In this study, ground-level ozone is modeled using Seasonal Autoregressive Integrated Moving Average (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 the European Center for Medium-Range Weather Forecasts. We split the dataset 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 goodness of fit of these models has been assessed using the Akaike information criterion and Bayesian information criterion. After rigorous assessment, the seasonal ARIMA(2,0,4)(1,1,2)[12], ARIMA(3,1,0)(2,0,0)[12], ARIMA(0,1,2)(0,0,2)[12], and ARIMA(2,0,0)(2,1,0)[12] models have been identified as best predictive models for Addis Ababa, Ras Dashen, Danakil Depression, and Bahir Dar, respectively. We 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. Among the SARIMA and Holt–Winters models, our findings show that the best model for forecasting surface-level ozone 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, the Holt–Winters model with α = 0.346, β = 0.023, γ = 0.36 and α = 0.302, β = 0.019, γ = 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, and hence all the forecasts are acceptable.
We have studied the spatiotemporal characteristics of ozone concentration over Ethiopia using Ozone Mapper and Profiling Suite (OMPS) Satellite measurements. Daily total column ozone measurements of 252 data points with spatial resolution 1 ◦ × 1 ◦ for the study area and its surrounding during the period 2012 – 2020 have been analyzed. We investigated the spatial variation over the region from longitudinal and latitudinal bands separately by assessing existence of mean difference among different bands using multicomparison analysis of variance technique and determined the clusters in the region. For the temporal variability, we employed timeseries analysis and decomposed the ozone concentration series for each class into seasonal, trend and residual components. We have found that the total column ozone concentration has a maximum value of 301DU during summer on August 18, 2013 and a minimum value of 216DU during winter on February 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 . 28 ± 4 . 2)DU . Our spatial data analysis revealed that the spatial distribution of ozone over Ethiopia can be classified into three major regions: Southern Cluster (4 . 5 ◦ N − 8 . 5 ◦ N & 32 . 5 ◦ E − 47 . 5 ◦ E ) , North–Eastern Cluster (9 . 5 ◦ N to 14 . 5 ◦ N & 41 . 5 ◦ E − 47 . 5 ◦ E ) and North–Western Cluster (9 . 5 ◦ N − 14 . 5 ◦ N & 32 . 5 ◦ E − 40 . 5 ◦ E ). We also checked the degree of determination among bands in same cluster to see if the concentration of ozone in one band can be explained by the concentration in a another band for each cluster and confirmed the reliability of the classification. From the timeseries analysis, we made an assessment of spectral periodogram for each cluster and obtained a single Fourier power peak with frequency of f = 0 . 002768 Hz , which indicated that the ozone concentration has an annual cyclic behavior in the region. A truncated Fourier series fit is made to determine the annual seasonal component. The non-parametric Mann-Kendall’s trend test with a 95% confidence level of significant indicated a decreasing linear trend with a depletion rate of 0.77 DU/yr, 0.73 DU/yr, and 0.43 DU/yr over North–Western, North–Eastern & Southern clusters respectively. The analysis of residuals result for each cluster indicated that the standardized residuals are normally distributed and white noises. Hence, the model considered is reliable.
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