Hierarchical deconvolution for incoherent scatter radar data
نویسندگان
چکیده
Abstract. We propose a novel method for deconvolving incoherent scatter radar data to recover accurate reconstructions of backscattered powers. The problem is modelled as hierarchical noise-perturbed deconvolution problem, where the lower hierarchy consists an adaptive length-scale function that allows non-stationary prior and such enables recovery smooth narrow layers in profiles. estimation done Bayesian statistical inversion framework two-step procedure, hyperparameters are first estimated by optimisation followed analytical closed-form solution deconvolved signal. proposed optimisation-based compared fully probabilistic approach using Markov chain Monte Carlo techniques enabling additional uncertainty quantification. In this paper we examine potential two different models function. apply developed methodology compute powers measured polar mesospheric winter echoes, well summer from EISCAT VHF Tromsø, Norway. Computational accuracy performance tested simulated signal corresponding typical background ionosphere sporadic E layer with known ground truth. results suggest can clean profiles, be successfully applied similar problems.
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ژورنال
عنوان ژورنال: Atmospheric Measurement Techniques
سال: 2022
ISSN: ['1867-1381', '1867-8548']
DOI: https://doi.org/10.5194/amt-15-3843-2022