Results 61 to 70 of about 494 (132)

Ensemble Based Estimation of Wet Refractivity Indices Using a Functional Model Approach

Earth and Space Science
The estimation of the wet refractivity indices is crucial for applications like weather predictions or improving the accuracy of real‐time positioning techniques.
Masoud Dehvari, Saeed Farzaneh, Ehsan Forootan   +2 more
doaj   +1 more source

Monitoring Zenithal Total Delays over the three different climatic zones from IGS GPS final products: A comparison between the use of the VMF1 and GMF mapping functions

Geodesy and Geodynamics, 2019
The International GNSS Service (IGS) final products (ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring.
Benachour Labib, Jianguo Yan, Jean-Pierre Barriot, Fangzhao Zhang, Peng Feng   +4 more
doaj   +1 more source

Deep Neural Networks for Refining Vertical Modeling of Global Tropospheric Delay

Geophysical Research Letters
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 ...
Peng Yuan, Kyriakos Balidakis, Jungang Wang, Pengfei Xia, Jian Wang, Mingyuan Zhang, Weiping Jiang, Harald Schuh, Jens Wickert, Zhiguo Deng   +9 more
doaj   +1 more source

Measurement report: Can zenith wet delay from GNSS “see” atmospheric turbulence? Insights from case studies across diverse climate zones [PDF]

Atmospheric Chemistry and Physics
Global navigation satellite system (GNSS) microwave signals are nearly unaffected by clouds but are delayed as they travel the troposphere. The hydrostatic delay accounts for approximately 90 % of the total delay and can be modelled well as a function of
G. Kermarrec, X. Calbet, Z. Deng, C. Carbajal Henken   +3 more
doaj   +1 more source

Three‐Year Characterization of Boundary Layer Dynamics From GNSS Zenith Wet Delay Spectral Analysis

Atmospheric Science Letters
Continuous monitoring of boundary layer turbulence remains a challenge due to sparse conventional instrumentation. Here we suggest that spectral analysis of Global Navigation Satellite System (GNSS) zenith wet delay (ZWD) fluctuations yields physically ...
Gaël Kermarrec, Xavier Calbet, Zhiguo Deng   +2 more
doaj   +1 more source

A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered

Atmosphere
The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval.
Yin Wu, Lu Huang, Wei Feng, Su Tian
doaj   +1 more source

Troposphere delay modeling in SLR based on PMF, VMF3o, and meteorological data

Progress in Earth and Planetary Science
Satellite laser ranging (SLR) requires accurate troposphere delay models to properly correct the observed distances to satellites and derive fundamental geodetic and geodynamic parameters.
Mateusz Drożdżewski, Krzysztof Sośnica   +1 more
doaj   +1 more source

88th Annual Meeting of the Meteoritical Society 2026: Abstracts

Meteoritics &Planetary Science, Volume 61, Issue S1, Page S48-S551, August 2026.
wiley   +1 more source

Improving GNSS Meteorology by Fusing Measurements of Several Colocated Receivers on the Observation Level

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Zenith wet delay (ZWD) estimation is a key component for the global navigation satellite system (GNSS) meteorology. At present, the zenith hydrostatic delay can be computed with sufficient accuracy by means of empirical models, while the ZWD, which is ...
Rui Wang, Grzegorz Marut, Tomasz Hadas, Thomas Hobiger   +3 more
doaj   +1 more source

An Interferometric Synthetic Aperture Radar Tropospheric Delay Correction Method Based on a Global Navigation Satellite System and a Backpropagation Neural Network: More Suitable for Areas with Obvious Terrain Changes. [PDF]

Sensors (Basel), 2023
Qiu L, Chen P, Yao Y, Chen H, Tang F, Xiong M.   +5 more
europepmc   +1 more source