This study investigates the interrelationship between solar irradiance and air temperature across the major climatic zones of Nigeria using data from ten representative meteorological stations over a 15-year period (2001–2015). Monthly observations of solar irradiance and temperature from Calabar, Benin, Oshogbo, Lokoja, Minna, Yola, Yelwa, Jos, Maiduguri, and Sokoto were obtained from the Nigerian Meteorological Agency (NiMET) through WASCAL at FUT Minna. Descriptive analysis showed distinct seasonal peaks in both irradiance and temperature across climatic zones, with coastal and rainforest zones peaking earlier in the year than savannah and Sahelian zones. In addition to correlation analysis, this study employed multiple linear regression models—both station-specific and a combined national model—to better understand the strength and dynamics of the irradiance–temperature relationship while accounting for elevation, latitude, and longitude. Coastal stations (Calabar and Benin) recorded peak irradiance and temperature in February–March, while tropical rainforest stations (Oshogbo and Lokoja) peaked between January and early April. In the Guinea savannah (Minna and Jos), peaks occurred in February to early May. Sudan savannah stations (Yelwa and Yola) peaked in February and May, with Yelwa recording the highest irradiance (40.25 W/m²). Sahel savannah locations (Sokoto and Maiduguri) also peaked in February and May. Across all zones, a direct proportionality between solar irradiance and temperature was observed. Station-level regression revealed significant relationships in all zones, with R² values ranging from 0.33 (Maiduguri) to 0.50 (Jos), and solar irradiance consistently emerging as a statistically significant predictor of temperature (p < 0.0001). A combined centered multiple regression model across all stations achieved an R² of 0.52 and a Root Mean Squared Error (RMSE) of 0.988°C, indicating strong predictive performance. Elevation showed a negative effect on temperature, while latitude and longitude were not statistically significant. The model accurately predicted monthly temperature variations and demonstrated broad applicability across Nigeria’s diverse climatic regions. These findings provide valuable insights for solar energy system design and agricultural thermal forecasting. Future research using longer data records is recommended to capture long-term climate trends.
Chigozie et al. (Wed,) studied this question.