| description abstract | The skill of two global numerical weather prediction models, the National Centers for Environmental Prediction (NCEP) medium-range forecast model and the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model, has been assessed over the Southern Hemisphere extratropics for much of the 1990s. Forecast skill and circulation predictability are calculated in terms of predicted and observed 500-hPa height fields. The skill of both the NCEP and ECMWF models has increased steadily through the decade. The useful forecast range (mean anomaly correlation at least 0.6) extended out to about day 6 during the late 1990s compared to day 5 in the early 1990s. The ECMWF model generally performed best out to the useful forecast limit, but scores were insignificantly different beyond that. ECMWF forecasts show a gradual increase in variance with forecast interval, while NCEP forecasts show a decrease. For both models, the most predictable wintertime circulation pattern, defined by a singular value decomposition analysis, is associated with wave propagation across the South Pacific and southern Atlantic Oceans, the so-called Pacific?South American pattern, analogous to results found for the Northern Hemisphere. At day 10, the predicted amplitude of the leading pattern correlates at 0.6 with the analysis amplitude, while average hemispheric anomaly correlations are less than 0.3. For the leading singular mode pair, the spatial patterns and summary statistics compare closely between models. The spatial pattern of the leading singular mode is very similar in form to the leading analysis EOF from either model. A study of forecast errors reveals that a pattern related to the ?high-latitude mode? or Antarctic oscillation, associated with a zonally symmetric exchange of mass between mid- and high latitudes, is weakly associated with large forecast errors. Large errors tend to be associated with positive height anomalies over the Pole and weak westerlies near 55°S. The more predictable patterns exhibit stronger temporal persistence than do the least predictable. Applications of these results to operational forecasting are discussed. | |
