The site-based Operational Consensus Forecast (OCF) method of Woodcock and Engel (2005) performs statistical corrections of local and overseas model output followed by weighted average consensus. This scheme was shown to produce guidance competitive with subjective official forecasts for daily forecast fields such as maximum and minimum daily air temperature (Woodcock and Engel, 2005). The extension of this scheme to an hourly basis (Engel, 2007) demonstrated consensus forecasts outperforming component models on the basis of aggregate MAE/MSE scores. The site-based OCF research provided valuable insights used for the development of a grid-based system.
Site-based bias corrections, when using spatially coarse model forecasts, were found to compensate for unrepresented spatial scales. This led to the development of a mesoscale grid-based OCF scheme combining both coarse and fine spatial scale model forecasts. The bias correction step was broken into multiple spatial scales using a 5km mesoscale analysis system for pseudo grid-based observations.
This presentation will include an overview of the grid-based OCF scheme along with accompanying fine and coarse-scale verification. Consensus forecasts will be shown to outperform individual component models and the grid-based scheme will be shown to produce site-based forecasts comparable with the site-based OCF scheme.