A Model Based Systems Engineering Approach for Behavioral Responses to Advanced Quantitative Precipitation Information

Abstract

Atmospheric rivers (AR) contribute a significant portion of the precipitation for the United States’ west coast. Flooding events caused by ARs can create millions of dollars in damages. Identifying probable ARs and early warning of potential flooding events can provide state and local agencies the time to prepare for such events. The Bay Area Advanced Quantitative Precipitation Information (AQPI) system links users with varied meteorological data ranging from weather radar observations, short term forecasts (Nowcast), 12-hour forecasts, and coastal inundation modeling data. The range of data types presents an opportunity for proactive responses by the users; however, it also presents the challenge of ensuring the correct data is available to the appropriate user. Through Model Based Systems Engineering (MBSE) Behavioral Analysis, the AQPI team analyzes the appropriate requirements for each of the different types of AQPI users. MBSE behavioral analysis leads to the development of a system that services the broad user community’s needs while giving each user group a specific interface tailored for them. This engineering approach also allows for the separation of the processing and weather model execution from the user interface. This separation allows for development and advancements in processing without being tied to the user interfaces.