Statistical Design of Experiments in Numerical Weather Prediction: Emerging Results

Numerical Weather Prediction (NWP) is the science of forecasting weather or climatic conditions based on past and present weather observations using computational methods applied to mathematical representations of the atmospheric physical processes. Temporally, weather forecasts range from a few hours to a several days in the future, while climate forecasts range from several weeks to years (or decades) into the future. Spatially, forecasts can cover small scale, highly resolved “local” weather conditions over small domains to large scale global weather features and climatic patterns.

Statistical design of experiments, a technique applied successfully in other areas to large scale simulation models, shows promise in assisting in a structured exploration of the parameterized processes in NWP codes. In this paper, we present some emerging results of our study of the role parameterizations play in a relevant forecast metric of interest obtained using design of experiment methods.