Preprints
https://doi.org/10.5194/amt-2021-287
https://doi.org/10.5194/amt-2021-287

  21 Sep 2021

21 Sep 2021

Review status: this preprint is currently under review for the journal AMT.

Hierarchical Deconvolution for Incoherent Scatter Radar Data

Snizhana Ross1, Arttu Arjas1, Ilkka I. Virtanen2, Mikko J. Sillanpää1, Lassi Roininen3, and Andreas Hauptmann4 Snizhana Ross et al.
  • 1Research Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, Finland
  • 2Research Unit of Space Physics and Astronomy, University of Oulu, FI-90014 Oulu, Finland
  • 3School of Engineering Science, Lappeenranta-Lahti University of Technology, FI-53851 Lappeenranta, Finland
  • 4Department of Computer Science, University College London, London WC1E 6BT, UK

Abstract. We propose a novel method for deconvolving incoherent scatter radar data to recover accurate reconstructions of backscattered powers. The problem is modelled as a hierarchical noise-perturbed deconvolution problem, where the lower hierarchy consists of an adaptive length-scale function that allows for a non-stationary prior and as such enables adaptive recovery of smooth and narrow layers in the profiles. The estimation is done in a Bayesian statistical inversion framework as a two-step procedure, where hyperparameters are first estimated by optimisation and followed by an analytical closed-form solution of the deconvolved signal. The proposed optimisation based method is compared to a fully probabilistic approach using Markov Chain Monte Carlo techniques enabling additional uncertainty quantification. In this paper we examine the potential of the hierarchical deconvolution approach using two different prior models for the length-scale function.We apply the developed methodology to compute the backscattered powers of measured Polar MesosphericWinter Echoes, as well as Summer Echoes, from the EISCAT VHF radar in Tromsø, Norway. Computational accuracy and performance are tested using a simulated signal corresponding to a typical background ionosphere and a sporadic E layer with known ground-truth. The results suggest that the proposed hierarchical deconvolution approach can recover accurate and clean reconstructions of profiles, and the potential to be successfully applied to similar problems.

Snizhana Ross et al.

Status: open (until 27 Oct 2021)

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Snizhana Ross et al.

Snizhana Ross et al.

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Short summary
Radar measurements of thermal fluctuations in the Earth's ionosphere produce weak signals and tuning to specific altitudes results in suboptimal resolution for other regions, making an accurate analysis of these changes difficult. A novel approach to improve the resolution and remove measurement noise is considered. The method can capture variable characteristics, making it ideal for the study of a large range of data. Synthetically generated examples and two measured datasets were considered.