Preprints
https://doi.org/10.5194/egusphere-2024-2716
https://doi.org/10.5194/egusphere-2024-2716
02 Oct 2024
 | 02 Oct 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Atmospheric horizontal gradients measured with eight co-located GNSS stations and a microwave radiometer

Ning Tong and Gunnar Elgered

Abstract. We have used eight co-located GNSS stations, with different antenna mounts, to estimate atmospheric signal propagation delays in the zenith direction
and linear horizontal gradients. The gradients are compared with the results from a water vapour radiometer (WVR).  Water drops in the atmosphere has a negative influence on the retrieval accuracy of the WVR. Hence we see a better agreement using WVR data with a liquid water content (LWC) less than 0.05 mm compared to when LWC values of up to 0.7 mm are included. We have used two different constraints when estimating the linear gradients from the GNSS data. Using a weak constraint enhances the GNSS estimates to track large gradients of short duration at the cost of increased formal errors. To mitigate random noise in the GNSS data, we adopted a fusion approach averaging estimates from the GNSS stations. This resulted in significant improvements for the agreement with WVR data, a maximum of 17 % increase in the correlation and an 14 % reduction in the root-mean-square (rms) difference for the east gradients. The corresponding values for the north gradients are both 25 %. Overall, no large differences in terms of quality is  observed for the eight GNSS stations. However, one station shows slightly poorer agreement for  the north gradients compared to the others.  This is attributed to the station's proximity to a radio telescope, which causes data loss of observations at low elevation angles in the south-south-west direction. 

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Ning Tong and Gunnar Elgered

Status: open (extended)

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Ning Tong and Gunnar Elgered
Ning Tong and Gunnar Elgered

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Short summary
The gradients estimated from multi-GNSS data were compared to WVR data. A better agreement is obtained when using WVR data with a smaller value of liquid water content. A weak constraint in GNSS data processing may be desirable for applications with a higher tolerance for formal errors but a greater focus on tracking the variability of water vapour. The averaged GNSS gradients can significantly reduce the random noise which can be used to obtain a more reliable variability of the water vapour.