Bias Correction of Short-Range Ensemble Forecasts of Daily Maximum Temperature Using Decaying Average

In this study, we assessed the performance of the decaying average bias correction method in removing the systematic error in daily maximum temperature (dTmax) ensemble forecasts. We applied the technique to a short-range high-resolution limited-area ensemble prediction system of the Korea Meteorological Administration, which shows under-predictive and under-dispersive characteristics for dTmax. The bias correction was applied to the grid of the model using spatial interpolation of the decaying average bias from surrounding reference points. The method was verified by evaluating the accuracy of the ensemble mean, spread-skill relationship, and the performance of the probabilistic forecasts. The results showed that the decaying average technique minimized the systematic error in the ensemble mean and improved the performance of the probabilistic forecasts. The overall mean absolute error of the ensemble mean was lowered from 2.2 to 1.2 ∘C and the root-mean-square error from 2.5 to 1.6 ∘C. The continuous ranked probability score decreased from 1.9 to 1.0 ∘C. The reliability of three dichotomous events also improved and the Brier skill scores increased. However, the bias correction only slightly affected the ensemble spread, and the system remained under-dispersive.