| Summary: | Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.
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