Based on the Sea Surface Temperature(SST)data of Shipu Station,time-series model of Seasonal Auto-Regressive Integrated Moving Average(SARIMA)was used to construct a short-term forecasting model for hourly SST.Model parameters were determined according to the periodic of the data and the model forecasting errors.Compared to the model with original hourly input data,the model with interpolated half-hourly input data shows better performance,and the phases of the forecasts have a better consistent with the observations.Using higher temporal resolution of the input data shows no obvious improvement of the accuracy of the 72 h hourly SST forecasts.The results also show that the forecasting error increases with the reduction of the training data length.SARIMA(2,0,2)(2,1,0)25 model with 366-day interpolated half-hourly SST data shows the best forecasting accuracy.The mean absolute errors of 0～24 h,24～48 h and 48～72 h forecasts are 0.176℃,0.350℃ and 0.520℃,the corresponding root mean square error are 0.217℃,0.396℃ and 0.567℃,respectively.
维普
万方数据