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

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 Techni- cal University Fuzzy Neural Network Model (METU-FNN- M). The METU-FNN-M consists of a Fuzzy Inference Sys- tem (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 Map- ping 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 in- fluence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.