###### Abstract
Thus, the need for specific location-based estimation of such effects is self-evident. In order to address this problem, several techniques have been proposed in the current literature. One of the most promising technologies is the Reconfigurable Intelligent Surface (RIS), a key enabler for beyond-5G and -6G wireless communication systems. In recent times, RIS has been widely projected as the critical enabling technology for beyond-5G and -6G wireless networks. RISs pose great potential in improving the performance of wireless communication networks [17; 18]. Passive elements are used in RIS design to mitigate interference in harsh environmental conditions [19]. Figure 1 presents an implementable RIS-assisted wireless communication system. Here, signals are reflected from the RIS to facilitate effective communication between the base station and the mobile terminal [20; 21]. However, real-time control of the reflection amplitude and phase shift of RIS pose enormous computational complexity [22]. Recently, machine learning, deep learning, and federated learning models have been provided to address the limitations of RIS [23; 24; 25]. However, testing and training data pose severe limitations to applying machine learning-based RIS schemes. Figure 1: Implementation of an RIS-Assisted Wireless Communication System. Due to some major propagation impairments in the atmosphere at different frequencies using measurement and simulated data, various attenuation studies have been reported for different regions. These regions comprise the tropical region [14; 15; 16; 26; 27; 28; 29; 30; 31; 32; 33], sub-tropical region [34; 35; 36; 37; 38], temperate regions [39; 40], semi-temperate region [41], and Mediterranean region [42; 43; 44], among others. However, clear, robust attenuation studies are lacking due to significant propagation impairments over the investigated environment, with a tropical wet and dry savanna climate. The Nigeria Communication Commission (NCC) has already concluded plans with the local cellular network providers to deploy 5G radio networks at a 3.5-GHz transmission frequency [45]. The proposed 5G network deployment also necessitated a prognostic evaluation of the total propagation attenuation for accurate link power margin analysis, especially in dense urban environments [46]. To this end, the focus of this contribution is to carry out a detailed prognostic evaluation of the total radio wave propagation losses due to rain, free space, gases, and cloud over the atmosphere at the ultra-high frequency band, leveraging experimental measurements. In particular, the main contributions of this study are outlined as follows:  
* We propose a prognostic-based empirical attenuation model for terrestrial radio frequency communication links in tropical wet and dry savanna climates based on experimental data. * We present a classification of rain rate values based on rainfall depth levels obtained from experimental measurements. * We determine the propagation loss due to attenuation from atmospheric variables obtained from experimental measurements. The remaining part of this paper is organized as follows. Section 2 presents the theoretical framework. Section 3 describes the materials and methods. Section 4 presents the results and discussion. Finally, Section 5 offers a concise conclusion to the study.