Consensus of Numerical Model Forecasts of Significant Wave Heights

Abstract

Abstract The operational consensus forecast (OCF) scheme uses past performance to bias correct and combine numerical forecasts to produce an improved forecast at locations where recent observations are available. Here, OCF uses past observations and forecasts of significant wave height from five numerical wave models available in real time at the Australian Bureau of Meteorology. In addition to OCF, different adaptive weighting and forecast combination strategies are investigated. At deep-water sites (ocean depth > 25 m), all of the interpolated raw model forecasts outperformed 24-h persistence and, after bias correction, one model was clearly best. Significant improvements over raw model significant wave height forecasts were achieved by bias correction, linear-regression methods, and combination strategies. The best forecasts were obtained from a “composite of composites” in which models with highly correlated errors were combined before being included in the performance-weighted bias-corrected forecast. This technique slightly outperformed the linear-regression-corrected best model. At shallow-water sites (ocean depth < 25 m), all raw models perform poorly relative to the 24-h persistence. The composited, corrected forecasts significantly improved on raw model significant wave height forecasts but only slightly outperformed the 24-h persistence. The raw models generated unrealistically large biases that tended to be amplified with larger observed values of significant wave height.