Evaluation of Technology for the Analysis and Forecasting of Precipitation Using Cyclostationary EOF and Regression Method

Precipitation time series exhibit complex fluctuations and statistical changes. Existing research stops short of a simple feasible model for precipitation forecasting. In this article, the authors investigate forecast variations in South Korea from 1973 to 2021 using cyclostationary empirical orthogonal function (CSEOF) regression methods. First, (EOF) CSEOF analyses are used examine periodic changes data. Then, autoregressive integrated moving average (ARIMA) method is applied principal component (PC) derived EOF analyses. The fifteen leading modes their corresponding PC clearly reflect spatial distribution temporal evolution characteristics Based on forecasts models, EOF–ARIMA composite CSEOF–ARIMA obtain quantitative forecasts. comparison results show that both models have good performance similar accuracy. However, better than under various measurements. Therefore, can be considered an efficient technology representing analytical approach forecasting Korea.