Deep Neural Networks for Refining Vertical Modeling of Global Tropospheric Delay

verfasst von: Peng Yuan, Kyriakos Balidakis, Jungang Wang, Pengfei Xia, Jian Wang, Mingyuan Zhang, Weiping Jiang, Harald Schuh, Jens Wickert, Zhiguo Deng
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.
Externe Organisation(en): Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ)
Technische Universität Berlin
Wuhan University
Typ: Artikel
Journal: Geophysical research letters
Band: 52
ISSN: 0094-8276
Publikationsdatum: 25.01.2025
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Geophysik, Allgemeine Erdkunde und Planetologie
Elektronische Version(en): https://doi.org/10.1029/2024GL111404 (Zugang: Offen)

BibTeX

@article{91b3a9ac9adc40aa9950f40da996d200,
title = "Deep Neural Networks for Refining Vertical Modeling of Global Tropospheric Delay",
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.",
keywords = "deep neural networks, ERA5, GNSS, GNSS meteorology, tropospheric delay, vertical correction model",
author = "Peng Yuan and Kyriakos Balidakis and Jungang Wang and Pengfei Xia and Jian Wang and Mingyuan Zhang and Weiping Jiang and Harald Schuh and Jens Wickert and Zhiguo Deng",
note = "Publisher Copyright: {\textcopyright} 2025. The Author(s).",
year = "2025",
month = jan,
day = "25",
doi = "10.1029/2024GL111404",
language = "English",
volume = "52",
journal = "Geophysical research letters",
issn = "0094-8276",
publisher = "Wiley-Blackwell",
number = "2",
}