Department of Physical Sciences

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https://repository.run.edu.ng/handle/123456789/74

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Browsing Department of Physical Sciences by Subject "Annual variation"

Now showing 1 - 3 of 3
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    Assessment of Five Predictive Models for Solar Radiation in Southwest Nigeria
    (Nigerian Journal of Environment and Health, 2021) Willoughby, Alexander
    This study compares the accuracies of five predictive models for estimating solar radiation amongst other meteorological parameters in Southwest Nigeria. Twenty-one years of monthly averages of six measured meteorological parameters obtained from six stations in southwest Nigeria have been subjected to five mathematical models for prediction purposes. Solar radiation and sunshine hours have been modelled using the sum of two-Gaussians, the sum of two-Lorentzians, Fourier on four harmonics, sine wave and fourth-order polynomial functions. The fitting accuracies of these models were tested using performance indicators; mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), standard error (SE) and the correlation coefficient (R). An evaluation of the models showed that the Gaussian and Lorentzian models are in good agreement with the observed data. However, the Fourier on the fourth harmonics model had the lowest MBE, RMSE and MPE, consequently highest correlation coefficient values, indicating high model accuracy. Thus, the Fourier model has the best correlation with the observed data and is recommended for estimating these variables in the selected locations.
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    Performance Analysis of Temperature Models for Environmental Monitoring in Southwest Nigeria
    (Nigerian Journal of Environment and Health, 2019) Willoughby, Alexander
    Temperature is a major meteorological parameter driving most of the atmospheric processes vis-`a-vis climate change. Therefore, a consistent model is necessary to achieve sustainable development goal 13 (SDG 13) known as climate action. Long-term monthly averages of surface temperature obtained from six southwest states in Nigeria were subjected to five mathematical models, namely the sum of two-Gaussians, the sum of two-Lorentzians, Fourier on four harmonics, Sine wave and Fourth-order polynomial functions. Statistical tools were used to examine the accuracy and fitness of the models. The evaluation showed that the Gaussian and Lorentzian models are good fits of the observed data. Furthermore, the performance indicators such as mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE) recorded the lowest values for Fourier on the fourth harmonic model. Similarly, its correlation coe cient, R, was the highest ranging from 0.95 to 1. Consequently, the Fourier model presented the best correlation with the observed data and hence recommended for predicting the temperature at the selected locations
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    Performance Evaluation of Rainfall Models and Associated Precipitation Oscillations in the Southwestern Climate System of Nigeria
    (Nigerian Journal of Environment and Health, 2020) Willoughby, Alexander
    The study explored the spatio-temporal evolution of rainfall and precipitable water oscillations using data of over two decades. The monthly averages of rainfall and precipitable water were obtained from six locations in the southwestern climatic region of Nigeria. The data were analysed using five mathematical models namely the sum of two-Gaussians, the sum of two-Lorentzian, Fourier on four harmonics, sine wave and fourth-order polynomial functions for prediction purposes. The performances of these models were evaluated using the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), standard error (SE) and the correlation coefficient (R). The performance indicators of the models showed that the Gaussian and Lorentzian models are in good agreement with the observed data. However, the Fourier on fourth harmonics model had the lowest MBE, RMSE and MPE, consequently highest correlation coefficient values, indicating high model accuracy. Thus, the Fourier model has the best correlation with the observed data and is recommended for estimating these variables in the selected locations.