High temporal resolution wet delay gradients estimated from multi-GNSS and microwave radiometer observations
Journal article, 2021

We have used 1 year of multi-GNSS observations at the Onsala Space Observatory on the Swedish west coast to estimate the linear horizontal gradients in the wet propagation delay. The estimated gradients are compared to the corresponding ones from a microwave radiometer. We have investigated different temporal resolutions from 5 min to 1 d. Relative to the GPS-only solution and using an elevation cutoff angle of 10 and a temporal resolution of 5 min, the improvement obtained for the solution using GPS, Glonass, and Galileo data is an increase in the correlation coefficient of 11 % for the east gradient and 20 % for the north gradient. Out of all the different GNSS solutions, the highest correlation is obtained for the east gradients and a resolution of 2 h, while the best agreement for the north gradients is obtained for 6 h. The choice of temporal resolution is a compromise between getting a high correlation and the possibility of detecting rapid changes in the gradient. Due to the differences in geometry of the observations, gradients which happen suddenly are either not captured at all or captured but with much less amplitude by the GNSS data. When a weak constraint is applied in the estimation of process, the GNSS data have an improved ability to track large gradients, however, at the cost of increased formal errors.

Author

Tong Ning

The Swedish Mapping, Cadastral and Land Registration Authority

Gunnar Elgered

Chalmers, Space, Earth and Environment, Onsala Space Observatory

Atmospheric Measurement Techniques

1867-1381 (ISSN) 1867-8548 (eISSN)

Vol. 14 8 5593-5605

Driving Forces

Sustainable development

Subject Categories

Remote Sensing

Meteorology and Atmospheric Sciences

Roots

Basic sciences

Infrastructure

Onsala Space Observatory

DOI

10.5194/amt-14-5593-2021

Related datasets

High temporal resolution wet delay gradients estimated from multi-GNSS and microwave radiometer observations [dataset]

DOI: 10.5878/fyt8-bs80 ID: 2021-219

More information

Latest update

12/5/2022