Forecasting of ionospheric critical frequency using neural networks

Altinay, Orkun; Tulunay, E.; Tulunay, Yurdanur

doi:10.1029/97gl01381

Cited by 84 publications

(51 citation statements)

References 4 publications

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“…A new approach for global ionospheric parameters prediction based on neural network (NN) technique is studied by Altinay et al (1997), Wintoft and Cander (1999), Kumluca et al (1999), Tulunay et al (2000), McKinnell and Poole (2001), Poole and Poole (2002), Oyeyemi and Poole (2004), and Oyeyemi et al (2005). Xenos (2002) demonstrated the NN technique for single station modelling and regional mapping of M(3000)F2 in the European region.…”

Section: M Hoque and N Jakowski: A New Global Model For The Ionomentioning

confidence: 99%

A new global model for the ionospheric F2 peak height for radio wave propagation

Hoque

Jakowski

2012

Ann. Geophys.

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Abstract. The F2-layer peak density height hmF2 is one of the most important ionospheric parameters characterizing HF propagation conditions. Therefore, the ability to model and predict the spatial and temporal variations of the peak electron density height is of great use for both ionospheric research and radio frequency planning and operation. For global hmF2 modelling we present a nonlinear model approach with 13 model coefficients and a few empirically fixed parameters. The model approach describes the temporal and spatial dependencies of hmF2 on global scale. For determining the 13 model coefficients, we apply this model approach to a large quantity of global hmF2 observational data obtained from GNSS radio occultation measurements onboard CHAMP, GRACE and COSMIC satellites and data from 69 worldwide ionosonde stations. We have found that the model fits to these input data with the same root mean squared (RMS) and standard deviations of 10 %. In comparison with the electron density NeQuick model, the proposed Neustrelitz global hmF2 model (Neustrelitz Peak Height Model -NPHM) shows percentage RMS deviations of about 13 % and 12 % from the observational data during high and low solar activity conditions, respectively, whereas the corresponding deviations for the NeQuick model are found 18 % and 16 %, respectively.

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Section: M Hoque and N Jakowski: A New Global Model For The Ionomentioning

confidence: 99%

A new global model for the ionospheric F2 peak height for radio wave propagation

Hoque

Jakowski

2012

Ann. Geophys.

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“…There have been many attempts to predict and forecast foF2 using neural networks (Altinay et al, 1997;Cander and Lamming, 1997;Williscroft and Poole, 1996). Neural networks, a kind of artificial intelligence methods, are widely used to describe complicated non-linear input/output relationships.…”

Section: Introductionmentioning

confidence: 99%

Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan

2007

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An operational model was developed for forecasting ionospheric variations and storms at Kokubunji (35 • N, 139• E), 24 hours in advance, by using a neural network. The ionospheric critical frequency (foF2) shows periodic variabilities from days to the solar cycle length and also shows sporadic changes known as ionospheric storms caused by geomagnetic storms (of solar disturbance origin). The neural network was trained for the target parameter of foF2 at each local time and input parameters of solar flux, sunspot number, day of the year, Kindex at Kakioka. The training was conducted using the data obtained for the period from 1960 to 1984. The method was validated for the period from 1985 to 2003. The trained network can be used for daily forecasting ionospheric variations including storms using prompt daily reports of K-index, sunspot number, and solar flux values available on-line.

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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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Forecasting of ionospheric critical frequency using neural networks

Cited by 84 publications

References 4 publications

A new global model for the ionospheric F2 peak height for radio wave propagation

A new global model for the ionospheric F2 peak height for radio wave propagation

Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan

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

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