Bias Correction and Multiensemble in the NAEFS Context or How to Get a "Free Calibration" through a Multiensemble Approach

Previous studies have shown that the raw combination e the combination of the direct output model without any postprocessing procedure) of the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble prediction systems (EPS) improves the probabilistic forecast both in terms of reliability and resolution This combination palliates the lack of reliability of the NCEP EPS because of the too small dispersion of the predicted ensemble and the lack of probabilistic resolution of the MSC EPS Such a multiensemble, called the North American Ensemble Forecast System (NAEFS), especially shows bias reductions and dispersion Improvements that could only come from the combination of different forecast errors It is then legitimate to wonder whether these improvements in terms of biases and dispersions, and by extension the skill improvements, are only due to the balancing between opposite model errors In the NAEFS framework, bias corrections "on the fly," where the bias is updated over time, are applied to the operational EPSs Each model of the EPS components (NCEP/MSC) is individually bias corrected against its own analysis with the same process The bias correction Improves the reliability of each EPS component It also slightly improves the accuracy of the predicted ensembles and thus the probabilistic resolution of the forecasts Once the EPSs are combined, the improvements due to the bias correction are not so obvious, tending to show that the success of the multiensemble method does not only come from the cancellation of different biases This study also shows that the combination of the raw EPS components (NAEFS) is generally better than either the bias corrected NCEP or MSC ensembles