Browsing by Subject "Space weather"
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Item Open Access Association of ionospheric storms and substorms of global electron content with proxy AE index(Pergamon Press, 2015) Yenen, S. D.; Gulyaeva, T. L.; Arikan, F.; Arıkan, OrhanStorm time modeling of Global Electron Content (GEC) calculated from GIM-TEC for 1999-2013 is associated with new proxy of Auroral Electrojet variability expressed as a smoothed and normalized Auroral Electrojet index (AEsn). The variability in GEC is captured by the computation of DGEC which is obtained by taking the hourly ratio of instant GEC to median of GEC values at the same hour of 7 preceding days. The storm onset is determined by a joint analysis of variations in IMF-B magnitude, its derivative (dB/dt) and direction of IMF-Bz together with sudden increase in AE exceeding 900 nT. The start of the pre-storm period is chosen to be 7 h prior to the storm onset time and the storm recovery period ends 41 h after the storm onset. The hourly AEsn is related to DGEC during the storm period through a polynomial whose coefficients are estimated in the linear least squares sense. Estimated coefficients are examined and grouped with respect to different kinds of auroral storms. Examples of modeling methodology are provided using four different kinds of intense storms and substorms, namely, Positive Arctic, Positive Antarctic, Negative Arctic and Negative Antarctic that occurred between 1999 and 2013. The estimated coefficients for storm periods are compared with those of non-storm periods. It is observed that the positive correlation between the increase of AE and GEC can be a promising precursor of space weather variability.Item Open Access Classification of regional ionospheric disturbance based on machine learning techniques(European Space Agency, 2016) Terzi, Merve Begüm; Arıkan, Orhan; Karatay, S.; Arıkan, F.; Gulyaeva, T.In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.Item Open Access Space weather activities of IONOLAB group using TNPGN GPS Network(IEEE, 2011) Aktug, B.; Lenk O.; Kurt, M.; Parmaksiz, E.; Ozdemir, S.; Arikan F.; Sezen, U.; Toker, C.; Arıkan, OrhanCharacterization and constant monitoring of variability of the ionosphere is of utmost importance for the performance improvement of HF communication, Satellite communication, navigation and guidance systems, Low Earth Orbit (LEO) satellite systems, Space Craft exit and entry into the atmosphere and space weather. Turkish National Permanent GPS Network (TNPGN) is the Reference Station Network of 146 continuously-operating GNSS stations of which are distributed uniformly across Turkey and North Cyprus Turkish Republic since May 2009. IONOLAB group is currently investigating new techniques for space-time interpolation, and automatic mapping of TEC through a TUBITAK research grant. It is utmost importance to develop regional stochastic models for correction of ionospheric delay in geodetic systems and also form a scientific basis for communication link characterization. This study is a brief summary of the efforts of IONOLAB group in monitoring of space weather, and correction of geodetic positioning errors due to ionosphere using TNPGN. © 2011 IEEE.Item Open Access Space-time interpolation and automatic mapping of TEC using TNPGN-active(2011-08) Arıkan, F.; Arıkan, Orhan; Sezen, U.; Toker, C.; Aktug, B.; Lenk, O.; Kurt ,M.; Parmaksız, E.Turkish National Permanent GPS Network (TNPGN) is the Reference Station Network of 146 continuously-operating GNSS stations o which are distributed uniformly across Turkey and North Cyprus Turkish Republic since May 2009. IONOLAB group, formed by researchers and students in Hacettepe University, Bilkent University and General Command of Mapping is currently investigating new techniques for space-time interpolation, and automatic mapping of TEC through a TUBITAK research grant. This study presents the developments in monitoring of space weather, and correction of geodetic positioning errors due to ionosphere using TNPGN. © 2011 IEEE.Item Open Access Spatio-temporal interpolation of total electron content using a GPS network(IEEE, 2013) Deviren, M. N.; Arıkan, F.; Arıkan, OrhanConstant monitoring and prediction of Space Weather events require investigation of the variability of total electron content (TEC), which is an observable feature of ionosphere using dual-frequency GPS receivers. Due to various physical and/or technical obstructions, the recordings of GPS receivers may be disrupted resulting in data loss in TEC estimates. Data recovery is very important for both filling in the data gaps for constant monitoring of ionosphere and also for spatial and/or temporal prediction of TEC. Spatial prediction can be obtained using the neighboring stations in a network of a dense grid. Temporal prediction recovers data using previous days of the GPS station in a less dense grid. In this study, two novel and robust spatio-temporal interpolation algorithms are introduced to recover TEC through optimization by using least squares fit to available data. The two algorithms are applied to a regional GPS network, and for a typical station, the number of days with full data increased from 68% to 85%.