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Journal Article

Deep Neural Networks for Refining Vertical Modeling of Global Tropospheric Delay

Authors
/persons/resource/pyuan

Yuan,  Peng       
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Submitting Corresponding Author, Deutsches GeoForschungsZentrum;

/persons/resource/balidak

Balidakis,  K.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/jgwang

Wang,  Jungang
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Xia,  Pengfei
External Organizations;

Wang,  Jian
External Organizations;

Zhang,  Mingyuan
External Organizations;

Jiang,  Weiping
External Organizations;

/persons/resource/schuh

Schuh,  H.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/wickert

Wickert,  J.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/deng

Deng,  Z.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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5030116.pdf
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Citation

Yuan, P., Balidakis, K., Wang, J., Xia, P., Wang, J., Zhang, M., Jiang, W., Schuh, H., Wickert, J., Deng, Z. (2025): Deep Neural Networks for Refining Vertical Modeling of Global Tropospheric Delay. - Geophysical Research Letters, 52, 2, e2024GL111404.
https://doi.org/10.1029/2024gl111404


Cite as: https://gfzpublic.gfz.de/pubman/item/item_5030116
Abstract
Kinematic airborne platforms are becoming increasingly vital for Earth observation. They highlight the critical need for accurate tropospheric delay corrections across varying altitudes, especially as most existing models are limited to Earth's surface. Although analytical functions have been used to model vertical reductions in tropospheric delays, they struggle to capture the intricate vertical variations of atmospheric state. In response, we introduce a novel approach that utilizes deep neural networks (DNN) to reconstruct global three-dimensional zenith hydrostatic delay (ZHD) and zenith wet delays (ZWD) derived from numerical weather models (NWM). Our method reconstructs NWM-derived ZHD and ZWD globally up to 14 km above the Earth's surface, with average precision levels of 0.4 and 0.8 mm, respectively. Compared to the analytical third-order exponential model, the DNN approach demonstrates substantial improvement with global average root-mean-square reductions of 63% for ZHD and 36% for ZWD.