The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the
National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble
forecasts before they are merged to form a joint ensemble within the North American Ensemble
Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to
accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP
implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble
forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational
statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are
enhanced significantly. In addition to the operational calibration technique, three other experiments were
designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration
method with short samples, a climate mean bias calibration method, and a bias calibration method using
dependent data. Preliminary results show that the decaying averaging method works well for the first few
days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved
probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System
Research Laboratory’s Physical Sciences Division with and without the climate mean bias correction were
also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows
that the climate mean bias correction can add value, especially for week-2 probability forecasts.