The Influence of Meteorological Parameters on Atmospheric Visibility over Ikeja, Nigeria

Akpootu, D. O.; Iliyasu, Mukhtar Isah; Mustapha, W.; Aruna, S.; Yusuf, Saher

doi:10.9734/acri/2017/36010

Cited by 6 publications

(14 citation statements)

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“…The skewness and kurtosis tests were studied in this present work. The skewness test 0b c 2 measures the asymmetry of the parameters data around their mean value; it is a measure of symmetry, or more precisely, the lack of symmetry [16]. It informs us about the direction of variation of the dataset [16].…”

Section: Methodsmentioning

confidence: 99%

Estimation of Surface Water Vapour Density and Its Variation with Other Meteorological Parameters Over Owerri, South Eastern, Nigeria

Akpootu¹,

Mustapha²,

Rabiu³

et al. 2019

HYD

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In this paper, the monthly variation of Surface Water Vapour Density (SWVD) with meteorological parameters of monthly average daily mean temperature, relative humidity, surface pressure, cloud cover and sunshine hours during the period of sixteen years (2000-2015) for Owerri (Latitude 5.48°N, Longitude 7.00°E, and 91m above sea level) were investigated. The daily variation of surface water vapour density for the two distinct seasons considering two typical months in each during the period of year 2015 was examined. The results showed fluctuation in the amount of surface water vapour density in each day of the month for the period under investigation. The monthly average daily values indicated that the surface water vapour densities are greater during the raining season than in the dry season. It was observed that the maximum average value of surface water vapour density of 21.002gm-3 occurred in the month of June during the raining season and minimum value of 14.653gm-3 in the month of January during the dry season. The highest value of surface water vapour density was observed on 9 th May, 2015 and the lowest on 14 th January, 2015. The comparison assessment of the developed SWVD based models was carried out using statistical indices of coefficient of determination (R 2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash-Sutcliffe Equation (NSE) and Index of Agreement (IA). The developed multivariate correlation regression model that relates temperature and relative humidity with R 2 =99.9% MBE=0.1259 RMSE=0.1462 MPE=-0.6739 NSE=99.8402% and IA=99.9611% was found more suitable for surface water vapour density estimation with good fitting and therefore can be used for estimating surface water vapour density in the location under investigation and region with similar climatic information. The results of the descriptive statistical analysis revealed that the surface water vapour density, mean temperature, relative humidity, cloud cover and sunshine hours data spread out more to the left of their mean value (negatively skewed), while the surface pressure data spread out more to the right of their mean value (positively skewed). The surface water vapour density data have positive kurtosis which indicates a relatively peaked distribution and possibility of a leptokurtic distribution while the mean temperature, relative humidity, surface pressure, cloud cover and sunshine hours data have negative kurtosis which indicates a relatively flat distribution and possibility of platykurtic distribution.

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Section: Methodsmentioning

confidence: 99%

Estimation of Surface Water Vapour Density and Its Variation with Other Meteorological Parameters Over Owerri, South Eastern, Nigeria

Akpootu¹,

Mustapha²,

Rabiu³

et al. 2019

HYD

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“…The virtual temperature, potential temperature, mean temperature and dew point depression data seem to have a quassi-Gaussian distribution. Skewness of exactly zero is quite not likely for real world data [38]. The dew point temperature and virtual temperature correction data are more divergent away from the normal distribution.…”

Section: Resultsmentioning

confidence: 98%

“…In this paper, the skewness and kurtosis tests were studied. The skewness test (I J ) measures the asymmetry of the parameters data around their mean value; it is a measure of symmetry, or more precisely, the lack of symmetry [38]. It gives information about the direction of variation of the dataset [38].…”

Section: Methodsmentioning

confidence: 99%

“…The skewness test (I J ) measures the asymmetry of the parameters data around their mean value; it is a measure of symmetry, or more precisely, the lack of symmetry [38]. It gives information about the direction of variation of the dataset [38]. If I J = 0, the data have a Gaussian distribution (normal distribution), while I J < 0 indicates that the data are spread out more to the left of the mean value than to its right (negatively skewed), when I J > 0 signifies that data are spread out more to the right than to its left (positively skewed) [39].…”

Section: Methodsmentioning

confidence: 99%

“…The Kurtosis test (M > ) explains the shape of a random variable's probability distribution, that is it characterizes the relative peakedness or flatness of a distribution compared to the normal distribution [38]. It measures the degree of normality of each of the meteorological parameters under investigation [39].…”

Section: Methodsmentioning

confidence: 99%

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Models for Estimating Precipitable Water Vapour and Variation of Dew Point Temperature with Other Parameters at Owerri, South Eastern, Nigeria

Akpootu¹,

Iliyasu²,

Mustapha³

et al. 2019

WROS

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Precipitable water vapour (PWV) is a vital component of the atmosphere and appreciably controls many atmospheric processes. The PWV is not easy to measure with sufficient spatial and time resolution under all weather conditions. In this paper, three precipitable water vapour models; the Smith, Won and Leckner's models were evaluated and compared for Owerri (Latitude 5.48°N, Longitude 7.00°E, and 91 m above sea level) using meteorological parameters of monthly average daily maximum temperature, minimum temperature and relative humidity during the period of sixteen years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). The Leckner's model was found most suitable and therefore recommended for estimating PWV for the location with range between 3.253 and 4.662 cm. The highest PWV occurred in June for Won and Leckner's models while for Smith's model it occurred in September; the lowest PWV occurred in January for all the evaluated models. The result showed that high values of dew point temperature (T dew ), PWV and relative humidity (RH) were observed during the raining season and low values in the dry season; this is an indication that the dew point temperature is a reflection of the PWV and RH. The dew point temperature is an opposite reflection of the virtual temperature (T virtual ), potential temperature (T potential ) and mean temperature (T mean ). The dew point temperature increases and decreases with mean temperature in the months from January to March and in July respectively for the location under investigation. The values of the dew point temperature indicated that the air is stable signifying no development of severe weather condition like thunderstorms. The maximum and minimum virtual temperature correction of 3.3246°C and 2.3371°C occurred in June and January respectively while for the dew point depression, it occurred in the months of January and September with 8.7514°C and 2.1094°C. The descriptive statistical analysis shows that the dew point temperature, potential temperature, mean temperature and virtual temperature correction data spread out more to the left of their mean value (negatively skewed), while the virtual temperature and dew point depression data spread out more to the right of their mean value (positively skewed). The dew point temperature and the virtual temperature correction data have positive kurtosis which indicates a relatively peaked distribution and possibility of a leptokurtic distribution while the virtual temperature, potential temperature, mean temperature and dew point depression data have negative kurtosis which indicates a relatively flat distribution and possibility of platykurtic distribution.

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Empirical Models for Estimating Tropospheric Radio Refractivity Over Osogbo, Nigeria

Akpootu¹,

Rabiu²

2019

TOASCJ

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Background:Estimation of tropospheric radio refractivity is significant in the planning and design of terrestrial communication links.Methods:In this study, the monthly average daily atmospheric pressure, relative humidity and temperature data obtained from the National Aeronautics and Space Administration (NASA) during the period of twenty two years (July 1983 - June 2005) for Osogbo (Latitude 7.470N, Longitude 4.290E, and 302.0 m above sea level) were used to estimate the monthly tropospheric radio refractivity. The monthly average daily global solar radiation with other meteorological parameters was used to developed one, two, three and four variable correlation(s) tropospheric radio refractivity models for the location. The accuracy of the proposed models are validated using statistical indicator of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash - Sutcliffe Equation (NSE) and Index of Agreement (IA).Results:In each case one empirical model was recommended based on their exceptional performances after ranking, except for the two variation correlations with two empirical models. The recommended models were further subjected to ranking from which the three variable correlations model that relates the radio refractivity with the absolute temperature, relative humidity and global solar radiation was found more suitable for estimating tropospheric radio refractivity for Osogbo with R2= 100.0%, MBE = -0.2913 N-units, RMSE = 0.3869 N-units, MPE = 0.0811%, NSE = 99.9999% and IA = 100.00%.Conclusion:The newly developed recommended models (Equations 16c, 17d, 17f, 18d and 19) can be used for estimating daily and monthly values of tropospheric radio refractivity with higher accuracy and has good compliance to highly varying climatic conditions for Osogbo and regions of similar climatic information.

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The Influence of Meteorological Parameters on Atmospheric Visibility over Ikeja, Nigeria

Cited by 6 publications

References 0 publications

Estimation of Surface Water Vapour Density and Its Variation with Other Meteorological Parameters Over Owerri, South Eastern, Nigeria

Estimation of Surface Water Vapour Density and Its Variation with Other Meteorological Parameters Over Owerri, South Eastern, Nigeria

Models for Estimating Precipitable Water Vapour and Variation of Dew Point Temperature with Other Parameters at Owerri, South Eastern, Nigeria

Empirical Models for Estimating Tropospheric Radio Refractivity Over Osogbo, Nigeria

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