he existence of ozone in the stratosphere is crucial for protecting living things from harmful ultraviolet radiation. However, when ozone moves to the tropospheric region, it acts as a greenhouse gas, contributing to global warming, and can be harmful to living things beings when inhaled. Hence accurate estimation of ozone’s spatiotemporal distribution is important for understanding temperature variations and preparing society for harmful radiation exposure. Therefore, this dissertation aimed to study the spatiotemporal distribution of ozone concentration over Ethiopia based on satellite observations. To address this objective daily total column ozone data of 108 observation points with spatial resolution 1 1 over the study area for the period 2012 – 2020 have been used. The spatial variations of ozone concentration over Ethiopian regions have been analyzed by considering the regions into longitudinal and latitudinal bands separately and investigating the sample mean difference among different bands using multicomparison analysis of variance technique to classify the regions into clusters. The spatial analysis revealed that the distribution of ozone over Ethiopian regions can be classified into three clusters: Southern Cluster (4.5 47.5 E), North–Eastern Cluster (9.5 N to 14.5 N & 41.5 E 47.5 N 8.5 E) and North–Western Cluster (9.5 N 14.5 N & 33.5 E 40.5 E ). This means that the ozone shield over Ethiopia varies from region to region with possible implication on impacts associated to these variabilities including exposure level to ultraviolet radiation and wind dynamics. We also studied the temporal variability of ozone layer over Ethiopia. The analysis has been carried out by decomposing the total column ozone time series observation as a sum of seasonal, trend and serially correlated noise components. This approach allowed us to separate the different components. The maximum total column ozone concentration found at 301DU during summer on August 18, 2013, while 216DU during winter on January 03, 2013, over the study period. The 95% confidence level of the overall mean of total column ozone concentration during the study period was found to be (261.35 2.38)DU. In order to capture the temporal characteristics, we N & 33.5 vi E computed the spectral periodogram for each cluster and obtained a power peak at frequency f = 2.768 millihertz, which indicates that the ozone concentration over the region exhibits an annual cyclic behavior. A truncated Fourier series fit is used to determine the annual seasonal component. The non-parametric Mann-Kendall’s trend test with a 95% confidence level of significant indicated marginally decreasing linear trend over all clusters. The analysis of residuals for each cluster indicated that the residuals are normally distributed with no significant outliers and the model explains 85% ,86% and 79% of the variance in the North-West, North-East and Southern clusters respectively, demonstrating the reliability of the model considered in this study. Tropospheric ozone level has been also modeled using SARIMA and additive Holt– Winters models over the North-Western cluster of Ethiopia using four homogeneous series of more than 13 years of data from ECMWF. We split the data set into training and testing sets. We used the data during the period 2007–2019 for model formulation and parameter estimation and the one year data in 2020 to test model forecasts. More than 60 SARIMA models have been generated for the time series. The best models has been assessed using the Akaike information criterion and Bayesian information criterion. We also applied model evaluation metrics such as root mean square error, mean absolute error and Mean Absolute Percentage Error (MAPE) to compare the accuracy between Seasonal ARIMA and Holt Winters models. Our findings show that the best model for forecasting tropospheric ozone level is ARIMA(2,0,4)(1,1,2)[12] and ARIMA(3,1,0)(2,0,0)[12] for Addis Ababa and Ras Dashen stations respectively. However, for Danakil Depression and Bahir Dar stations Holt–Winters model with a = 0.346, b = 0.023, g = 0.36 and a = 0.302, b = 0.019, g = 0.266 respectively are found to be best models than the SARIMA. Moreover, the maximum MAPE were found to be less than 7.86% in the study & hence all the forecasts are acceptable.