The operation of any city or county is dependent to a significant degree upon weather conditions and in particular, precipitation. Local typography and weather conditions influence water runoff, which 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 operations and locally operated flood early warning systems has the potential to mitigate the impact of severe precipitation events on citizens and local infrastructure.

To begin to explore the relevance of this idea, we build upon earlier efforts at the IBM Thomas J. Watson Research Center to implement and apply an operational mesoscale prediction system to business problems, dubbed “Deep Thunder”. In particular, we have extended our capabilities to include the Austin-San Antonio, Texas region. The prototype operational system produces two 24-hour forecasts each day covering east-central Texas at 6.3km resolution and the Austin-San Antonio metropolitan areas at 2.1 km. The system was implemented to generate a number of customized visualizations available via a web browser, which are automatically updated for each forecast cycle. All of the prerequisite processing is completed in a little over an hour on relatively modest hardware to enable timely dissemination of model results at reasonable cost.

The forecast products were accessed by the City of Austin Office of Emergency Management staff based in the aforementioned geographic area starting in 2007. We will discuss how those products were used and their quality with respect to specific weather events that affected local water systems as well as other available forecasts. We will also present some results concerning the overall effectiveness of such modelling capabilities and this particular approach for these applications and recommendations for future work.