Browsing by Author "Sharafa, S. B."

Now showing 1 - 8 of 8
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    Analysis of Kano Meteorological Data using Time Series Analysis and Empirical Orthogonal Functions
    (FACULTY OF SCIENCE, KANO UNIVERSITY OF SCIENCE AND TECHNOLOGY, WUDIL, 2020) Aliyu, R.; Tijjani, B. I.; Gana, U. M.; Bala, S.; Sharafa, S. B.; Uba, S.; Auwalu, S.; Yerima, S. U.; Abdulkarim, U. Y.; Idris, M.
    In this work, two different statistical techniques were used to analyses the meteorological data of Kano State. The meteorological parameters utilized are daily Solar Radiations, Sunshine Hours, wind speed, Maximum and Minimum Temperatures, Rainfall, Cloud Cover, and Relative Humidity, data spanning for thirty-one years (1980 to 2010). The statistical analysis involves the use of time series analysis and empirical orthogonal functions. In the time series analysis (TS) all the parameters are assumed independent. The second is the Empirical Orthogonal Transformation (EOF) in which the data were analyzed using unrotated and orthogonal transformations and six components were extracted. From component matrices, it is discovered that there are two distinct season as Rainy and Dry seasons. The rainy season has two components comprising heavy rain and light rain, while the dry season comprises of three different types of seasons. The period of heavy rain is around 3.3 months and period of light rain is 1.6 month. These gives a total of 4.9 months for rainy season and 7.1 months for dry season. The values of the eigen values are consistent with what is observed in real-time of seven (7.1) months of dry season and (4.7) months of rainy season. Now combining the two results, it can be said that the TS analysis show that all the parameters have seasonal variation, and the EOF now described the two seasons quantitatively
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    Analysis of Kano Meteorological data using Time Series Analysis and Empirical Orthogonal Functions
    (Faculty of Sciences, Aliko Dangote University of Science and Technology, Wudil, Nigeria, 2020) Aliyu, R.; Tijjani, B. I.; Gana, U. M.; Bala, S.; Sharafa, S. B.; Uba, S.; Auwalu, S.; Yerima, S. U.; Abdulkarim, U. Y.; Muhammad, A.; Idris, M.
    In this work, Ansgrom exponent (α) and curvature (α2), skewness and kurtosis are used to give a clear particles size distribution. This is because some researchers reported the existence of negative curvatures for fine mode aerosols and near zero or positive curvatures are characteristic of size distributions with a dominant coarse-mode or bimodal distribution with coarse-mode aerosols having a significant relative magnitude. The aerosol types used in this work are Sahara and Urban aerosols that are extracted from Optical Properties of Aerosols and Clouds (OPAC4.0). From the results, it is discovered that a and skewness can be used to determine the main dominance particles size distribution in terms of number. The kurtosis can be used to determine the dominant particles in terms of volume. The α2 signifies whether the particle distribution is either monomodal or bimodal. It shows that when α2 is negative, it signifies monomodal distribution while when α2 is positive it signifies bimodal type of size distribution.
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    Computational Analysis of Meteorological Data of Ikeja Lagos State Using the EOF Analysis
    (Faculty of Computing and Information Technology and Faculty of Natural and Applied Science, Sule lamido University, Kafin Hausa, Jigawa State, Nigeria., 2024) Rakiya, A.,; Sharafa, S. B.; Tijjani, B. I.
    In this work, empirical orthogonal transformation (EOF) is used to analyse the meteorological data of Ikeja Lagos State. The meteorological Also, when comparison was made on the wet seasons of all components, with minimal rainfall, high windspeed, zero SSH, and zero Tmax shows that the air is humic at all the three components. For the dry season, it could be observed that the highest month realized is at the first component while the month in component 5 is minimal. Also high and minimal amount of Tmax, Solar Radiation, and SSH were seen on the first and fifths component respectively which, shows that the dry season is very hot at component one and cold at component five When rotation was performed on the data a Comparison was also performed on all components (wet season) and this shows that the months realizes on each are same (1.1) other futures in common are Tmax, rainfall, which shows that the wet season is cold. parameters utilized were monthly Solar radiations, sunshine hours, wind speed, maximum and minimum temperatures, rainfall, cloud cover, and relative humidity, with a data spanning forty-three (43) years (1980 to 2023). The data was analysed using unrotated data and three orthogonal transformations (Varimax, Quatimax, and Equamax) where seven components were extracted. From component matrices, it was discovered that there are two distinct seasons: A rainy or wet and dry. The period of wet determined to be around 7.2 months and for the dry season, as 4.9 months. These values determined are consistent with what is observed in real-time of 7.0 months of wet season and 5.0 months of dry season. This shows that EOF describe the two seasons quantitatively.
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    The application of Angstrom Exponent and measures of shapes in the classification of aerosols size distributions
    (Faculty of Sciences, Aliko Dangote University of Science and Technology, Wudil, Nigeria, 2020) Aliyu, R.; Tijjani, B. I.; Gana, U. M.; Bala, S.; Sharafa, S. B.; Uba, S.; Auwalu, S.; Yerima, S. U.; Abdulkarim, U. Y.; Muhammad, A.; Idris, M.
    In this work, Ansgrom exponent (α) and curvature (α2), skewness and kurtosis are used to give a clear particles size distribution. This is because some researchers reported the existence of negative curvatures for fine mode aerosols and near zero or positive curvatures are characteristic of size distributions with a dominant coarse-mode or bimodal distribution with coarse-mode aerosols having a significant relative magnitude. The aerosol types used in this work are Sahara and Urban aerosols that are extracted from Optical Properties of Aerosols and Clouds (OPAC4.0). From the results, it is discovered that a and skewness can be used to determine the main dominance particles size distribution in terms of number. The kurtosis can be used to determine the dominant particles in terms of volume. The α2 signifies whether the particle distribution is either monomodal or bimodal. It shows that when α2 is negative, it signifies monomodal distribution while when α2 is positive it signifies bimodal type of size distribution.
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    The application of Angstrom Exponent and measures of shapes in the classifications of aerosol size distributions
    (FACULTY OF SCIENCE, KANO UNIVERSITY OF SCIENCE AND TECHNOLOGY, WUDIL, 2020) Aliyu, R.; Tijjani, B. I.; Gana, U. M.; Bala, S.; Sharafa, S. B.; Uba, S.; Auwalu, S.; Yerima, S. U.; Abdulkarim, U. Y.; Muhammad, A.; Idris, M.
    In this work, Ansgrom exponent (a) and curvature (a2), skewness and kurtosis are used to give a clear particles size distribution. This is because some researchers reported the existence of negative curvatures for fine mode aerosols and near zero or positive curvatures are characteristic of size distributions with a dominant coarse-mode or bimodal distribution with coarse-mode aerosols having a significant relative magnitude. The aerosol types used in this work are Sahara and Urban aerosols that are extracted from Optical Properties of Aerosols and Clouds (OPAC4.0). From the results, it is discovered that a and skewness can be used to determine the main dominance particles size distributionin terms of number. The kurtosis can be used to determine the dominant particles in terms of volume. The a2 signifies whether the particle distribution is either monomodal or bimodal. It shows that when a2 is negative, it signifies monomodal distribution while when a2 is positive it signifies bimodal type of size distribution.
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    THE APPLICATIONS OF ANGSTROM EXPONENT AND MEASURES OF SHAPES IN THE CLASSIFICATIONS OF AEROSOLS SIZE DISTRIBUTIONS
    (Faculty of Sciences, Aliko Dangote University of Science and Technology, Wudil, Nigeria, 2020) Aliyu, R.; Tijjani, B. I.; Gana, U. M.; Bala, S.; Sharafa, S. B.; Uba, S.; Auwalu, S.; Yerima, S. U.; Abdulkarim, U. Y.; Muhammad, A.; Idris, M.
    In this work, Ansgrom exponent (α) and curvature (α2), skewness and kurtosis are used to give a clear particles size distribution. This is because some researchers reported the existence of negative curvatures for fine mode aerosols and near zero or positive curvatures are characteristic of size distributions with a dominant coarse-mode or bimodal distribution with coarse-mode aerosols having a significant relative magnitude. The aerosol types used in this work are Sahara and Urban aerosols that are extracted from Optical Properties of Aerosols and Clouds (OPAC4.0). From the results, it is discovered that a and skewness can be used to determine the main dominance particles size distribution in terms of number. The kurtosis can be used to determine the dominant particles in terms of volume. The α2 signifies whether the particle distribution is either monomodal or bimodal. It shows that when α2 is negative, it signifies monomodal distribution while when α2 is positive it signifies bimodal type of size distribution.
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    TREND ANALYSIS OF AOD AND AE IN ILORIN AND CAPO VERDO, WEST AFRICA
    (IKSAD GLOBAL Publishing House, 2021) Sharafa, S. B.; Aliyu, R.; Ibrahim, B. B.
    Analysis of atmospheric aerosols enhances confidence in the evaluation of its effects on both local and regional climate change. To accomplish this over West Africa, it is vital to be familiar with aerosols' temporal characteristics over the area. West Africa has witnessed more frequent and intensified dust storms in the past. Trend analysis of aerosols in this region requires observational data regarding its physical and optical properties. For this, the Statistical Package for the Social Sciences (SPSS) software has been used. Monthly averaged measurements of aerosol optical depth at 550 nm (AOD, τ) and Angstrom exponent at 470 - 660 nm (AE, α) data from Moderate Resolution Imaging Spectroradiometer (MODIS) over Capo Verdo and Ilorin Aerosol Robotic Network (AERONET) stations were analyzed in this study. The satellite dataset collected for a period from 2000 to 2015 were examined in relation to their trends. It was discovered that the Ilorin and Capo Verdo stations were dominated by fine mode and coarse mode particles, respectively. Also, an increasing trend in aerosol loading was noticed in Ilorin while a reduction was noticed in Capo Verdo. It was also deduced that fine mode aerosols are on the increase in the two stations. The analysis for the seasonal variation shows that during the dry season, both aerosol loading and fine mode aerosols are on the increase in both stations. In the rainy season, Ilorin station recorded increasing aerosol loading and an increase in dust pollution. On the other hand in Capo Verdo, a reduction in aerosol loading was noticed while fine mode aerosols are on the increase. Seasonal contribution of aerosols show that dry seasons has the highest pollution recorded in Ilorin while Capo Verdo record the least during the same dry season. The highest contribution of fine mode aerosols in both stations was noticed in the rainy season.
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    Trend and variability of aerosol optical depth at UV wavelength (340 nm) over Ilorin AERONET site in the North Central State of Nigeria
    (School of Science and Technology Education, Federal University of Technology, Minna, Nigeria, 2020-12-03) Ibrahim, B. B.; Sharafa, S. B.; Usman, A.
    Aerosols are small solid or fluid airborne mass, or particles, that remain suspended in the air and move with the motion of the air within broad limits. The extinction of sunlight reaching the ground by a vertical column of atmosphere due to aerosol is often termed as aerosol optical depth. This research work investigates the trend of Aerosol Optical Depth (AOD) at 340 nm and its variability over 13 years (2002-2014) using AERONET data for Ilorin, Nigeria. Daily and monthly mean, maximum, minimum and range of AOD values and their trends were investigated using regression analysis. The result shows that on annual basis, the minimum value of AOD occurred in the year 2008 with a value of 0.096 in August while the maximum value of AOD occurred in 2006 with a value of 3.878. The highest range of AOD also occurred in the year 2006 with a value of 3.663 in March. On year by year monthly average,the minimum value of AOD occurred in year 2008 with a value of 0.293 in August while the maximum occurred in year 2007 with a value of 1.6747 in January. Year 2008 has the highest value of range with a value of 1.3068. The result further shows that the yearly representation of daily mean, maximum, minimum and range behave similarly in trend with AOD rising from November up to March of the following year. There is a drop in AOD in May until a minimum in August of the year before another rise in the value. A quadratic fit best described the plot of daily mean, maximum, minimum and range of AOD with days of the year. AOD shows positive and negative value of monthly trend throughout the study period. No year with similar monthly trend. This analysis will be useful for air quality assessment studies.