[1] Constant 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%.

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Investigation of the variability of total electron content (TEC) is one of the most important parameters of the observation and monitoring of space weather, which is the main cause of signal disturbance in space-based communication, positioning, and navigation systems. TEC is defined as the total number of electrons on a ray path.The Global Positioning System (GPS) provides a cost-effective solution for the estimation of TEC. Due to various physical and operational disturbances, TEC may have temporal and spatial domain gaps. Global ionospheric maps (GIMs) provide worldwide TEC with 1-to 2-h temporal resolution and 2.5 • ×5 • spatial resolution in latitude and longitude, respectively.The GIM-TEC with the highest possible accuracy can be obtained 10 days after the recording of the signals. Therefore, a high-resolution and accurate interpolation of TEC is necessary to image and monitor the regional distribution of TEC in near-real time. In this study, a novel spatiotemporal interpolation algorithm with automatic gridding is developed for 2-D TEC imaging by data fusion of GPS-TEC and GIM-TEC. The algorithm automatically implements optimum spatial resolution and desired temporal resolution with universal kriging with linear trend for midlatitude regions and ordinary kriging for other regions. The theoretical semivariogram function is estimated from GPS network data using a Matern family, whose parameters are determined with a particle swarm optimization algorithm. The developed algorithm is applied to the Turkish National Permanent GPS Network (TNPGN-Active), a dense midlatitude GPS network. For the first time in the literature, high spatial resolution TEC maps are obtained between May 2009 and May 2012 with a 2.5min temporal update period. These TEC maps will be used to investigate the spatiotemporal variability of the ionosphere over the diurnal and annual trend structure, including seasonal anomalies and geomagnetic and seismic disturbances over ionosphere.

On 23 October 2011, a very strong earthquake with a magnitude of Mw = 7.2 shook Eastern Anatolia, and tremors were felt up to 500 km from the epicentre. In this study, we present an early analysis of ionospheric disturbance due to this earthquake using Global Positioning Satellite-Total Electron Content (GPS-TEC). The variability with respect to average quiet day TEC (AQDT) and variability between the consecutive days are measured with symmetric Kullback-Leibler divergence (SKLD). A significant variability in total electron content (TEC) is observed from the GPS stations in the 150 km neighbourhood of the epicentre eight and nine days prior to the earthquake. An ionospheric disturbance is observed from GPS stations even more than 1,000 km to the epicentre, especially those on the North Anatolian fault (NAF). The present results support the existence of lithosphere-atmosphere-ionosphere coupling (LAIC) associated with Van, Turkey earthquake. © 2012 Taylor and Francis Group, LLC

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