Monthly Temporal-Spatial Variability and Estimation of Absorbing Aerosol Index Using Ground-Based Meteorological Data in Nigeria

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 R 2 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.