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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Emilia Kilpua, Liisa Juusola, Maxime Grandin, Antti Kero, Stepan Dubyagin, Noora Partamies, Adnane Osmane, Harriet George, Milla Kalliokoski, Tero Raita, Timo Asikainen, and Minna Palmroth
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Liisa Juusola, Sanni Hoilijoki, Yann Pfau-Kempf, Urs Ganse, Riku Jarvinen, Markus Battarbee, Emilia Kilpua, Lucile Turc, and Minna Palmroth
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The solar wind interacts with the Earth’s magnetic field, forming a magnetosphere. On the night side solar wind stretches the magnetosphere into a long tail. A process called magnetic reconnection opens the magnetic field lines and reconnects them, accelerating particles to high energies. We study this in the magnetotail using a numerical simulation model of the Earth’s magnetosphere. We study the motion of the points where field lines reconnect and the fast flows driven by this process.
Liisa Juusola, Yann Pfau-Kempf, Urs Ganse, Markus Battarbee, Thiago Brito, Maxime Grandin, Lucile Turc, and Minna Palmroth
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Jorge L. Chau, Derek McKay, Juha P. Vierinen, Cesar La Hoz, Thomas Ulich, Markku Lehtinen, and Ralph Latteck
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Minna Palmroth, Sanni Hoilijoki, Liisa Juusola, Tuija I. Pulkkinen, Heli Hietala, Yann Pfau-Kempf, Urs Ganse, Sebastian von Alfthan, Rami Vainio, and Michael Hesse
Ann. Geophys., 35, 1269–1274, https://doi.org/10.5194/angeo-35-1269-2017, https://doi.org/10.5194/angeo-35-1269-2017, 2017
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Much like solar flares, substorms occurring within the Earth's magnetic domain are explosive events that cause vivid auroral displays. A decades-long debate exists to explain the substorm onset. We devise a simulation encompassing the entire near-Earth space and demonstrate that detailed modelling of magnetic reconnection explains the central substorm observations. Our results help to understand the unpredictable substorm process, which will significantly improve space weather forecasts.
Noora Partamies, James M. Weygand, and Liisa Juusola
Ann. Geophys., 35, 1069–1083, https://doi.org/10.5194/angeo-35-1069-2017, https://doi.org/10.5194/angeo-35-1069-2017, 2017
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Large-scale undulations of the diffuse aurora boundary, auroral omega bands, have been studied based on 438 omega-like structures identified over Fennoscandian Lapland from 1996 to 2007. The omegas mainly occurred in the post-magnetic midnight sector, in the region between oppositely directed ionospheric field-aligned currents, and during substorm recovery phases. The omega bands were observed during substorms, which were more intense than the average substorm in the same region.
Yann Pfau-Kempf, Heli Hietala, Steve E. Milan, Liisa Juusola, Sanni Hoilijoki, Urs Ganse, Sebastian von Alfthan, and Minna Palmroth
Ann. Geophys., 34, 943–959, https://doi.org/10.5194/angeo-34-943-2016, https://doi.org/10.5194/angeo-34-943-2016, 2016
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We have simulated the interaction of the solar wind – the charged particles and magnetic fields emitted by the Sun into space – with the magnetic field of the Earth. The solar wind flows supersonically and creates a shock when it encounters the obstacle formed by the geomagnetic field. We have identified a new chain of events which causes phenomena in the downstream region to eventually cause perturbations at the shock and even upstream. This is confirmed by ground and satellite observations.
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
Related subject area
Data base
Making geoscientific lab data FAIR: a conceptual model for a geophysical laboratory database
A universal and multi-dimensional model for analytical data on geological samples
Expanding HadISD: quality-controlled, sub-daily station data from 1931
A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland)
The abandoned surface mining sites in the Czech Republic: mapping and creating a database with a GIS web application
Determining the focal mechanisms of the events in the Carpathian region of Ukraine
Sven Nordsiek and Matthias Halisch
Geosci. Instrum. Method. Data Syst., 13, 63–73, https://doi.org/10.5194/gi-13-63-2024, https://doi.org/10.5194/gi-13-63-2024, 2024
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Research data resulting from geoscientific laboratory methods comprise multiple types and formats. A diversity of Earth system science fuels are needed to make research data available to scientists beyond a particular discipline. Global working groups define standards and create tools to improve research data handling in general. Adaption of these solutions to the needs of the geoscientific disciplines is vital to enable the development of a widely applicable geoscientific laboratory database.
Yutong He, Di Tian, Hongxia Wang, Li Yao, Miao Yu, and Pengfei Chen
Geosci. Instrum. Method. Data Syst., 8, 277–284, https://doi.org/10.5194/gi-8-277-2019, https://doi.org/10.5194/gi-8-277-2019, 2019
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A universal data model is a core to converge big geoanalytical data. We studied geoanalytical instruments, geological samples, and geoanalytical results and give a summarization of comprehensive geoanalytical data. We abstracted the data contents and designed the data model. It can be used for the construction of any geoanalytical data management system, data sharing system, or database by professional or amateur developers. Morever, we highly improved the efficiency of the data model.
Robert J. H. Dunn, Kate M. Willett, David E. Parker, and Lorna Mitchell
Geosci. Instrum. Method. Data Syst., 5, 473–491, https://doi.org/10.5194/gi-5-473-2016, https://doi.org/10.5194/gi-5-473-2016, 2016
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We have extended the sub-daily, integrated HadISD back to 1931 to double the time coverage of the dataset. We have updated and improved the station selection and merging procedure, which will be rerun on an annual basis to prevent it becoming out of date. The quality-control code has been rewritten from IDL to Python2.7 to make it clearer and more accessible. We have also calculated humidity and heat-stress variables in HadISD.2.0.0. This increases the value and applicability of this dataset.
Richard Essery, Anna Kontu, Juha Lemmetyinen, Marie Dumont, and Cécile B. Ménard
Geosci. Instrum. Method. Data Syst., 5, 219–227, https://doi.org/10.5194/gi-5-219-2016, https://doi.org/10.5194/gi-5-219-2016, 2016
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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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The paper is aimed at the evidence of old quarries and other open mining sites in former Czechoslovakia. It is the result of several years of research. In the manuscript is described a very interesting history of the mapping in our country and especially the digital database and the web map building process. We register 10 000 historical mining sites that were active in the first half of the 20th century. The web map is designated for geologist, historians, landscape ecologists, biologists, etc.
A. Pavlova, O. Hrytsai, and D. Malytskyy
Geosci. Instrum. Method. Data Syst., 3, 229–239, https://doi.org/10.5194/gi-3-229-2014, https://doi.org/10.5194/gi-3-229-2014, 2014
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The paper is devoted to mathematical modelling of propagation of seismic waves in inhomogeneous media. The trial and error method for determining the angles of orientation of fault plane and earthquake mechanism has been proposed. The graphic and trial and error approaches have been applied for determining the source parameters of earthquakes in the seismically active region of eastern Carpathian.
Cited articles
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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....
