Modelling Cloud Cover Climatology over Tropical Climates in Ghana
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Atmosphere
Abstract
Clouds play a crucial role in Earth’s climate system by modulating radiation fluxes via
reflection and scattering, and thus the slightest variation in their spatial coverage significantly alters
the climate response. Until now, due to the sparse distribution of advanced observation stations, large
uncertainties in cloud climatology remain for many regions. Therefore, this paper estimates total
cloud cover (TCC) by using sunshine duration measured in different tropical climates in Ghana. We
used regression tests for each climate zone, coupled with bias correction by cumulative distribution
function (CDF) matching, to develop the estimated TCC dataset from nonlinear empirical equations.
It was found that the estimated percentage TCC, 20.8–84.7 3.5%, compared well with stationobserved
TCC, 21.9–84.4 3.5%, with root mean square errors of 1.08–9.13 1.8% and correlation
coefficients of 0.87–0.99 0.03. Overall, spatiotemporal characteristics were preserved, establishing
that denser clouds tended to prevail mostly over the southern half of the forest-type climate during the
June–September period. Moreover, the model and the observations show a non-normality, indicating
a prevalence of above-average TCC over the study area. The results are useful for weather prediction
and application in meteorology.
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Research Article
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Citation: Dogbey, F.; Asilevi, P.J.; Dzrobi, J.F.; Koffi, H.A.; Klutse, N.A.B. Modelling Cloud Cover Climatology over Tropical Climates in Ghana. Atmosphere 2022, 13, 1265. https://doi.org/10.3390/ atmos13081265