Digital Twin Support in Adaptability of the Advanced Quantitative Precipitation Information System
William Brooks, V. Chandrasekar, Rob Cifelli · Procedia Computer Science · 2025
Climate change presents challenges for municipal, state, and federal governments across the globe. The San Francisco Bay Area is no exception, with water resource managers, reservoir operators, and emergency managers all trying to develop and respond to the emerging scenarios. To help in this matter, NOAA, working with local agencies and Colorado State University, developed the Advanced Quantitative Precipitation Information (AQPI) system. A complex system, AQPI provides users in the Bay Area access to a myriad of data including radar, forecast, and observations covering precipitation, coastal flooding, and streamflow. As more data becomes available with advancements in modeling, the AQPI system must adapt to incorporate the new data and evolve the user interface. By developing a functional Digital Twin of the AQPI system using Model-Based Systems Engineering (MBSE) following the Complex, Large-Scale, Interconnected, Open Sociotechnical (CLIOS) process, the engineering and development teams can evaluate the different options using trade studies. This paper reviews the AQPI Digital Twin identifying the role it plays in adapting the AQPI system.