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Accurate and reliable total cloud cover (TCC) observation is essential for astronomy, renewable energy resource assessment, climate impact studies, and agriculture. In order to improve the spatial coverage for a climatological distribution pattern of TCC observation across different climatic zones in Ghana – West Africa, this paper developed a merged database comprising ground-based manual TCC observation dataset (TCC M ) at 22 tropical synoptic stations and satellite-based TCC dataset retrieved from the NASA Prediction of Worldwide Energy Resource (POWER) climatological archives (TCC N ) spanning 30 years (1983–2013) for each dataset. Firstly, the satellite data was assessed statistically for merging with station data. From the results, it is shown that on the overall, the satellite data (TCC N ) is a good representation of local TCC climatology comparative to station observation by a mean percentage deviation of 7.8 ± 1.7, and indices of agreement between 0.7 and 0.99 ± 0.01, indicating strong zonal and seasonal similarities. Moreover, the best station-by-station similarities are over the northern half, being predominantly Savannah climate areas, while the southernmost half show the weakest similarities. This can be attributed to a complex interplay of coastal ocean-land-atmosphere interactions obstructing satellite sensing. Finally, the gridded merged dataset established that December–February is the lowest TCC season countrywide, whereas June–August is the highest TCC season, more pronounced over the southern half, being predominantly Forest climate type and showing significant non-linearity with atmospheric clarity indices. The results have useful applications for solar energy resource assessment, crop yield models, and provides a framework for development of cloud property and cloud radiative effect assessment for climate related studies.

Development of High Resolution Cloud Cover Climatology Databank Using Merged Manual and Satellite Datasets over Ghana, West Africa