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

Related authors

Interferometric Imaging with EISCAT_3D for Fine-Scale In-Beam Incoherent Scatter Spectra Measurements
Devin Huyghebaert, Björn Gustavsson, Juha Vierinen, Andreas Kvammen, Matthew Zettergren, John Swoboda, Ilkka Virtanen, Spencer Hatch, and Karl M. Laundal
EGUsphere, https://doi.org/10.5194/egusphere-2024-802,https://doi.org/10.5194/egusphere-2024-802, 2024
Short summary
How does auroral electron precipitation near the open–closed field line boundary compare to that within the auroral oval during substorm onset?
Maxime Grandin, Noora Partamies, and Ilkka I. Virtanen
EGUsphere, https://doi.org/10.5194/egusphere-2024-483,https://doi.org/10.5194/egusphere-2024-483, 2024
Short summary
Improved method of estimating temperatures at meteor peak heights
Emranul Sarkar, Alexander Kozlovsky, Thomas Ulich, Ilkka Virtanen, Mark Lester, and Bernd Kaifler
Atmos. Meas. Tech., 14, 4157–4169, https://doi.org/10.5194/amt-14-4157-2021,https://doi.org/10.5194/amt-14-4157-2021, 2021
Short summary
Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements
Johannes Norberg, Ilkka I. Virtanen, Lassi Roininen, Juha Vierinen, Mikko Orispää, Kirsti Kauristie, and Markku S. Lehtinen
Atmos. Meas. Tech., 9, 1859–1869, https://doi.org/10.5194/amt-9-1859-2016,https://doi.org/10.5194/amt-9-1859-2016, 2016
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
CALOTRITON: a convective boundary layer height estimation algorithm from ultra-high-frequency (UHF) wind profiler data
Alban Philibert, Marie Lothon, Julien Amestoy, Pierre-Yves Meslin, Solène Derrien, Yannick Bezombes, Bernard Campistron, Fabienne Lohou, Antoine Vial, Guylaine Canut-Rocafort, Joachim Reuder, and Jennifer K. Brooke
Atmos. Meas. Tech., 17, 1679–1701, https://doi.org/10.5194/amt-17-1679-2024,https://doi.org/10.5194/amt-17-1679-2024, 2024
Short summary
Enhancing consistency of microphysical properties of precipitation across the melting layer in dual-frequency precipitation radar data
Kamil Mroz, Alessandro Battaglia, and Ann M. Fridlind
Atmos. Meas. Tech., 17, 1577–1597, https://doi.org/10.5194/amt-17-1577-2024,https://doi.org/10.5194/amt-17-1577-2024, 2024
Short summary
Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer
Volker Wulfmeyer, Christoph Senff, Florian Späth, Andreas Behrendt, Diego Lange, Robert M. Banta, W. Alan Brewer, Andreas Wieser, and David D. Turner
Atmos. Meas. Tech., 17, 1175–1196, https://doi.org/10.5194/amt-17-1175-2024,https://doi.org/10.5194/amt-17-1175-2024, 2024
Short summary
Forward operator for polarimetric radio occultation measurements
Daisuke Hotta, Katrin Lonitz, and Sean Healy
Atmos. Meas. Tech., 17, 1075–1089, https://doi.org/10.5194/amt-17-1075-2024,https://doi.org/10.5194/amt-17-1075-2024, 2024
Short summary
Assessing atmospheric gravity wave spectra in the presence of observational gaps
Mohamed Mossad, Irina Strelnikova, Robin Wing, and Gerd Baumgarten
Atmos. Meas. Tech., 17, 783–799, https://doi.org/10.5194/amt-17-783-2024,https://doi.org/10.5194/amt-17-783-2024, 2024
Short summary

Cited articles

Adler, J. and Öktem, O.: Deep bayesian inversion, arXiv [preprint], arXiv:1811.05910, 14 November 2018. a
Arjas, A.: Hierarchical-deconvolution: Hierarchical deconvolution codes, Version V1, Zenodo [code], https://doi.org/10.5281/zenodo.6542699, 2022. a, b
Arjas, A., Hauptmann, A., and Sillanpää, M. J.: Estimation of dynamic SNP-heritability with Bayesian Gaussian process models, Bioinformatics, 36, 3795–3802, https://doi.org/10.1093/bioinformatics/btaa199, 2020a. a
Arjas, A., Roininen, L., Sillanpää, M. J., and Hauptmann, A.: Blind hierarchical deconvolution, in: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, 1–6, https://doi.org/10.1109/MLSP49062.2020.9231822, 2020b. a, b, c
Barker, R. H.: Group synchronizing of binary digital systems, in: Communication Theory, edited by: Jackson, W., Academic Press, New York, 273–287, 1953. a
Download
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.