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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 26, issue 12
Ann. Geophys., 26, 3945–3954, 2008
https://doi.org/10.5194/angeo-26-3945-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 26, 3945–3954, 2008
https://doi.org/10.5194/angeo-26-3945-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  05 Dec 2008

05 Dec 2008

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

Y. Tulunay1, E. T. Şenalp2, Ş. Öz3, L. I. Dorman4,5, E. Tulunay2, S. S. Menteş6, and M. E. Akcan6 Y. Tulunay et al.
  • 1ODTÜ/METU, Dept. of Aerospace Eng., Ankara, Turkey
  • 2ODTÜ/METU, Dept. of Electrical and Electronics Eng., Ankara, Turkey
  • 3DMİ, Turkish State Meteorological Service, Ankara, Turkey
  • 4Israel Cosmic Ray and Space Weather Center and Emilio Segre' Observatory affiliated to Tel Aviv University, Technion and Israel Space Agency, Tel Aviv, Israel
  • 5Russian Academy of Science, Cosmic Ray Dept. of IZMIRAN, Russia
  • 6İTÜ, Dept. of Meteorological Eng., İstanbul, Turkey

Abstract. 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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