English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Global, spatially explicit modelling of zenith wet delay with XGBoost

Authors

Crocetti,  Laura
External Organizations;

Schartner,  Matthias
External Organizations;

/persons/resource/zusflo

Zus,  Florian
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Zhang,  Wenyuan
External Organizations;

Moeller,  Gregor
External Organizations;

Navarro,  Vicente
External Organizations;

See,  Linda
External Organizations;

Schindler,  Konrad
External Organizations;

Soja,  Benedikt
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)

5026314.pdf
(Publisher version), 4MB

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

Crocetti, L., Schartner, M., Zus, F., Zhang, W., Moeller, G., Navarro, V., See, L., Schindler, K., Soja, B. (2024): Global, spatially explicit modelling of zenith wet delay with XGBoost. - Journal of Geodesy, 98, 23.
https://doi.org/10.1007/s00190-024-01829-2


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026314
Abstract
Radio signals transmitted by Global Navigation Satellite System (GNSS) satellites experience tropospheric delays. While the hydrostatic part, referred to as zenith hydrostatic delay (ZHD) when mapped to the zenith direction, can be analytically modelled with sufficient accuracy, the wet part, referred to as zenith wet delay (ZWD), is much more difficult to determine and needs to be estimated. Thus, there exist several ZWD models which are used for various applications such as positioning and climate research. In this study, we present a data-driven, global model of the spatial ZWD field, based on the Extreme Gradient Boosting (XGBoost). The model takes the geographical location, the time, and a number of meteorological variables (in particular, specific humidity at several pressure levels) as input, and can predict ZWD anywhere on Earth as long as the input features are available. It was trained on ZWDs at 10718 GNSS stations and tested on ZWDs at 2684 GNSS stations for the year 2019. Across all test stations and all observations, the trained model achieved a mean absolute error of 6.1 mm, respectively, a root mean squared error of 8.1 mm. Comparisons of the XGBoost-based ZWD predictions with independently computed ZWDs and baseline models underline the good performance of the proposed model. Moreover, we analysed regional and monthly models, as well as the seasonal behaviour of the ZWD predictions in different climate zones, and found that the global model exhibits a high predictive skill in all regions and across all months of the year.