Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | e2024GL111404 |
| Fachzeitschrift | Geophysical research letters |
| Jahrgang | 52 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 25 Jan. 2025 |
| Extern publiziert | Ja |
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.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Geophysik
- Erdkunde und Planetologie (insg.)
- Allgemeine Erdkunde und Planetologie
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Geophysical research letters, Jahrgang 52, Nr. 2, e2024GL111404, 25.01.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Deep Neural Networks for Refining Vertical Modeling of Global Tropospheric Delay
AU - Yuan, Peng
AU - Balidakis, Kyriakos
AU - Wang, Jungang
AU - Xia, Pengfei
AU - Wang, Jian
AU - Zhang, Mingyuan
AU - Jiang, Weiping
AU - Schuh, Harald
AU - Wickert, Jens
AU - Deng, Zhiguo
N1 - Publisher Copyright: © 2025. The Author(s).
PY - 2025/1/25
Y1 - 2025/1/25
N2 - 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.
AB - 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.
KW - deep neural networks
KW - ERA5
KW - GNSS
KW - GNSS meteorology
KW - tropospheric delay
KW - vertical correction model
UR - http://www.scopus.com/inward/record.url?scp=85216215167&partnerID=8YFLogxK
U2 - 10.1029/2024GL111404
DO - 10.1029/2024GL111404
M3 - Article
AN - SCOPUS:85216215167
VL - 52
JO - Geophysical research letters
JF - Geophysical research letters
SN - 0094-8276
IS - 2
M1 - e2024GL111404
ER -