Research on the variation in Ozone concentration contains the analysis of its changing in the time. We will present a detailed discussion about how to apply the seasonal ARIMA (SARIMA) model, also known as seasonal Box-Jenskins approach, to the daily average maximum ozone data extracted from the California Air Resource Board, and based on the fitted model, we will forecast the level of ozone in the years to come. The SARIMA model is parsimonious enough as well as sufficient to describe non-stationary data such as the underlying ozone data. We firstly reduce a stochastic series to a stationary series and applie the MA and AR model to make further illustration of the data; considering the seasonal pattern occurring in the original data, we may as well use the seasonal ARIMA model to adjust the seasonality and improve the results of our forecast. We will examine our model by three methods: the model checking process; comparison with other possible models such as the non-seasonal ARIMA model and the mixed ARMA model, and applying the in-sample prediction concept and then comparing with the actual values. After conducting the three method, we can reach our conclusion that the SARIMA model provides a good description of our data.