TEC and foF2 variations: preliminary results

Kouris, S.S.; Xenos, Thomas D.; Polimeris, Konstantinos; Stergiou, D.

doi:10.4401/ag-3346

Cited by 19 publications

(14 citation statements)

References 2 publications

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“…In this way, a worse performance of the method was expected, when regions and periods with different ionospheric gradients are considered. More details on the influence of slab thickness in the relations between foF 2 and VTEC can be found in Spalla and Ciraolo [19], Leitinger et al [20], and Kouris et al [21]. Regarding the general performance of GIMs, it is worth mentioning the consistency of most of the products, presenting similar behavior, mainly if only the final products are considered, similar to other GIM assessment techniques [2].…”

Section: Daily and Weekly Analysismentioning

confidence: 80%

“…The method of assessment proposed in this paper is based on the correlation between the critical frequency at the F 2 layer and the VTEC [19][20][21]. Figure 2 presents plots comparing the behavior of foF 2 measured by two pairs of ionosondes and the respective VTEC values estimated using two GIM products (CODG and UQRG) for the ionosonde positions.…”

Section: Methodsmentioning

confidence: 99%

“…In the method proposed, we consider that the behavior of foF 2 is correlated to VTEC, assuming constant slab thickness. One limitation of this assumption is related to regions with significantly distinct ionosphere (and consequently different slab thickness in the two regions considered) [19][20][21]. To analyze this influence, in the first assessment section, we also estimated other ionospheric indicators.…”

Section: Methodsmentioning

confidence: 99%

“…Leitinger et al [20] present an analysis of the relations between VTEC and foF 2 at regular and extreme behaviors, highlighting the influence of slab thickness for estimating VTEC based on foF 2 or vice versa, considering mid-latitudes. Kouris et al [21] present a study of the variation of VTEC and foF 2 values, showing a clear correlation between them, specifically when low/medium solar activity periods are considered. A study correlating VTEC and foF 2 , and using foF 2 to predict VTEC, by means of linear regression, was conducted by Cander [22].…”

Section: Introductionmentioning

confidence: 95%

See 3 more Smart Citations

Assessment of Global Ionospheric Maps Performance by Means of Ionosonde Data

et al. 2020

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This work presents a new method for assessing global ionospheric maps (GIM) using ionosonde data. The method is based on the critical frequency at the F2 layer directly measured by ionosondes to validate VTEC (vertical total electron content) values from GIMs. The analysis considered four different approaches to using foF2. The study was performed over one of the most challenging scenarios, the Brazilian region, considering four ionosondes (combined in six pairs) and thirteen GIM products available at CDDIS (Crustal Dynamics Data Information System). Analysis was conducted using daily, weekly, one year (2015), and four years (2014–2017) of data. Additional information from the ionosphere was estimated to complement the daily analysis, such as slab thickness and shape function peak. Results indicated that slab thickness and shape function peak could be used as alternative indicators of periods and regions where this method could be applied. The weekly analysis indicated the squared frequency ratio with local time correction as the best approach of using foF2, between the ones evaluated. The analysis of one-year data (2015) was performed considering thirteen GIMs, where CODG and UQRG were the two GIMs that presented the best performance. The four-year time series (2014–2017) were analyzed considering these two products. Regional and temporal ionospheric influences could be noticed in the results, with expected larger errors during the solar cycle peak in 2014 and at locations with pairs of ionosondes with the larger distance apart. Therefore, we have confirmed the viability of the developed approach as an assessment method to analyze GIMs quality based on ionosonde data.

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Section: Daily and Weekly Analysismentioning

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

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Assessment of Global Ionospheric Maps Performance by Means of Ionosonde Data

et al. 2020

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“…Because of the time-varying and dispersive characteristics of the ionosphere, the working parameters for the HF communication systems and satellite communications must be adaptively adjusted according to the current state of the ionosphere [1]. During the ionospheric disturbance, the variations of the foF2 play a significant impact on wireless communication [2,3], which may lead to the degradation or interruption of the information quality such as communication, navigation, measurement, and remote sensing [4,5]. Therefore, forecasting foF2 has become an important concern in ionospheric studies and applications to provide effective support and decision for high-frequency radars and shortwave communication systems.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Ionospheric foF2 Based on Deep Learning Method

Zhou

Tang

et al. 2021

Remote Sensing

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In this paper, a deep learning long-short-term memory (LSTM) method is applied to the forecasting of the critical frequency of the ionosphere F2 layer (foF2). Hourly values of foF2 from 10 ionospheric stations in China and Australia (based on availability) from 2006 to 2019 are used for training and verifying. While 2015 and 2019 are exclusive for verifying the forecasting accuracy. The inputs of the LSTM model are sequential data for the previous values, which include local time (LT), day number, solar zenith angle, the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, geographic coordinates, neutral winds, and the observed value of foF2 at the previous moment. To evaluate the forecasting ability of the deep learning LSTM model, two different neural network forecasting models: a back-propagation neural network (BPNN) and a genetic algorithm optimized backpropagation neural network (GABP) were established for comparative analysis. The foF2 parameters were forecasted under geomagnetic quiet and geomagnetic disturbed conditions during solar activity maximum (2015) and minimum (2019), respectively. The forecasting results of these models are compared with those of the international reference ionosphere model (IRI2016) and the measurements. The diurnal and seasonal variations of foF2 for the 4 models were compared and analyzed from 8 selected verification stations. The forecasting results reveal that the deep learning LSTM model presents the optimal performance of all models in forecasting the time series of foF2, while the IRI2016 model has the poorest forecasting performance, and the BPNN model and GABP model are between two of them.

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Vertical structure of medium‐scale traveling ionospheric disturbances

Ssessanga

Kim

2015

Geophysical Research Letters

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We develop an algorithm of computerized ionospheric tomography (CIT) to infer information on the vertical and horizontal structuring of electron density during nighttime medium‐scale traveling ionospheric disturbances (MSTIDs). To facilitate digital CIT we have adopted total electron contents (TEC) from a dense Global Positioning System (GPS) receiver network, GEONET, which contains more than 1000 receivers. A multiplicative algebraic reconstruction technique was utilized with a calibrated IRI‐2012 model as an initial solution. The reconstructed F2 peak layer varied in altitude with average peak‐to‐peak amplitude of ~52 km. In addition, the F2 peak layer anticorrelated with TEC variations. This feature supports a theory in which nighttime MSTID is composed of oscillating electric fields due to conductivity variations. Moreover, reconstructed TEC variations over two stations were reasonably close to variations directly derived from the measured TEC data set. Our tomographic analysis may thus help understand three‐dimensional structure of MSTIDs in a quantitative way.

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TEC and foF2 variations: preliminary results

Cited by 19 publications

References 2 publications

Assessment of Global Ionospheric Maps Performance by Means of Ionosonde Data

Assessment of Global Ionospheric Maps Performance by Means of Ionosonde Data

Forecasting Ionospheric foF2 Based on Deep Learning Method

Vertical structure of medium‐scale traveling ionospheric disturbances

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