Comparisons of IRI TEC predictions with GPS and digisonde measurements at Ebro

Mosert, M.; Gende, Mauricio; Brunini, C.; Ezquer, Rodolfo Gerardo; Altadill, D.

doi:10.1016/j.asr.2006.10.020

Cited by 73 publications

(29 citation statements)

References 15 publications

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“…This provides the opportunity to study the TEC prediction. Moreover, the availability of historic TEC data is important for the development of the IRI model (Mosert, 2007), as well as the NN model which can learn from prior data (Watthanasangmechai et al, 2010). Better representations of the region above the F peak are of critical importance for many investigations that require TEC predictions (Bilitza, 1997).…”

Section: Introductionmentioning

confidence: 99%

TEC prediction with neural network for equatorial latitude station in Thailand

et al. 2012

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This paper describes the neural network (NN) application for the prediction of the total electron content (TEC) over Chumphon, an equatorial latitude station in Thailand. The studied period is based on the available data during the low-solar-activity period from 2005 to 2009. The single hidden layer feed-forward network with a back propagation algorithm is applied in this work. The input space of the NN includes the day number, hour number and sunspot number. An analysis was made by comparing the TEC from the neural network prediction (NN TEC), the TEC from an observation (GPS TEC) and the TEC from the IRI-2007 model (IRI-2007 TEC). To obtain the optimum NN for the TEC prediction, the root-mean-square error (RMSE) is taken into account. In order to measure the effectiveness of the NN, the normalized RMSE of the NN TEC computed from the difference between the NN TEC and the GPS TEC is investigated. The RMSE, and normalized RMSE, comparisons for both the NN model and the IRI-2007 model are described. Even with the constraint of a limited amount of available data, the results show that the proposed NN can predict the GPS TEC quite well over the equatorial latitude station.

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Section: Introductionmentioning

confidence: 99%

TEC prediction with neural network for equatorial latitude station in Thailand

et al. 2012

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“…The model is an empirical one developed using available data from all around the world, and has been widely accepted as a dependable ionospheric model (Bilitza, 2001;Bilitza and Reinisch, 2008). The IRI model is however more accurate in predicting the bottom-side ionosphere than the topside ionosphere since it is derived mostly from ionosonde data, and as such not a very good candidate for topside ionospheric modeling as is evident in the works of Mosert et al (2007) and Kailiang and Jianming (1994). The NeQuick model (Hochegger et al, 2000;Radicella and Leitinger, 2001) is used by some groups for TEC modeling because of its improved performance in predicting the topside ionosphere (Nava et al, 2008).…”

Section: The Iri Modelmentioning

confidence: 95%

IRI-vTEC versus GPS-vTEC for Nigerian SCINDA GPS stations

Okoh

McKinnell

Cilliers

et al. 2015

Advances in Space Research

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“…3 The analysis of the observed GPS TEC values in the close As it is known GPS TEC represents a measurement of TEC up to around 20000 km (height of the GPS satellites), including the ionospheric TEC (bottom and topside) and the plasmaspheric TEC. Nevertheless, previous studies (Belehaki et al, 2003(Belehaki et al, , 2004Mosert et al, 2007) that analyze the behaviour of TEC obtained by GPS observations and the ionospheric TEC obtained with the technique proposed by Huang and Reinisch (2001) show that the seasonal and solar activity variations of GPS TEC follow very well the corresponding ionospheric TEC variations. Figure 4 shows the comparison between the f o F 2 monthly median values for San Martin and the two options (URSI and CCIR) provided by the IRI model (Bilitza and Reinisch, 2008) to predict the density peak f o F 2 during a high solar activity year (2000).…”

Section: Behaviour Of Gps Tecmentioning

confidence: 99%

Variations of f o F 2 and GPS total electron content over the Antarctic sector

et al. 2011

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This paper presents a preliminary analysis of the variations of the critical frequency of the F 2 region ( f o F 2 ) and the total electron content (TEC) derived from Global Positioning System (GPS) data. Hourly f o F 2 values were scaled from ionograms recorded at San Martin (68.1• S, 293.0 • E) and the TEC values were derived from GPS observations at O Higgins (63.3• S, 302.5• E). The database includes measurements obtained under different seasonal and solar activity conditions. The study shows that the daily peak of f o F 2 occurs around local noon in winter and fall, and in spring a secondary peak is observed around midnight. In summer (January) f o F 2 reaches its minimum value around the noon sector while the maximum in the diurnal variation of f o F 2 is located in a time sector close to midnight. This behaviour is observed at low and high solar activity. The semiannual anomaly appears around noon at high and low solar activity and the winter anomaly is not observed. The effect of the solar activity is generally observed in every season. The analysis of the GPS TEC measurements in the same region indicates that the diurnal, seasonal and solar activity variations are similar to those observed in the f o F 2 values. An analysis of the performance of the IRI model to predict f o F 2 is also shown using the two IRI options (URSI and CCIR). The comparisons between the experimental values and the IRI predictions show some discrepancies.

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Comparisons of IRI TEC predictions with GPS and digisonde measurements at Ebro

Cited by 73 publications

References 15 publications

TEC prediction with neural network for equatorial latitude station in Thailand

TEC prediction with neural network for equatorial latitude station in Thailand

IRI-vTEC versus GPS-vTEC for Nigerian SCINDA GPS stations

Variations of f o F 2 and GPS total electron content over the Antarctic sector

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