on crowdsourced data collected from the CAMALIOT campaign. The paper is generally well organized and the experiments were well designed. But, in my opinion, there are still many work to do before you can say ‘this is the first successful demonstration of high-precision ZTD determination with crowdsourced smartphone GNSS data’. The main limitation of this study is that the selected data for the ZTD estimation are all from static users in an open-sky environment. Can these data be called real crowdsourced data? As we know, most smartphone users are indoor or kinematic. So the results from open-sky static users are just too ideal. The data from the 10 crowdsourced users in this study are similar to the data collected by repeating 10 times of experiments at ETH in sect 3.1.
Other minor comments include,
- L56: why the accuracy of Benvenuto et al. (2021) is so bad with errors of several cm compared to several mm in other studies? Please make necessary comments here.
- L132: the PCO corrections were not applied either, right?
- L138: why the C/N0-dependent weighting tends to introduce artifacts in ZTD estimates in your results? Several studies have showed that the traditional elevation-dependent weighting strategy may not applicable for smartphone data. Why you still use this strategy?
- L231: why did you attribute the 6 mm bias to the uncorrected PCV errors? Can it also be possible due to the worse data quality in smartphone observations? You didn’t apply PCV corrections for UBLX either, but the bias was much smaller.
- Figure 10: you need to give the number of ZTD samples in each case.
Citation: https://doi.org/10.5194/egusphere-2024-66-RC2 -
AC2: 'Reply on RC2', Yuanxin Pan, 18 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-66/egusphere-2024-66-AC2-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-66', Anonymous Referee #1, 19 Apr 2024
GENERAL COMMENTS
This is a very interesting manuscript that deserves publication on the potential of using ZTDs derived from raw GNSS data obtained from private citizens smartphones. Supplemented with an example of ZTD data from a smartphone providing GNSS data in an idealized setting and compared to ZTD derived from a professional grade GNSS receiver.
I congratulate the authors of having written a nice, easy to read manuscript almost ready to publish.
Compared to previous tests of usage of other crowsourced smartphone data, such as for example pressure (see e.g. Hintz et al,
https://doi.org/10.1002/met.1805) it is clear that for derivation of ZTD from raw GNSS smartphone data much longer un-interrupted timeseries are required for the mobile phone data to be useful. On top there is a benefit from obtaining the GNSS data, when the phone in an open environment.It would be interesting to include in the article information about:
1 The local time of day distribution of the full data volume versus the volume kept for analysis after filtering.
2 Were the volunteers providing the data given any information about how to use (e.g., how long in the proper mode) or place (indoor/outdoor/sky view) or not move their devices prior to the experiment?
3 Many Android phones contain also a pressure sensor, data from that could be collected simultanously, the two types of data potentially
improving usage when used together (just your thoughts on this).
SPECIFIC COMMENTSThe smartphone based curves in figure 11 appear to me surprisingly smooth. Is that due to constraints in the data processing of the raw GNSS data or subsequent smoothing of the ZTDs?
Citation: https://doi.org/10.5194/egusphere-2024-66-RC1 -
AC1: 'Reply on RC1', Yuanxin Pan, 18 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-66/egusphere-2024-66-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Yuanxin Pan, 18 May 2024
-
RC2: 'Comment on egusphere-2024-66', Anonymous Referee #2, 03 May 2024
Tropospheric delay information is valuable to both spatial geodetic measuring and weather forecasting. Ground-based GNSS has advantages of high-accuracy, high temporal resolution and can be operated under all weather conditions. However, the number of traditional geodetic GNSS receivers is limited due to the cost. The use of crowdsourced smartphone GNSS data is promising to provide much denser tropospheric delay information due to the massive smartphone users. This paper carried out novel investigations on the ZTD estimation based on crowdsourced data collected from the CAMALIOT campaign. The paper is generally well organized and the experiments were well designed. But, in my opinion, there are still many work to do before you can say ‘this is the first successful demonstration of high-precision ZTD determination with crowdsourced smartphone GNSS data’. The main limitation of this study is that the selected data for the ZTD estimation are all from static users in an open-sky environment. Can these data be called real crowdsourced data? As we know, most smartphone users are indoor or kinematic. So the results from open-sky static users are just too ideal. The data from the 10 crowdsourced users in this study are similar to the data collected by repeating 10 times of experiments at ETH in sect 3.1.
Other minor comments include,
- L56: why the accuracy of Benvenuto et al. (2021) is so bad with errors of several cm compared to several mm in other studies? Please make necessary comments here.
- L132: the PCO corrections were not applied either, right?
- L138: why the C/N0-dependent weighting tends to introduce artifacts in ZTD estimates in your results? Several studies have showed that the traditional elevation-dependent weighting strategy may not applicable for smartphone data. Why you still use this strategy?
- L231: why did you attribute the 6 mm bias to the uncorrected PCV errors? Can it also be possible due to the worse data quality in smartphone observations? You didn’t apply PCV corrections for UBLX either, but the bias was much smaller.
- Figure 10: you need to give the number of ZTD samples in each case.
Citation: https://doi.org/10.5194/egusphere-2024-66-RC2 -
AC2: 'Reply on RC2', Yuanxin Pan, 18 May 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-66/egusphere-2024-66-AC2-supplement.pdf
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Cited
1 citations as recorded by crossref.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3720 KB) - Metadata XML