The mission of the National Weather Service is to protect property and lives from the impacts of weather events. NWS does this through a variety of approaches, one of which is providing impact-based decision support services to state and local officials when events occur. IDSS are a suite of tools that NWS uses to provide location-specific information to local officials that goes beyond the information available to the general public. This information allows those officials to make timely and informed decisions to protect the public from the adverse impacts of weather events. Placing an economic value on IDSS, however, is complicated because NWS only disseminates information; local officials and ultimately the public must make decisions based on that information. This creates numerous confounding factors when measuring value.
ERG’s solution was to develop a simulation approach using an agent-based model to cut through the confounding factors and enable development of a value estimate. We focused this valuation on ground transportation (driving) during winter storms and started with a process model that demonstrated how NWS information was disseminated and then used by local officials and the public during storms. To develop the mathematical components, we drew from published literature on the various stages of the model. We combined this into a Monte Carlo simulation environment that allowed us to run the model under different storm intensities, population sizes, and other factors. Based on the model output, we estimated the values of IDSS under various conditions. Our solution for NWS resulted in a valid and repeatable approach to estimate IDSS value.