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| Titel |
A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps |
| VerfasserIn |
Y. Tulunay, E. T. Şenalp, Ş. Öz, L. I. Dorman, E. Tulunay, S. S. Menteş, M. E. Akcan |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 26, no. 12 ; Nr. 26, no. 12 (2008-12-05), S.3945-3954 |
| Datensatznummer |
250016321
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| Publikation (Nr.) |
 copernicus.org/angeo-26-3945-2008.pdf |
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| Zusammenfassung |
| Atmospheric processes are highly nonlinear. A small group at the METU in
Ankara has been working on a fuzzy data driven generic model of nonlinear
processes. The model developed is called the Middle East Technical
University Fuzzy Neural Network Model (METU-FNN-M). The METU-FNN-M consists
of a Fuzzy Inference System (METU-FIS), a data driven Neural Network module
(METU-FNN) of one hidden layer and several neurons, and a mapping module,
which employs the Bezier Surface Mapping technique. In this paper, the
percent cloud coverage (%CC) and cloud top temperatures (CTT) are
forecast one month ahead of time at 96 grid locations. The probable
influence of cosmic rays and sunspot numbers on cloudiness is considered by
using the METU-FNN-M. |
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