Articles | Volume 5, issue 1
https://doi.org/10.5194/gi-5-263-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gi-5-263-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Jul 2016
Research article |
| 01 Jul 2016
Sodankylä ionospheric tomography data set 2003–2014
Johannes Norberg
CORRESPONDING AUTHOR
Finnish Meteorological Institute, Helsinki, Finland
Sodankylä Geophysical Observatory, University of Oulu, Sodankylä, Finland
Lassi Roininen
Sodankylä Geophysical Observatory, University of Oulu, Sodankylä, Finland
Department of Mathematics, Tallinn University of Technology, Tallinn, Estonia
Antti Kero
Sodankylä Geophysical Observatory, University of Oulu, Sodankylä, Finland
Tero Raita
Sodankylä Geophysical Observatory, University of Oulu, Sodankylä, Finland
Thomas Ulich
Sodankylä Geophysical Observatory, University of Oulu, Sodankylä, Finland
Markku Markkanen
Eigenor Corporation, Sodankylä, Finland
Liisa Juusola
Finnish Meteorological Institute, Helsinki, Finland
Kirsti Kauristie
Finnish Meteorological Institute, Helsinki, Finland
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We validate 2-D ionospheric tomography reconstructions against EISCAT incoherent scatter radar measurements. The method is based on Bayesian statistical inversion. We employ ionosonde measurements for the choice of the prior distribution parameters and use a sparse matrix approximation for the computations. This results in a computationally efficient tomography algorithm with clear probabilistic interpretation. We find that ionosonde measurements improve the reconstruction significantly.
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Noora Partamies, Bas Dol, Vincent Teissier, Liisa Juusola, Mikko Syrjäsuo, and Hjalmar Mulders
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Auroral imaging produces large amounts of image data that can no longer be analysed by visual inspection. Thus, every step towards automatic analysis tools is crucial. Previously supervised learning methods have been used in auroral physics, with a human expert provided ground truth. However, this ground truth is debatable. We present an unsupervised learning method, which shows promising results in detecting auroral breakups in the all-sky image data.
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Ann. Geophys., 41, 511–528, https://doi.org/10.5194/angeo-41-511-2023, https://doi.org/10.5194/angeo-41-511-2023, 2023
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We use computed tomography to reconstruct the three-dimensional distributions of the Hall and Pedersen conductivities of pulsating auroras, a key research target for understanding the magnetosphere–ionosphere coupling process. It is suggested that the high-energy electron precipitation associated with pulsating auroras may have a greater impact on the closure of field-aligned currents in the ionosphere than has been previously reported.
Liisa Juusola, Ari Viljanen, Noora Partamies, Heikki Vanhamäki, Mirjam Kellinsalmi, and Simon Walker
Ann. Geophys., 41, 483–510, https://doi.org/10.5194/angeo-41-483-2023, https://doi.org/10.5194/angeo-41-483-2023, 2023
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At times when auroras erupt on the sky, the magnetic field surrounding the Earth undergoes rapid changes. On the ground, these changes can induce harmful electric currents in technological conductor networks, such as powerlines. We have used magnetic field observations from northern Europe during 28 such events and found consistent behavior that can help to understand, and thus predict, the processes that drive auroras and geomagnetically induced currents.
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Kirsti Kauristie, Minna Myllys, Noora Partamies, Ari Viljanen, Pyry Peitso, Liisa Juusola, Shabana Ahmadzai, Vikramjit Singh, Ralf Keil, Unai Martinez, Alexej Luginin, Alexi Glover, Vicente Navarro, and Tero Raita
Geosci. Instrum. Method. Data Syst., 5, 253–262, https://doi.org/10.5194/gi-5-253-2016, https://doi.org/10.5194/gi-5-253-2016, 2016
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We use the connection between auroras and geomagnetic field variations in a concept for a Regional Auroral Forecast (RAF) service. RAF is based on statistical relationships between alerts by the NOAA Space Weather Prediction Center and magnetic time derivatives measured by five MIRACLE magnetometer stations located in the surroundings of the Sodankylä research station. As an improvement to previous similar services RAF yields knowledge on typical auroral storm durations at different latitudes.
Carsten Baumann, Markus Rapp, and Antti Kero
Ann. Geophys., 34, 573–580, https://doi.org/10.5194/angeo-34-573-2016, https://doi.org/10.5194/angeo-34-573-2016, 2016
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Meteor smoke particles (MSPs), originating from evaporated meteoric matter at 60–110 km altitude, are present in the whole atmosphere including polar regions. As electron precipitation is present at high latitudes, these MSPs are bombarded by energetic electrons. The energetic electrons can enter the MSPs and excite secondary electrons. That can lead to a change of the charge state of these MSPs. The study finds that other charging processes, e.g., electron attachment, are more important.
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
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We validate 2-D ionospheric tomography reconstructions against EISCAT incoherent scatter radar measurements. The method is based on Bayesian statistical inversion. We employ ionosonde measurements for the choice of the prior distribution parameters and use a sparse matrix approximation for the computations. This results in a computationally efficient tomography algorithm with clear probabilistic interpretation. We find that ionosonde measurements improve the reconstruction significantly.
K. Kauristie, M. V. Uspensky, N. G. Kleimenova, O. V. Kozyreva, M. M. J. L. Van De Kamp, S. V. Dubyagin, and S. Massetti
Ann. Geophys., 34, 379–392, https://doi.org/10.5194/angeo-34-379-2016, https://doi.org/10.5194/angeo-34-379-2016, 2016
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This study presents some example events in which sudden changes in the auroral activity at midnight sector seem to have an impact on the intensity of morning-sector magnetic pulsations. Mechanisms which could link these two separate regions are discussed in the paper. Sudden changes in the solar wind properties and fast westward-propagating electrons are suggested to explain the coupling between midnight-sector and morning-sector phenomena.
Juha Vierinen, Anthea J. Coster, William C. Rideout, Philip J. Erickson, and Johannes Norberg
Atmos. Meas. Tech., 9, 1303–1312, https://doi.org/10.5194/amt-9-1303-2016, https://doi.org/10.5194/amt-9-1303-2016, 2016
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We present a statistical framework for estimating GNSS receiver bias by using a weighted linear least squares of independent differences (WLLSID) model to examine differences of a large number of TEC measurements. This allows a consistent way for treating elevation-dependent model errors and spatiotemporal distance-dependent geophysical differences arising in ionospheric GNSS measurements. The method is also applicable to other GNSS system than GPS, supporting, e.g., GLONASS.
Carl-Fredrik Enell, Alexander Kozlovsky, Tauno Turunen, Thomas Ulich, Sirkku Välitalo, Carlo Scotto, and Michael Pezzopane
Geosci. Instrum. Method. Data Syst., 5, 53–64, https://doi.org/10.5194/gi-5-53-2016, https://doi.org/10.5194/gi-5-53-2016, 2016
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Ionograms from the Sodankylä Geophysical Observatory ionosonde (station SO166) were scaled automatically with the Autoscala software during a test period. The results were compared with manually scaled ionospheric parameters. In general, the F-layer parameters were found to agree well, whereas high-latitude phenomena like auroral E layers were often misidentified.
M. Myllys, N. Partamies, and L. Juusola
Ann. Geophys., 33, 573–581, https://doi.org/10.5194/angeo-33-573-2015, https://doi.org/10.5194/angeo-33-573-2015, 2015
P. T. Verronen, M. E. Andersson, A. Kero, C.-F. Enell, J. M. Wissing, E. R. Talaat, K. Kauristie, M. Palmroth, T. E. Sarris, and E. Armandillo
Ann. Geophys., 33, 381–394, https://doi.org/10.5194/angeo-33-381-2015, https://doi.org/10.5194/angeo-33-381-2015, 2015
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Electron concentrations observed by EISCAT radars can be reasonable well represented using AIMOS v1.2 satellite-data-based ionization model and SIC D-region ion chemistry model. SIC-EISCAT difference varies from event to event, probably because the statistical nature of AIMOS ionization is not capturing all the spatio-temporal fine structure of electron precipitation. Below 90km, AIMOS overestimates electron ionization because of proton contamination of the satellite electron detectors.
M. van de Kamp, D. Pokhotelov, and K. Kauristie
Ann. Geophys., 32, 1511–1532, https://doi.org/10.5194/angeo-32-1511-2014, https://doi.org/10.5194/angeo-32-1511-2014, 2014
K. Andréeová, L. Juusola, E. K. J. Kilpua, and H. E. J. Koskinen
Ann. Geophys., 32, 1293–1302, https://doi.org/10.5194/angeo-32-1293-2014, https://doi.org/10.5194/angeo-32-1293-2014, 2014
C. Baumann, M. Rapp, A. Kero, and C.-F. Enell
Ann. Geophys., 31, 2049–2062, https://doi.org/10.5194/angeo-31-2049-2013, https://doi.org/10.5194/angeo-31-2049-2013, 2013
N. Partamies, L. Juusola, E. Tanskanen, and K. Kauristie
Ann. Geophys., 31, 349–358, https://doi.org/10.5194/angeo-31-349-2013, https://doi.org/10.5194/angeo-31-349-2013, 2013
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Robert J. H. Dunn, Kate M. Willett, David E. Parker, and Lorna Mitchell
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Richard Essery, Anna Kontu, Juha Lemmetyinen, Marie Dumont, and Cécile B. Ménard
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Physically based models that predict the properties of snow on the ground are used in many applications, but meteorological input data required by these models are hard to obtain in cold regions. Monitoring at the Sodankyla research station allows construction of model input and evaluation datasets covering several years for the first time in the Arctic. The data are used to show that a sophisticated snow model developed for warmer and wetter sites can perform well in very different conditions.
Richard Pokorný and Marie Tereza Peterková
Geosci. Instrum. Method. Data Syst., 5, 143–149, https://doi.org/10.5194/gi-5-143-2016, https://doi.org/10.5194/gi-5-143-2016, 2016
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A. Pavlova, O. Hrytsai, and D. Malytskyy
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Cited articles
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Austen, J. R., Franke, S. J., and Liu, C. H.: Ionospheric imaging using computerized tomography, Radio Sci., 3, 299–307, 1988.
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Short summary
The Sodankylä Geophysical Observatory has been producing ionospheric tomography data since 2003. Based on these data, one solar cycle of ionospheric vertical total electron content (VTEC) estimates is constructed. The measurements are compared against the IRI-2012 model, F10.7 solar flux index and sunspot number data. Qualitatively the tomographic VTEC estimate corresponds to reference data very well, but the IRI-2012 model are on average 40 % higher of that of the tomographic results.
The Sodankylä Geophysical Observatory has been producing ionospheric tomography data since 2003....
