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There are large temporal variations in the optical properties and types of aerosols. These are due to the effects of environmental and meteorological conditions, industrial and agricultural influences, and other human influences and natural factors in each ecological functional area. Aerosol optical depth AOD and Ångström exponent AE parameters were extracted from the Moderate Resolution Imaging Spectroradiometer MODIS satellite with a 3 km resolution of MOD04_3K and MYD04_3K. The study covers sixteen selected stations in Horn Africa–Ethiopia with neighboring Eritrea, Djibouti, and South Sudan countries clustered into four regions for the periods of 2001–2022 to obtain detailed information on the temporal behaviors and classification of aerosols in each season. The results show that the temporal variability of AOD and AE values is large, with values ranging from 0.00 to 2.10 and 0.67 to 1.23, with minimum values most likely between December 16 and January 07, and maximum values between June 22 and July 24 for both Terra and Aqua in the southeast and northeast clusters, respectively. The lowest AOD and AE values were most common in the Belg (during September–November) and sometimes in Bega (during December–February), while the highest were in the Kiremt season (during June–August). The minimum values were observed at the Kebri Dahar site, and the maximum were at the Erta Ale site, which belongs to Ethiopia. Appropriate AOD and AE thresholds were used to classify the aerosol types into marine (MR), dust (DS), biomass burning (BB), desert dust (DD), urban (UR), and the residual cases were considered mixture (MX) types. The study concludes that marine aerosols were the most abundant at 47.71%, followed by urban aerosols at 28.29%, while desert dusts, enriched at 14.65%, accounted for the third place with free of dust particles. The Mann–Kendall (MK) statistical trend test was applied to the annual time series in all clustered regions and shows an almost positive increasing variability with a slight decrease in the forward series and vice–versa. • The minimum daily AOD and AE were observed in the southeast cluster at the Kebri Dahar site, and the maximum AOD and AE were in the northeast cluster at the Erta Ale site. • The southeast cluster had the lowest AOD and AE values in both instruments, Terra and Aqua, while the northeast cluster had the highest. • The aerosols were at their highest density in the northwest cluster at the Humera site during Bega and at their lowest levels mostly in the northeast cluster at the Juba site in the Kiremt season. • The yearly total forward trend of aerosols was almost increasing, and the reverse was vice versa. • The variations shown in the trend were lowest in the northwest cluster and highest in the northeast cluster. [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]