MODELING OF PRIMARY RADIOCLIMATICAL VARIABLES AND THEIR VARIATION WITH RADIO REFRACTIVITY OVER SELECTED LOCATIONS IN NIGERIA

Abstract

The effects of meteorological parameters on radio wave propagation have been the concern of radio engineers. It is therefore necessary to adopt appropriate measure in a particular location to ensure good signals reception by considering the climatic parameters. Meteorological parameters such as temperature, humidity and atmospheric pressure are known to determine radio refractivity which affects the path of radiowave in the atmosphere. Ability to easily measure these meteorological parameters is important in planning radio communication networks. Temperature, relative humidity and pressure vary from one location to another; hence, there is the need to study inter-relationship among these parameters. In this study, five years (2013-2017) reanalysis data comprising of air temperature, relative humidity and pressure were obtained from the archive of European Center for Medium-Range Weather Forecast (ECMWF) at 00:00 LT, 06:00 LT, 12:00 noon and 18:00 LT on daily basis at resolution of 0.25o × 0.25o covering Nigeria. The reanalysis data were obtained for 26 locations based on the four climatic regions of Nigeria in order to derive appropriate model equations that can be used to predict meteorological parameters over Nigeria. The five years’ data were averaged to obtain monthly values over all the locations. Multiple polynomial regression model of order two was used to fit the data to obtain coefficients (bo, b1, …, b6) for predicting the monthly mean parameters over the study locations. The R2, Adjusted R2 and Standard Error (SE) of the estimated values for each of the month were equally obtained while the Mean Absolute Error (MAE) was obtained and compared with the Standard Error. Eight stations were used for validation, two each from the four climatic regions of Nigeria. ArcGIS was used to generate the predictive maps for the twelve months based on Kriging and Inverse Distance Weighting Interpolation methods. The R2 values and root mean square error values of the multiple polynomial regression, kriging and Inverse Distance Weighting (IDW) were compared and multiple polynomial regression model was found to have the highest R2 values. The data were also characterized diurnally and seasonally to establish relationship between primary radioclimatic variable and surface radio refractivity. High values of radio refractivity were observed during the rainy season and low values during the dry season months. The refractivity values during the rainy sason (May) for Sub-Sahelian, Midland, Guinea Savanna, and Coastal are 419.39 N-units, 403.19 N-units, 399.64 N-units, and 397.17 N-units respectively. The model was used to predict meteorological parameters of the selected locations.