Articles | Volume 15, issue 12
https://doi.org/10.5194/amt-15-3843-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-3843-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hierarchical deconvolution for incoherent scatter radar data
Snizhana Ross
CORRESPONDING AUTHOR
Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
Arttu Arjas
Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
Ilkka I. Virtanen
Research Unit of Space Physics and Astronomy, University of Oulu, 90014 Oulu, Finland
Mikko J. Sillanpää
Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
Lassi Roininen
School of Engineering Science, Lappeenranta-Lahti University of Technology, 53851 Lappeenranta, Finland
Andreas Hauptmann
Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
Department of Computer Science, University College London, London WC1E 6BT, UK
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Noora Partamies, Rowan Dayton-Oxland, Katie Herlingshaw, Ilkka Virtanen, Bea Gallardo-Lacourt, Mikko Syrjäsuo, Fred Sigernes, Takanori Nishiyama, Toshi Nishimura, Mathieu Barthelemy, Anasuya Aruliah, Daniel Whiter, Lena Mielke, Maxime Grandin, Eero Karvinen, Marjan Spijkers, and Vincent E. Ledvina
Ann. Geophys., 43, 349–367, https://doi.org/10.5194/angeo-43-349-2025, https://doi.org/10.5194/angeo-43-349-2025, 2025
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We studied the first broad band emissions, called continuum, in the dayside aurora. They are similar to Strong Thermal Emission Velocity Enhancement (STEVE) with white-, pale-pink-, or mauve-coloured light. But unlike STEVE, they follow the dayside aurora forming rays and other dynamic shapes. We used ground optical and radar observations and found evidence of heating and upwelling of both plasma and neutral air. This study provides new information on conditions for continuum emission, but its understanding will require further work.
Ilkka I. Virtanen, Ayanew Nigusie, Antti Kero, Neethal Thomas, and Juhana Lankinen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2462, https://doi.org/10.5194/egusphere-2025-2462, 2025
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EISCAT3D is an ionospheric radar currently under construction in Northern Fenno-Scandinavia. The radar will make 3D measurements of the ionosphere at 50–1000 km altitudes. We show that the so-called multipurpose radar modulations and optimal data analysis can improve time resolution of the measurements by more than an order of magnitude, and they enable one to measure ion-neutral collision frequencies, which are proportional to neutral particle density, in the lower ionosphere.
Liisa Juusola, Ilkka Virtanen, Spencer Mark Hatch, Heikki Vanhamäki, Maxime Grandin, Noora Partamies, Urs Ganse, Ilja Honkonen, Abiyot Workayehu, Antti Kero, and Minna Palmroth
EGUsphere, https://doi.org/10.5194/egusphere-2025-2394, https://doi.org/10.5194/egusphere-2025-2394, 2025
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Key properties of the ionospheric electrodynamics are electric fields, currents, and conductances. They provide a window to the vast and distant near-Earth space, cause Joule heating that affect satellite orbits, and drive geomagnetically induced currents (GICs) in technological conductor networks. We have developed a new method for solving the key properties of ionospheric electrodynamics from ground-based magnetic field observations.
Oliver Stalder, Björn Gustavsson, and Ilkka Virtanen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2340, https://doi.org/10.5194/egusphere-2025-2340, 2025
This preprint is open for discussion and under review for Annales Geophysicae (ANGEO).
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The rapid changes in ion composition during auroral are dynamically modeled by integrating the coupled continuity equations for 15 ionospheric species. The effect of the ionospheric variation on the inversion of ISR electron density profiles to differential energy spectra of precipitating electrons is studied. A systematic overestimation at high electron energies can be removed using a dynamic model. Comparisons are made with static and steady-state ionospheric models.
Spencer Mark Hatch, Ilkka Virtanen, Karl Magnus Laundal, Habtamu Wubie Tesfaw, Juha Vierinen, Devin Ray Huyghebaert, Andres Spicher, and Jens Christian Hessen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1768, https://doi.org/10.5194/egusphere-2025-1768, 2025
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This study addresses the design of next-generation incoherent scatter radar experiments used to study the ionosphere, particularly with systems that have multiple sites. We have developed a method to estimate uncertainties of measurements of plasma density, temperature, and ion drift. Our method is open-source, and helps to optimize radar configurations and assess the effectiveness of an experiment. This method ultimately serves to enhance our understanding of Earth's space environment.
Devin Huyghebaert, Björn Gustavsson, Juha Vierinen, Andreas Kvammen, Matthew Zettergren, John Swoboda, Ilkka Virtanen, Spencer M. Hatch, and Karl M. Laundal
Ann. Geophys., 43, 99–113, https://doi.org/10.5194/angeo-43-99-2025, https://doi.org/10.5194/angeo-43-99-2025, 2025
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The EISCAT_3D radar is a new ionospheric radar under construction in the Fennoscandia region. The radar will make measurements of plasma characteristics at altitudes above approximately 60 km. The capability of the system to make these measurements at spatial scales of less than 100 m using multiple digitised signals from each of the radar antenna panels is highlighted. There are many ionospheric small-scale processes that will be further resolved using the techniques discussed here.
Maxime Grandin, Noora Partamies, and Ilkka I. Virtanen
Ann. Geophys., 42, 355–369, https://doi.org/10.5194/angeo-42-355-2024, https://doi.org/10.5194/angeo-42-355-2024, 2024
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Auroral displays typically take place at high latitudes, but the exact latitude where the auroral breakup occurs can vary. In this study, we compare the characteristics of the fluxes of precipitating electrons from space during auroral breakups occurring above Tromsø (central part of the auroral zone) and above Svalbard (poleward boundary of the auroral zone). We find that electrons responsible for the aurora above Tromsø carry more energy than those precipitating above Svalbard.
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
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The biasing effect in meteor radar temperature has been a pressing issue for the last 2 decades. This paper has addressed the underlying reasons for such a biasing effect on both theoretical and experimental grounds. An improved statistical method has been developed which allows atmospheric temperatures at around 90 km to be measured with meteor radar in an independent way such that any subsequent bias correction or calibration is no longer required.
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
Beynon, W. J. G. and Williams, P. J. S.: Incoherent scatter of radio waves from the ionosphere, Rep. Prog. Phys., 41, 909–947, https://doi.org/10.1088/0034-4885/41/6/003, 1978. a
Bilitza, D., Altadill, D., Truhlik, V., Shubin, V., Galkin, I., Reinisch, B., and Huang, X.: International Reference Ionosphere 2016: From ionospheric climate to real-time weather predictions, Space Weather, 15, 418–429, https://doi.org/10.1002/2016SW001593, 2017. a
Damtie, B., Nygrén, T., Lehtinen, M. S., and Huuskonen, A.: High resolution observations of sporadic-E layers within the polar cap ionosphere using a new incoherent scatter radar experiment, Ann. Geophys., 20, 1429–1438, https://doi.org/10.5194/angeo-20-1429-2002, 2002. a, b, c
Damtie, B., Lehtinen, M. S., Orispää, M., and Vierinen, J.: Mismatched filtering of aperiodic quadriphase codes, IEEE T. Inform. Theory, 54, 1742–1749, https://doi.org/10.1109/TIT.2008.917655, 2008. a
EISCAT Scientific Association: EISCAT Operations Schedule, June 2022, EISCAT data archive [data set], https://portal.eiscat.se/schedule/, last access: 23 June 2022. a
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B.: Bayesian data analysis, Chapman and Hall/CRC, https://doi.org/10.1201/b16018, 2013. a
Holt, J. M., Rhoda, D. A., Tetenbaum, D., and van Eyken, A. P.: Optimal analysis of incoherent scatter radar data, Radio Sci., 27, 435–447, https://doi.org/10.1029/91RS02922, 1992. a
Huuskonen, A. and Lehtinen, M. S.: The accuracy of incoherent scatter measurements: error estimates valid for high signal levels, J. Atmos. Terr. Phys., 58, 453–463, https://doi.org/10.1016/0021-9169(95)00048-8, 1996. a
Huuskonen, A., Pollari, P., Nygren, T., and Lehtinen, M. S.: Range ambiguity effects in Barker-coded multipulse experiments with incoherent scatter radars, J. Atmos. Terr. Phys., 50, 265–276, https://doi.org/10.1016/0021-9169(88)90013-X, 1988. a, b
Hysell, D. L., Rodrigues, F. S., Chau, J. L., and Huba, J. D.: Full profile incoherent scatter analysis at Jicamarca, Ann. Geophys., 26, 59–75, https://doi.org/10.5194/angeo-26-59-2008, 2008. a
Kaipio, J. and Somersalo, E.: Statistical and computational inverse problems, Springer-Verlag, New York, https://doi.org/10.1007/b138659, 2004. a
Kirkwood, S.: Polar mesosphere winter echoes – A review of recent results, Adv. Space. Res, 40, 751–757, https://doi.org/10.1016/j.asr.2007.01.024, 2007. a
Kärkkäinen, H. P. and Sillanpää, M. J.: Back to basics for Bayesian model building in genomic selection, Genetics, 191, 969–987, https://doi.org/10.1534/genetics.112.139014, 2012. a
Lehtinen, M. S., Damtie, B., and Nygrén, T.: Optimal binary phase codes and sidelobe-free decoding filters with application to incoherent scatter radar, Ann. Geophys., 22, 1623–1632, https://doi.org/10.5194/angeo-22-1623-2004, 2004. a
Lehtinen, M., Damtie, B., Piiroinen, P., and Orispää, M.: Perfect and almost perfect pulse compression codes for range spread radar targets, Inverse Probl. Imag., 3, 465–486, https://doi.org/10.3934/ipi.2009.3.465, 2009. a
Lehtinen, M. S. and Huuskonen, A.: General incoherent scatter analysis and GUISDAP, J. Atmos. Terr. Phys., 58, 435–452, https://doi.org/10.1016/0021-9169(95)00047-X, 1996. a
Lehtinen, M. S., Huuskonen, A., and Pirttilä, J.: First experiences of full-profile analysis with GUISDAP, Ann. Geophys., 14, 1487–1495, https://doi.org/10.1007/s00585-996-1487-3, 1996. a
Lindgren, F., Rue, H., and Lindström, J.: An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach, J. Roy. Stat. Soc. B, 73, 423–498, https://doi.org/10.1111/j.1467-9868.2011.00777.x, 2011. a
Liu, D. C. and Nocedal, J.: On the limited memory BFGS method for large scale optimization, Math. Program., 45, 503–528, https://doi.org/10.1007/BF01589116, 1989. a
Markkanen, J., Lehtinen, M., and Landgraf, M.: Real-time space debris monitoring with EISCAT, Adv. Space Res., 35, 1197–1209, https://doi.org/10.1016/j.asr.2005.03.038, 2005. a
Markkanen, M., Roininen, L., Huttunen, J. M., and Lasanen, S.: Cauchy difference priors for edge-preserving Bayesian inversion, J. Inverse Ill-Pose. P., 27, 225–240, 2019. a
Mathews, J. D.: Sporadic E: Current views and recent progress, J. Atmos. Sol.-Terr. Phys., 60, 413–435, https://doi.org/10.1016/S1364-6826(97)00043-6, 1998. a
Nikoukar, R., Kamalabadi, F., Kudeki, E., and Sulzer, M. P.: An efficient near-optimal approach to incoherent scatter radar parameter estimation, Radio Sci., 43, RS5007, https://doi.org/10.1029/2007RS003724, 2008. a, b
Park, T. and Casella, G.: The Bayesian lasso, J. Am. Stat. Assoc., 103, 681–686, https://doi.org/10.1198/016214508000000337, 2008. a
Pellinen-Wannberg, A. and Wannberg, G.: Meteor observations with the European incoherent scatter UHF radar, J. Geophys. Res., 99, 11379–11390, https://doi.org/10.1029/94JA00274, 1994. a
Pollari, P., Huuskonen, A., Turunen, E., and Turunen, T.: Range ambiguity effects in a phase coded D-region incoherent scatter radar experiment, J. Atmos. Terr. Phys., 51, 937–945, https://doi.org/10.1016/0021-9169(89)90009-3, 1989.
a, b
Rapp, M. and Lübken, F.-J.: Polar mesosphere summer echoes (PMSE): Review of observations and current understanding, Atmos. Chem. Phys., 4, 2601–2633, https://doi.org/10.5194/acp-4-2601-2004, 2004. a
Rasmussen, C. E. and Williams, C. K. I.: Gaussian processes for machine learning (adaptive computation and machine learning), The MIT Press, ISBN: 9780262182539, 2006. a
Repetti, A., Pham, M. Q., Duval, L., Chouzenoux, E., and Pesquet, J.-C.: Euclid in a taxicab: sparse blind deconvolution with smoothed regularization, IEEE Signal Proc. Let., 22, 539–543, https://doi.org/10.1109/LSP.2014.2362861, 2014. a
Robert, C. P. and Casella, G.: Introducing Monte Carlo methods with R, Springer, New York, https://doi.org/10.1007/978-1-4419-1576-4, 2009. a
Roininen, L. and Lehtinen, M. S.: Perfect pulse-compression coding via ARMA algorithms and unimodular transfer functions, Inverse Probl. Imag., 7, 649–661, https://doi.org/10.3934/ipi.2013.7.649, 2013. a
Roininen, L., Lehtinen, M. S., Piiroinen, P., and Virtanen, I. I.: Perfect radar pulse compression via unimodular Fourier multipliers, Inverse Probl. Imag., 8, 831–844, https://doi.org/10.3934/ipi.2014.8.831, 2014a. a
Roininen, L., Huttunen, J. M. J., and Lasanen, S.: Whittle-Matérn priors for Bayesian statistical inversion with applications in electrical impedance tomography, Inverse Probl. Imag., 8, 561–586, https://doi.org/10.3934/ipi.2014.8.561, 2014b. a, b, c
Roininen, L., Girolami, M., Lasanen, S., and Markkanen, M.: Hyperpriors for Matérn fields with applications in Bayesian inversion, Inverse Probl. Imag., 13, 1–29, https://doi.org/10.3934/ipi.2019001, 2019. a, b
Skolnik, M. I. (Ed.): Radar Handbook, McGraw-Hill Publishing Company, 3rd edn., ISBN: 97800714854702008. a
Swoboda, J., Semeter, J., Zettergren, M., and Erickson, P. J.: Observability of ionospheric space-time structure with ISR: A simulation study, Radio Sci., 52, 215–234, https://doi.org/10.1002/2016RS006182, 2017. a
Virtanen, I. I., Lehtinen, M. S., Nygrén, T., Orispää, M., and Vierinen, J.: Lag profile inversion method for EISCAT data analysis, Ann. Geophys., 26, 571–581, https://doi.org/10.5194/angeo-26-571-2008, 2008. a, b, c, d
Virtanen, I. I., Vierinen, J., and Lehtinen, M. S.: Phase-coded pulse aperiodic transmitter coding, Ann. Geophys., 27, 2799–2811, https://doi.org/10.5194/angeo-27-2799-2009, 2009. a
Virtanen, I. I., Tesfaw, H. W., Roininen, L., Lasanen, S., and Aikio, A.: Bayesian Filtering in Incoherent Scatter Plasma Parameter Fits, J. Geophys. Res.-Space, 126, e2020JA028700, https://doi.org/10.1029/2020JA028700, 2021. a, b
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.
Radar measurements of thermal fluctuations in the Earth's ionosphere produce weak signals, and...