Estimation of Hourly Clearness Index and Diffuse Fraction Over Coastal and Sahel Regions of Nigeria Using NCEP/NCAR Satellite Data
Adeyemi Babatunde, Omole Opeyemi Vincent
Journal of Energy Research and Reviews · pp. 1–18 · Published 22 Apr 2021
10.9734/jenrr/2021/v7i430195Abstract
In this paper, daily satellite data of global and diffuse irradiances obtained from the archives of National Centre for Environmental Prediction (NCEP) and National Centre for Atmospheric Research (NCAR) covering a period of ten years (2005-2014) have been employed to study clearness index (KT) and diffuse fraction (KD) distributions over six carefully selected meteorological stations in Nigeria. These stations are Port Harcourt, Akure and Enugu in the coastal region; Kano, Maiduguri and Bauchi in the sahelian region. Results have shown that while global irradiance show double peaks in its mean annual daily variation at both regions, diffuse radiation only show this in the sahelian region. In the coastal region, its values are almost uniform throughout the year .In observing the synoptic hourly distribution of clearness index and diffuse fraction, it was found that while clearness index exhibits minima values at hours close to sunrise and sunset, the reverse is the case for diffuse fraction. Also that in the coastal region, clear sky condition is prevalent in Akure and Enugu at both seasons whereas in Port Harcourt, it seldomly occurs. In the sahelian region, on the other hand, clear sky condition is prevalent at both seasons. Using ANOVA method, empirical models were developed for KT. All the models at the stations and regions gave good and significant coefficient of determination R2. During the dry season months, it ranges between 0.98 and 0.99 while during the rainy season, it is 0.89 and 0.95. To further test the efficiencies of the developed models, the Mean Bias Error (MBE) and Root Mean Square Error (RMSE) were computed. Values obtained for both MBE and RMSE for most of the stations are low and positive. These are desirable results for good and significant models. It shows that these models can be used to predict clearness index KTwith high accuracy in the selected stations and proximateones.
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