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dc.contributor.author
Shehaj, Endrit
dc.contributor.author
Miotti, Luca
dc.contributor.author
Geiger, Alain
dc.contributor.author
D'Aronco, Stefano
dc.contributor.author
Wegner, Jan Dirk
dc.contributor.author
Möller, Gregor
dc.contributor.author
Soja, Benedikt
dc.contributor.author
Rothacher, Markus
dc.date.accessioned
2023-03-17T08:05:09Z
dc.date.available
2023-01-05T14:07:08Z
dc.date.available
2023-01-05T14:19:02Z
dc.date.available
2023-03-17T08:05:09Z
dc.date.issued
2023-03
dc.identifier.issn
0094-5765
dc.identifier.other
10.1016/j.actaastro.2022.10.007
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/590523
dc.identifier.doi
10.3929/ethz-b-000590523
dc.description.abstract
Signals used for Earth observation, when travelling through the atmosphere, are sensitive to refractivity; especially high spatio-temporal variations of water vapor are difficult to model and correct. Remaining unmodeled tropospheric delays deteriorate the positioning solution and therefore limit the accuracy of sensing and navigation applications. These delays are usually computed with empirical models based on ground meteorological parameters (pressure, temperature and water vapor partial pressure). However, existing models are not accurate enough for high-precision applications such as GNSS, where in consequence the so-called zenith total delay (ZTD) has to be estimated together with other unknown parameters (coordinates etc.). For decades, the Institute of Geodesy and Photogrammetry at ETH Zurich has been studying collocation methods for the modeling of tropospheric delays using meteorological parameters, successfully interpolating pointwise or integral atmospheric observations. Meanwhile, machine learning has become a widely used and valuable alternative, when big datasets are available for the training process. Indeed, we have already successfully predicted ZTDs based on meteorological parameters with an accuracy of 1–2 cm for locations (GNSS stations) already used in the training phase. However, difficulties arise to predict delays at new locations. In this work, we take a step forward in investigating the combination of machine learning algorithms and physical models used in a collocation approach to derive atmospheric delay fields at a very high resolution. Thus, without processing any GNSS data we can predict tropospheric delay fields everywhere in the area of investigation. In this paper, we firstly describe the designed architecture of the neural network, secondly, the combination of least-squares collocation and artificial neural networks for high-resolution prediction of tropospheric delays. We benefit from the complementary characteristics of these algorithms. While machine learning is capable of successfully predicting the variation of time series for given points, empirical models based on collocation are well suited for describing spatial variations within the area of investigation. Finally, we report the achieved performance for the entire territory of Switzerland (1–2 cm in terms of RMS), showing that the synergic combination of these algorithms can overcome the individual drawbacks of each method and provide more accurate delay estimates than either method individually. Datasets of 11 years, covering the territory of Switzerland, consisting of GNSS ZTDs from 72 permanent AGNES/COGEAR (swisstopo, ETHZ) stations and meteorological data from MeteoSwiss were used for this research.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Pergamon
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
GNSS
en_US
dc.subject
Troposphere
en_US
dc.subject
Machine learning
en_US
dc.subject
Collocation
en_US
dc.subject
Zenith delay
en_US
dc.subject
Meteorological parameters
en_US
dc.title
High-resolution tropospheric refractivity fields by combining machine learning and collocation methods to correct earth observation data
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2022-10-11
ethz.journal.title
Acta Astronautica
ethz.journal.volume
204
en_US
ethz.journal.abbreviated
Acta astronaut.
ethz.pages.start
591
en_US
ethz.pages.end
598
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
High-Resolution Atmospheric Water Vapor Fields by Spaceborne Geodetic Sensing, Tomographic Fusion, and Atmospheric Modeling
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Oxford
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03824 - Rothacher, Markus (emeritus) / Rothacher, Markus (emeritus)
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::09707 - Soja, Benedikt / Soja, Benedikt
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03824 - Rothacher, Markus (emeritus) / Rothacher, Markus (emeritus)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::09707 - Soja, Benedikt / Soja, Benedikt
ethz.grant.agreementno
168952
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte MINT
ethz.relation.isNewVersionOf
20.500.11850/527240
ethz.date.deposited
2023-01-05T14:07:08Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-03-17T08:05:13Z
ethz.rosetta.lastUpdated
2024-02-02T21:07:21Z
ethz.rosetta.versionExported
true
ethz.COinS
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