Abstract
The demand for good quality satellite signals in earth-space communication multimedia services has continuously increased due to the expansion of service providers for various telecommunication companies and internet network providers such as MTN, ETISALAT, GLOBACOM, and AIRTEL. Therefore, a radio engineer needs continuous information on the statistics of rain-induced attenuation to overcome the degradation of satellite signals due to heavy rain downpours. In the present study, an algorithm for the future prediction of rain attenuation based on the Feed Forward Neural Networks (FFNN) prediction model is presented. The seasonal assessment of rain rate and rain attenuation is also investigated for five stations (Okitipupa, Ikole-Ekiti, Ile-Ife, Ogbomosho, and Sagamu) covering the Southwestern region of Nigeria using the Global Precipitation Mission (GPM) satellite observations data. The study revealed that the intensive season exhibited the highest rainfall regime compared to the pre-monsoon, cessation, and dry seasons, which implies maximum rain attenuation during the intensive period of the year. The results of this study would be helpful for radio engineers to plan future link budgets for the provision of adequate signals for link availability.
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