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.
In this study, the measured monthly average daily global solar radiation and extraterrestrial solar radiation using the generalized 45% and 40% dataset was utilized to estimate the photosynthetically active radiation (PAR) and extraterrestrial photosynthetically active radiation (PAR 0 ) for Akure (Latitude 7.17 0 N, Longitude 5.18 0 E, and 375.0 m above sea level) Ondo State located in South Western, Nigeria. The monthly average daily sunshine hours, maximum and minimum temperature data were used to develop nine (9) new PAR sunshine based models and three (3) PAR temperature based models. The meteorological parameters used in this study covered a period of thirty one years (1980 -2010). The newly developed models were tested using statistical indicators of coefficient of determination (R 2 ), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), ttest, Nash -Sutcliffe Equation (NSE) and Index of Agreement (IA). The PAR sunshine based models that took a quadratic form and the linear logarithmic PAR temperature based models were found more suitable for estimating PAR for the location under study. Comparing the PAR sunshine based and temperature based models indicated that the PAR sunshine based model is more suitable for PAR estimation in Akure. Furthermore, the results showed that the PAR is high during the dry season and low during the rainy season. Based on the measured and estimated PAR models; the minimum values was found in July and August while the maximum values in February, March and November. The descriptive statistical analysis shows that the PAR and all the estimated sunshine based PAR data spread out more to the left of their mean value (negatively skewed). Similarly, they have negative kurtosis which indicates a relatively flat distribution and possibility of platykurtic distribution. The PAR and the PAR logarithmic temperature based model (equation 17a) data spread out more to the left of their mean value (negatively skewed), while the PAR linear exponent and linear temperature based models (equation 17b and 17c) data spread out more to the right of their mean value (positively skewed). The PAR and all the estimated PAR temperature based data have negative kurtosis which indicates a relatively flat distribution and possibility of platykurtic distribution.
In this study, the monthly average minimum and maximum temperature meteorological data obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) during the period of thirty eight years (1979 – 2016) were used to estimate the mean velocity and most probable velocity of atomic Oxygen and Hydrogen for Ilorin. The values of the mean velocity and most probable velocity for these atoms were compared to the value of escape velocity. The results revealed that the highest values of mean velocity and most probable velocity for atomic Oxygen were found to be in the month of March with and respectively and the highest values of mean velocity and most probable velocity for atomic Hydrogen were found to be in the month of March with and respectively. Based on the values of the mean velocity and most probable velocity for atomic Oxygen and Hydrogen obtained during the studied period suggests that these atoms cannot escape the gravitational field as their values are less than the escape velocity .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.