On the Use of Autoregressive-Moving Average Processes to Model Meteorological Time Series

Abstract

Statistical problems that may be encountered in fitting autoregressive-moving average (ARMA) processes to meteorological time series are described. Techniques that lead to an increased likelihood of choosing the most appropriate ARMA process to model the data at hand are emphasized. One specific meteorological application of ARMA processes, the modeling of Palmer Drought Index time series for climatic divisions of the United States is considered in detail. It is shown that low-order purely autoregressive processes adequately fit these data.