Near-real time foF2 predictions using neural networks

Oyeyemi, E. O.; McKinnell, Lee-Anne; Poole, A. W. V.

doi:10.1016/j.jastp.2006.07.002

Cited by 58 publications

(34 citation statements)

References 23 publications

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“…Neural network (NN) techniques have been applied to various topics in the study of the upper atmosphere. A number of works employ the NN to predict atmospheric parameters and determine the optimum parameters for modeling, such as the temporal and spatial forecasting of the f o F 2 values up to twenty-four hours in advance and near-real time prediction (Tulunay et al, 2000;Oyeyemi et al, 2006), to make operational forecasts of ionospheric variations (Nakamura et al, 2007), the topside ionospheric variability and electron-density modelling (McKinnell and Poole, 2001;Maruyama, 2002), solar proxies pertaining to an empirical model (McKinnell, 2008;Maruyama, 2010), and regional TEC modeling with the NN (Leandro and Santos, 2004;Tulunay et al, 2004b;Maruyama, 2007;Habarulema et al, 2007Habarulema et al, , 2009b; Watthanasangmechai et al, 2010).…”

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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“…Several new modeling techniques with respect to different ionospheric parameters have been proposed. Some studies made the temporal and spatial forecasting of ionospheric foF2 and built the model by using neural network analysis (Kumluca et al, 1999;Oyeyemi et al, 2005Oyeyemi et al, , 2006Oyeyemi, 2009, 2010). Of particular intention is concentrated on modeling the ionospheric parameters such as foE, foF2, hmF2, and M(3000)F2, etc.…”

Section: Introductionmentioning

confidence: 99%

Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis

et al. 2011

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Abstract.A similar-parameters interpolation method and an empirical orthogonal function analysis are used to construct empirical models for the ionospheric foF2 by using the observational data from three ground-based ionosonde stations in Japan which are Wakkanai (Geographic 45.4 • N, 141.7 • E), Kokubunji (Geographic 35.7 • N, 140.1 • E) and Yamagawa (Geographic 31.2 • N, 130.6 • E) during the years of [1971][1972][1973][1974][1975][1976][1977][1978][1979][1980][1981][1982][1983][1984][1985][1986][1987]. The impact of different drivers towards ionospheric foF2 can be well indicated by choosing appropriate proxies. It is shown that the missing data of original foF2 can be optimal refilled using similar-parameters method. The characteristics of base functions and associated coefficients of EOF model are analyzed. The diurnal variation of base functions can reflect the essential nature of ionospheric foF2 while the coefficients represent the long-term alteration tendency. The 1st order EOF coefficient A 1 can reflect the feature of the components with solar cycle variation. A 1 also contains an evident semi-annual variation component as well as a relatively weak annual fluctuation component. Both of which are not so obvious as the solar cycle variation. The 2nd order coefficient A 2 contains mainly annual variation components. The 3rd order coefficient A 3 and 4th order coefficient A 4 contain both annual and semi-annual variation components. The seasonal variation, solar rotation oscillation and the small-scale irregularities are also included in the 4th order coefficient A 4 . The amplitude range and developing tendency of all these coefficients depend on the level of solar activity and geomagnetic activity. The reliability and validity of EOF model are verified by comparison with observational data and with International Reference Ionosphere (IRI). The agreement between observations and EOF model is quite well, indicating that the EOF model can reflect the major changes and the temporal distribution characteristicsCorrespondence to: D.-H. Zhang (zhangdh@pku.edu.cn) of the mid-latitude ionosphere of the Sea of Japan region. The error analysis processes imply that there are seasonal anomaly and semi-annual asymmetry phenomena which are consistent with pre-existing ionosphere theory.

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“…and observed ionospheric parameters (foF2, h'F2, hmF2, etc. ) (Williscroft and Poole, 1996;McKinnell and Poole, 2004;Oyeyemi et al, 2005), short-term forecasting of ionospheric conditions (Altinay et al, 1997;Cander et al, 1998;Kumluca et al, 1999;Wintoft and Cander, 2000;Poole and McKinnell, 2000;Oyeyemi et al, 2006), and long-term trend analyses (Poole and Poole, 2002;Yue et al, 2006). Because of the input-output mapping features of NNs, they could be used to generate reference ionospheric models for possible incorporation into the IRI (McKinnell and Friedrich, 2007).…”

Section: Introductionmentioning

confidence: 99%

Regional reference total electron content model over Japan based on neural network mapping techniques

Maruyama

2007

Ann. Geophys.

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Abstract.A regional reference model of total electron content (TEC) was constructed using data from the GPS Earth Observation Network (GEONET), which consists of more than 1000 Global Positioning System (GPS) satellite receivers distributed over Japan. The data covered almost one solar activity period from April 1997 to June 2007. First, TECs were determined for 32 grid points, expanding from 27 to 45 • N in latitude and from 127 to 145 • E in longitude at 15-min intervals. Secondly, the time-latitude variation averaged over three days was determined by using the surface harmonic functional expansion. The coefficients of the expansion were then modeled by using a neural network technique with input parameters of the season (day of the year) and solar activity (F10.7 index and sunspot number). Thus, two-dimensional TEC maps (time vs. latitude) can be obtained for any given set of solar activity and day of the year.

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Near-real time foF2 predictions using neural networks

Cited by 58 publications

References 23 publications

TEC prediction with neural network for equatorial latitude station in Thailand

TEC prediction with neural network for equatorial latitude station in Thailand

Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis

Regional reference total electron content model over Japan based on neural network mapping techniques

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Near-real time foF2 predictions using neural networks

Cited by 58 publications

References 23 publications

TEC prediction with neural network for equatorial latitude station in Thailand

TEC prediction with neural network for equatorial latitude station in Thailand

Modeling ionospheric &lt;I&gt;fo&lt;/I&gt;F2 by using empirical orthogonal function analysis

Regional reference total electron content model over Japan based on neural network mapping techniques

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Modeling ionospheric <I>fo</I>F2 by using empirical orthogonal function analysis