TITLE:
Monthly Temporal-Spatial Variability and Estimation of Absorbing Aerosol Index Using Ground-Based Meteorological Data in Nigeria
AUTHORS:
Mukhtar Balarabe, Khiruddin Abdullah, Mohd Nawawi, Amin Esmail Khalil
KEYWORDS:
Aerosol Index, Nigeria, Relative Humidity, Temperature, Visibility
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.6 No.3,
July
8,
2016
ABSTRACT: The objective
of this work is to analyze the temporal and spatial variability of the monthly mean
aerosol index (AI) obtained from the Total Ozone Mapping Spectrometer (TOMS) and
Ozone Monitoring Instrument (OMI) in comparison with the available ground observations
in Nigeria during 1984-2013. It also aims at developing a regression model to allow
the estimation of the values of AI in Nigeria based on the data from ground observations.
TOMS and OMI data are considered and treated separately to provide continuity and
consistency in the long-term data observations, together with the meteorological
variable such as wind speed, visibility, air temperature and relative humidity that
can be used to characterize the dust activity in Nigeria. The results revealed a
strong seasonal pattern of the monthly distribution and variability of absorbing
aerosols along a north to south gradient. The monthly mean AI showed higher values
during the dry months (Harmattan) and lower values during the wet months (Summer)
in all zones. From December to February, higher AI values are observed in the southern
region, decreasing progressively towards the north, while during March-October,
the opposite pattern is observed. The AI showed clear maximum values of 2.06, 1.93,
and 1.87 (TOMS) and 2.32, 2.27 and 2.24 (OMI) in the month of January and minimum
values in September over the north-central, southern and coastal zones, while showing
maximum values of 1.76 (TOMS) and 2.10 (OMI) during March in the Sahel. New empirical
algorithms for predicting missing AI data were proposed using TOMS data and multiple
linear regression, and the model co-efficient was determined. The generated coefficients
were applied to another dataset for cross-validation. The accuracy of the model
was determined using the coefficient of determination R2 and the root
mean square error (RMSE) calculated at the 95% confidence level. The AI values for
the missing years were retrieved, plotted and compared with the measured monthly
AI cycle. It is concluded that the meteorological variables can significantly explain
the AI variability and can be used efficiently to predict the missing AI data.