A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Tulunay, Yurdanur; Şenalp, Erdem Türker; Öz, Şükriye; Дорман, Л. И.; Tulunay, E.; Mentes, Sibel; Akcan, M. E.

doi:10.5194/angeo-26-3945-2008

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Considering the near‐Earth space processes, the authors have employed some data‐driven models in parallel with physical models [e.g., Tulunay , 1991; Altinay et al , 1997; Tulunay et al , 2001; Y. Tulunay et al , 2004a, 2004b; E. Tulunay et al , 2004a, 2004b; Tulunay et al , 2006; Senalp et al , 2006; Tulunay et al , 2008a, 2008b; Senalp et al , 2008; Tulunay et al , 2009]. One such model of the cited works is called the Middle East Technical University Neural Network model (METU‐NN).…”

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confidence: 99%

Performance of IRI‐based ionospheric critical frequency calculations with reference to forecasting

et al. 2011

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Ionospheric critical frequency (foF2) is an important ionospheric parameter in telecommunication. Ionospheric processes are highly nonlinear and time varying. Thus, mathematical modeling based on physical principles is extremely difficult if not impossible. The authors forecast foF2 values by using neural networks and, in parallel, they calculate foF2 values based on the IRI model. The foF2 values were forecast 1 h in advance by using the Middle East Technical University Neural Network model (METU‐NN) and the work was reported previously. Since then, the METU‐NN has been improved. In this paper, 1 h in advance forecast foF2 values and the calculated foF2 values have been compared with the observed values considering the Slough (51.5°N, 0.6°W), Uppsala (59.8°N, 17.6°E), and Rome (41.8°N, 12.5°E) station foF2 data. The authors have considered the models alternative to each other. The performance results of the models are promising. The METU‐NN foF2 forecast errors are smaller than the calculated foF2 errors. The models may be used in parallel employing the METU‐NN as the primary source for the foF2 forecasting.

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