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Aerosols are tiny particles (liquid or solid) suspended in the atmosphere. They play a significant role in climate dynamics directly or indirectly. Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) are significant parameters to study the concentration and size or type of aerosol over an area, respectively. In this article, we utilized three years of AOD and AE parameters derived from moderate resolution imaging spectroradiometer (MODIS) satellite during the period January, 2013 to December, 2015 over Ethiopia. In order to study the spatiotemporal pattern of aerosols, we choose three areas (Debretabour, Gojjam and Addis Ababa) over Ethiopian highlands, which are representative of nonindustrial, agricultural and industrial areas respectively. Further we compare continental aerosols with marine aerosols from Djibouti. Our results clearly depicts the aerosol distribution over Ethiopia is highly variable spatially and temporally. The results indicates that the urban and biomass aerosols are dominate over Addis Ababa, and Gojjam respectively, whereas dust and biomass aerosols are present over Debretabour, while Djibouti is loaded by sea spray aerosols. The seasonal variability of AOD is found to be maximum during the kiremt (summer) and minimum during bega (winter) over all areas (continental and marine). [ABSTRACT FROM AUTHOR]

A composite analysis is used to evaluate the teleconnections between the long term (June-August) rainfall anomalies with east and west phases of quasi-biennial oscillation (QBO) in the stratospheric zonal winds from 1979-2017. Applying the lower equatorial stratospheric zonal wind index in JJA rainfall prediction is based on its tendency to persist for several months after the phase change from easterly to westerly and vice versa. Below normal condition rainfall used to coin drought. This study is important because, the Upper Blue Nile region is one of the most inviting areas for different activities like agriculture and hydroelectric power; therefore, timely prediction of June-August rainfall serves farmers and other concerned sectors. The aim of this analysis is to establish the global signal quasi-biennial oscillation contribution alone at different time lags and its association among Southern Oscillation Index (SOI) for estimation June-August rainfall of the Upper Blue Nile. The rainfall used as predictand while quasi-biennial oscillation & SOI datasets are used as predictors in regression model after testing collinearity of these two independent variables. Performance of regression model and actual value is tested by using statistical techniques: Root Mean Square Error, Mean Absolute Error and bias. The performance is seen reasonably high between actual and estimated values show strong agreement. [ABSTRACT FROM AUTHOR]