Abstract
Abstract. It is well-known that in the presence of super-refractive layers in the lower-tropospheric inversion of GNSS radio occultation (RO) measurements using the Abel transform yields biased refractivity profiles. As such it is problematic to reconstruct the true refractivity from the RO signal. Additional information about this lower region of the atmosphere might be embedded in reflected parts of the signal. To retrieve the bending angle, the phase matching operator can be used. This operator produces a complex function of the impact parameter, and from its phase we can calculate the bending angle. Instead of looking at the phase, in this paper we focus on the function's amplitude. The results in this paper show that the signatures of surface reflections in GNSS RO measurements can be significantly enhanced when using the phase matching method by processing only an appropriately selected segment of the received signal. This signature enhancement is demonstrated by simulations and confirmed with 10 hand-picked MetOp-A occultations with reflected components. To validate that these events show signs of reflections, radio holographic images are generated. Our results suggest that the phase matching amplitude carries information that can improve the interpretation of radio occultation measurements in the lower troposphere.
Highlights
GNSS radio occultation (RO) is a technique used for sounding the Earth’s atmosphere
Orbit defined for straight line tangent altitude (SLTA) >= -300 km Orbit defined for SLTA >= -65 km 10 we provide radio holographic images as a means of validation
We present 10 cases where phase matching (PM) is performed on real signals alongside their simulated counterparts based on ECMWF’s co-located refractivity profiles
Summary
GNSS radio occultation (RO) is a technique used for sounding the Earth’s atmosphere. Assuming spherical symmetry of the atmosphere, the bending angles of GNSS signals passing through the atmosphere can be found and assimilated into numerical weather prediction systems. A method to detect these reflected components was suggested by Hocke et al (1999) and has later been used on real data and shown to work (e.g., Beyerle et al, 2002; Pavelyev et al, 2002) This method uses a radio hologram generated by subtracting a ray-traced reference field from the received signal. An effort to flag occultation events where reflections are present is described by Cardellach and Oliveras (2016), based on a supervised learning approach classifying such radio holographic images. We demonstrate signatures corresponding to surface reflections and truncate the received signal to distinguish them from the much stronger signatures of the direct components. We justify this truncation using simulated data and a simple geometric model for which reflected components are expected to appear in the signal, and
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