| This study focuses on Nigeria, a region in the tropics bounded between latitude 4°N and 14°N, and longitude 3°E and 14°E. The project investigated, in a geographic information system, the spatial and seasonal variation in wind and solar energy over Nigeria, using the surface meteorology and solar energy (SSE version 6) dataset. These data, derived from satellites and models, were recently released by the United States' National Aeronautical Space Agency (NASA). The data used for the study was collected between 1983 and 2005.;The results from NASA's SSE dataset were used to evaluate previous work done on renewable energy in Nigeria, using conventional data measured from the ground. This study confirmed some observations, made from conventional data, about the wind and solar energy characteristics of Nigeria. For example, the meridional increase in surface insolation towards the semi-arid northern part of the country; the meridional increase in averaged annual precipitation towards the coastal region of the south; and the phenomenon known as the 'little dry season', characterized by an abnormal decrease in precipitation which occurred during the month of August, within a narrow belt at the coastal region, during the peak of the rainy season. Furthermore, the trend in seasonal variations of surface wind, and the predominant directions of the prevailing winds within the study area were similar.;However, this investigation revealed that wind speed in Nigeria increased towards a northeastern direction, as opposed to the northward increase in wind speed recorded with conventional data. Also, the remotely sensed estimates for wind in most of the stations in Nigeria are much less than the ground measured values. According to NASA's SSE data, the range of the wind power density for the country indicated that Nigeria is among those countries that have the lowest wind power density in the world.;Comparing measurements at 35 locations within Nigeria, the study concluded that there was a strong correlation between NASA's SSE dataset, and insolation data obtained from measurements on the ground, thus making remote sensing a reliable method for analyzing solar energy for this region. |
