Comparison of Ensemble-MOS Methods Using GFS Reforecasts

Abstract

Abstract Three recently proposed and promising methods for postprocessing ensemble forecasts based on their historical error characteristics (i.e., ensemble-model output statistics methods) are compared using a multidecadal reforecast dataset. Logistic regressions and nonhomogeneous Gaussian regressions are generally preferred for daily temperature, and for medium-range (6–10 and 8–14 day) temperature and precipitation forecasts. However, the better sharpness of medium-range ensemble-dressing forecasts sometimes yields the best Brier scores even though their calibration is somewhat worse. Using the long (15 or 25 yr) training samples that are available with these reforecasts improves the accuracy and skill of these probabilistic forecasts to levels that are approximately equivalent to gains of 1 day of lead time, relative to using short (1 or 2 yr) training samples.