Comparative skill of two analog seasonal temperature prediction systems: objective selection of predictors

Abstract

Analog prediction systems developed in the United States and the former Soviet Union are compared for U.S. seasonal temperature prediction. Of primary interest is the viability of the Russian "optimization' concept for a priori selection of U.S. seasonal analog forecast predictors. The Russian system (called GRAN for "Group Analog') was first run without optimization using the a posteriori selected predictors used in the U.S. system. A version of the U.S. system (without use of antianalogs) that is conceptually very similar to GRAN without optimization was run for comparison in this calibration step. These systems perform in a nearly identical manner when predictor and predictand datasets are the same. Next GRAN forecasts were made using all available predictors and then using only predictors selected via optimization. The results not only show that objective a priori predictor selection by optimization is just as effective (in terms of skill) as subjective a posteriori selection but also suggest it may produce superior results in summer forecasts. -from Authors