Atmospheric bending effects in GNSS tomography

• Main Authors: G. Möller, D. Landskron
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-01-01
• Series: Atmospheric Measurement Techniques
• Online Access: https://www.atmos-meas-tech.net/12/23/2019/amt-12-23-2019.pdf
• ISSN: 1867-1381; 1867-8548

<p>In Global Navigation Satellite System (GNSS) tomography, precise information about the tropospheric water vapor distribution is derived from integral measurements like ground-based GNSS slant wet delays (SWDs). Therefore, the functional relation between observations and unknowns, i.e., the signal paths through the atmosphere, have to be accurately known for each station–satellite pair involved. For GNSS signals observed above a 15<span class="inline-formula">^∘</span> elevation angle, the signal path is well approximated by a straight line. However, since electromagnetic waves are prone to atmospheric bending effects, this assumption is not sufficient anymore for lower elevation angles. Thus, in the following, a mixed 2-D piecewise linear ray-tracing approach is introduced and possible error sources in the reconstruction of the bended signal paths are analyzed in more detail. Especially if low elevation observations are considered, unmodeled bending effects can introduce a systematic error of up to 10–20&thinsp;<span class="inline-formula">ppm</span>, on average 1–2&thinsp;<span class="inline-formula">ppm</span>, into the tomography solution. Thereby, not only the ray-tracing method but also the quality of the a priori field can have a significant impact on the reconstructed signal paths, if not reduced by iterative processing. In order to keep the processing time within acceptable limits, a bending model is applied for the upper part of the neutral atmosphere. It helps to reduce the number of processing steps by up to 85&thinsp;% without significant degradation in accuracy. Therefore, the developed mixed ray-tracing approach allows not only for the correct treatment of low elevation observations but is also fast and applicable for near-real-time applications.</p>