.png){kind=icon}
Return
Simultaneous troposphere estimation with precise point positioning
.png){kind=icon}
OA
AI
DOI：10.1007/s00190-026-02088-z.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Theoretically, it is possible to reconstruct the 3D distribution of water vapor by means of GNSS tomography using troposphere estimates from a network of GNSS stations, i.e., zenith delays mapped back into satellite directions. However, this technique is still limited by restricted satellite-to-ground observation geometry, a simplified parameterization of troposphere delays in the observation model and mandatory usage of constraints to stabilize the equation system. We propose an alternative approach, called STEPPP, in which a network of ground-based GNSS receivers is used to process GNSS observations, based on the Precise Point Positioning (PPP) technique and, instead of estimating the zenith wet delay and horizontal gradients, the estimation of the whole wet refractivity field in a grid space is performed simultaneously. Contrary to GNSS tomography, STEPPP operates on raw observation data instead of products. Values of wet refractivity at grid nodes are estimated from all stations simultaneously, i.e., they appear as common parameters in PPP. We present the functional and stochastic model of STEPPP, as well as first results of the model performance. We use GPS and Galileo dual-frequency observations generated by the Spirent simulator for 20 evenly distributed stations. The simulated observations are intentionally free of troposphere delays. However, we use a numerical weather model to retrieve reference profiles of wet refractivity and calculate slant wet delays, which are added to the simulated observations. We define a voxel space above the network of stations up to 12 km height and we recover the wet refractivity profiles by means of the STEPPP model. Several numerical experiments are performed using homogenous, inhomogeneous, constant and dynamic wet refractivity profiles. After a convergence time of a few hours, the STEPPP model accurately recovers all model states, including the 3D wet refractivity field and station coordinates.
Keywords:
Troposphere
Refractivity
Precise point positioning
GNSS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
2.5K
Citations:
7.6K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveOptimizing Boundary Conditions in GNSS Tomography: A Continuous 7‐Month Case Study in the Amazon
OAAI
OAAIShareSave
View more