<div class="csl-bib-body">
<div class="csl-entry">Li, Q., Böhm, J., Yuan, L., & Weber, R. (2023). Modeling of the weighted mean temperature based on the random forest machine learning approach. In <i>EGU General Assembly 2023</i>. EGU General Assembly 2023, Vienna, Austria. EGU. https://doi.org/10.5194/egusphere-egu23-17204</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192161
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dc.description.abstract
Atmospheric weighted mean temperature, Tm, is an important parameter in the Earth’s atmospheric water vapor sounding with the Global Navigation Satellite System (GNSS) technique. In this study, considering spatial distribution, time-varying characteristics, and the correlation with surface meteorological variables, Tm modeling is realized based on the random forest (RF) machine learning and global atmospheric profiles from radiosonde (RS) data and GPS radio occultations (RO) measurements. Comparisons of modeled results and numerical integrations of atmospheric profiles in 2020 show that the RF-based Tm model with surface meteorological parameters generally obtains a good accuracy with overall RMS errors of 2.8 K in comparison with RS data and 2.6 K in contrast to GPS RO data.
en
dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
machine learning
en
dc.subject
weighted mean temperature
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dc.subject
GNSS
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dc.subject
GPS radio occultations
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dc.title
Modeling of the weighted mean temperature based on the random forest machine learning approach
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.contributor.affiliation
Southwest Jiaotong University, Chengdu, P.R. China
