Predicting Impacts of Weather-Driven Urban Disasters in the Current and Future Climate (original) (raw)

IBM Journal of Research and Development

Efficient, resilient, and safe operation of many cities is dependent on the local weather conditions at the scale of their critical infrastructure (electric, communications and water utilities, transportation, etc.) This includes both routine and severe weather events such as tropical storms, tornadoes, snowstorms, damaging winds and hail. For example, with precipitation events, local topography and weather influence water runoff and infiltration, which directly affect flooding as well as drinking water quality and availability. The impact of such events creates issues of public safety for both citizens and first responders. Therefore, the availability of highly localized weather model predictions focused on municipal public safety and operations of infrastructure has the potential to mitigate the impact of severe weather. This is especially true if the lead time for the availability of such predictions enables proactive allocation and deployment of resources (people and equipment) to minimize time for restoration of damage from severe events. Typically, information at such a scale is simply not available. Hence, what optimization that is applied to these processes to enable proactive efforts utilizes either historical weather data as a predictor of trends or the results of continental-or regionalscale weather models. Neither source of information is appropriately matched to the temporal or spatial scale of many such operations. While near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature it is only directly suitable for reactive response. The initial step to address this gap is the application of state-of-the-art physical weather models at the spatial scale of the city'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 actionable prediction of weather (business) impacts. In some cases, an intermediate physical model may be required to translate predicted weather into the phenomena that leads to such impacts. We have applied these ideas to several cities with a diversity of impacts and weather concerns. This coupled model methodology has enabled operational prediction of storm impacts on local infrastructure, as well as measurement of the model error associated with such forecasts. We have defined a flexible approach for such one-way coupling that includes an abstraction of the weather forecasting component. We will present the implementation of these urban weather impact predictions and the ongoing challenges they represent. We will then discuss how we can extend this concept to a climate scale in order to evaluate the potential localized impacts of a warming planet and the effectiveness of strategies being used to mitigate such impacts.