Modeling of diurnal pattern of air temperature (T-a) is essential in the parameterization of turbulent heat fluxes in climate models. However, climate models still simulate the nocturnal stable boundary layer surface Ta with biases across the globe. This paper therefore validated five different diurnal T-a models (Ephrath, Hirotaka, de Wit, Parton and Fourier series models) used for estimating hourly T-a from daily maximum, daily minimum, and daily mean air temperature. It also developed an improved fourier series parameterization approach for T-a using surface layer observations from Nigerian micrometeorological experimental (NIMEX) site. The overall performance of the Ephrath, Hirotaka, de Wit and Parton models revealed a large deviation from the measured data during the early hours of the morning and late hours of the nighttime. The original fourier series model showed better performance for unstable air temperature parameterizations while the stable T-a was strongly overestimated. The performance of the model was improved with the inclusion of the atmospheric cooling rate that accounts for the temperature inversion which occurs during the nocturnal boundary layer condition. The mean bias error and root mean square error in estimated T-a by the modified fourier series model reduced by 4.13 and 3.01 degrees C, respectively during the transition period from dry to wet stable atmospheric conditions. The existing models simulated night time T-a with high biases and could not accurately capture the strong cooling inversion associated with a humid tropical region. The modified Fourier series model that incorporated the night time inversion variables gave the best estimation of the diurnal weather patterns of T-a when compared with other existing models for a tropical environment.
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