Calibration of Ensemble Forecasts Using Reforecast Datasets

The accurate numerical prediction of hazardous airborne plumes requires two important capabilities. First, meteorological conditions at fine spatial scale both at the time of plume release as well as a few hours into the future, and second, quantification of this information in a statistically reliable probabilistic framework. The proposed study will use a combination of fine-scale ensemble re-forecasts, as well as historical surface observations, to achieve the goals of the solicitation. Because no unique method exists for doing so, we examine several and will pursue that which is most accurate, efficient and adaptable to future needs. The first approach, arguably the most simple and computationally efficient, involves using a 20-year history of surface observations to create regression equations that yield statistically reliable probabilistic point forecasts given current conditions. At the other extreme, fine-scale reforecasts will be generated from historical re-analyses and both linear and nonlinear regression approaches applied for calibration. Uniquely, we combine the 20-years of historical observations with this framework such that the final outcome is a combination of dynamical forecast and observation-based statistics. Finally, we expand the nearest-neighbor analog method using ensemble reforecasts alone or in combination with historical surface observations.