Articles | Volume 15, issue 12
https://doi.org/10.5194/amt-15-3843-2022
https://doi.org/10.5194/amt-15-3843-2022
Research article
 | 
28 Jun 2022
Research article |  | 28 Jun 2022

Hierarchical deconvolution for incoherent scatter radar data

Snizhana Ross, Arttu Arjas, Ilkka I. Virtanen, Mikko J. Sillanpää, Lassi Roininen, and Andreas Hauptmann

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-287', Ryan Volz, 10 Nov 2021
  • RC2: 'Comment on amt-2021-287', Anonymous Referee #2, 17 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Snizhana Ross on behalf of the Authors (19 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (24 Jan 2022) by Jorge Luis Chau
RR by Anonymous Referee #3 (25 Feb 2022)
RR by Koji Nishimura (03 Mar 2022)
ED: Publish subject to minor revisions (review by editor) (03 Mar 2022) by Jorge Luis Chau
AR by Snizhana Ross on behalf of the Authors (02 May 2022)  Author's response    Manuscript
ED: Publish as is (03 May 2022) by Jorge Luis Chau
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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.