An evaluation of analog-based postprocessing methods across several variables and forecast models

Results show that the analog methods are generally more effective than time series–based methods at reducing the random error component, leading to an overall reduction in root-mean-square error. Details among the methods differ and are elucidated upon in this study. The relative levels of random and systematic error in the raw forecasts determine, to a large extent, the effectiveness of each postprocessing method in reducing forecast errors.

When the errors are dominated by random errors (e.g., where thunderstorms are common), the analog-based methods far outperform the time series–based methods. When the errors are strictly systematic (i.e., a bias), the analog methods lose their advantage over the time series methods. It is shown that slowly evolving systematic errors rarely dominate, so reducing the random error component is most effective at reducing the error magnitude.

The results are shown to be valid for all seasons. The analog methods show similar performance to the operational model output statistics (MOS) while showing greater reduction of random errors at certain lead times.