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




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The objectiveof this work is to analyze the temporal and spatial variability of the monthly meanaerosol index AI obtained from the Total Ozone Mapping Spectrometer TOMS andOzone Monitoring Instrument OMI in comparison with the available ground observationsin Nigeria during 1984-2013. It also aims at developing a regression model to allowthe 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 andconsistency in the long-term data observations, together with the meteorologicalvariable such as wind speed, visibility, air temperature and relative humidity thatcan be used to characterize the dust activity in Nigeria. The results revealed astrong seasonal pattern of the monthly distribution and variability of absorbingaerosols along a north to south gradient. The monthly mean AI showed higher valuesduring the dry months Harmattan and lower values during the wet months Summerin all zones. From December to February, higher AI values are observed in the southernregion, 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 minimumvalues in September over the north-central, southern and coastal zones, while showingmaximum values of 1.76 TOMS and 2.10 OMI during March in the Sahel. New empiricalalgorithms for predicting missing AI data were proposed using TOMS data and multiplelinear regression, and the model co-efficient was determined. The generated coefficientswere applied to another dataset for cross-validation. The accuracy of the modelwas determined using the coefficient of determination R2 and the rootmean square error RMSE calculated at the 95% confidence level. The AI values forthe missing years were retrieved, plotted and compared with the measured monthlyAI cycle. It is concluded that the meteorological variables can significantly explainthe AI variability and can be used efficiently to predict the missing AI data.

KEYWORDS

Aerosol Index, Nigeria, Relative Humidity, Temperature, Visibility

Cite this paper

Balarabe, M. , Abdullah, K. , Nawawi, M. and Khalil, A. 2016 Monthly Temporal-Spatial Variability and Estimation of Absorbing Aerosol Index Using Ground-Based Meteorological Data in Nigeria. Atmospheric and Climate Sciences, 6, 425-444. doi: 10.4236-acs.2016.63035.





Autor: Mukhtar Balarabe1,2*, Khiruddin Abdullah1, Mohd Nawawi1, Amin Esmail Khalil1

Fuente: http://www.scirp.org/



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