Efficient, resilient, and safe operation of an electric utility is dependent on the local weather conditions at the scale of its infrastructure. This sensitivity to weather includes such factors as damage to distribution or transmission systems due to relative extremes in precipitation or wind, determining electricity demand and load, and power generation from renewable facilities. Hence, the availability of highly focused weather predictions has the potential to enable proactive planning for the effect of weather on utility systems. Often, such information is simply unavailable. The initial step to address this gap is the application of state-of-The-Art physical weather models at the spatial scale of the utility's infrastructure, calibrated to avoid this mismatch in predictability. The results of such a model are then coupled to a data-driven stochastic model to represent the weather impacts. The deployment of such methods requires an abstraction of the weather forecasting component to drive the model coupling.