학술논문

On the information content in linear horizontal delay gradients estimated from space geodesy observations
Document Type
article
Source
Atmospheric Measurement Techniques, Vol 12, Pp 3805-3823 (2019)
Subject
Environmental engineering
TA170-171
Earthwork. Foundations
TA715-787
Language
English
ISSN
1867-1381
1867-8548
Abstract
We have studied linear horizontal gradients in the atmospheric propagation delay above ground-based stations receiving signals from the Global Positioning System (GPS). Gradients were estimated from 11 years of observations from five sites in Sweden. Comparing these gradients with the corresponding ones from the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses shows that GPS gradients detect effects over different timescales caused by the hydrostatic and the wet components. The two stations equipped with microwave-absorbing material below the antenna, in general, show higher correlation coefficients with the ECMWF gradients compared to the other three stations. We also estimated gradients using 4 years of GPS data from two co-located antenna installations at the Onsala Space Observatory. Correlation coefficients for the east and the north wet gradients, estimated with a temporal resolution of 15 min from GPS data, can reach up to 0.8 for specific months when compared to simultaneously estimated wet gradients from microwave radiometry. The best agreement is obtained when an elevation cut-off angle of 3∘ is applied in the GPS data processing, in spite of the fact that the radiometer does not observe below 20∘. We also note a strong seasonal dependence in the correlation coefficients, from 0.3 during months with smaller gradients to 0.8 during months with larger gradients, typically during the warmer and more humid part of the year. Finally, a case study using a 15 d long continuous very-long-baseline interferometry (VLBI) campaign was carried out. The comparison of the gradients estimated from VLBI and GPS data indicates that a homogeneous and frequent sampling of the sky is a critical parameter.