The AMS Board on Enterprise Communication set goals and prepared a road map of tasks for enterprise sectors-led by the National Weather Service-to work on together to make uncertainty information integral to hydrometeorological forecasts.
The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is a multiyear engineering research center established by the National Science Foundation for the development of small, inexpensive, low-power radars designed to improve the scanning of the lowest levels (,3 km AGL) of the atmosphere. Instead of sensing autonomously, CASA radars are designed to operate as a network, collectively adapting to the changing needs of end users and the environment; this network approach to scanning is known as distributed collaborative adaptive sensing (DCAS). DCAS optimizes the low-level volume coverage scanning and maximizes the utility of each scanning cycle. A test bed of four prototype CASA radars was deployed in southwestern Oklahoma in 2006 and operated continuously while in DCAS mode from March through June of 2007.This paper analyzes three convective events observed during April-May 2007, during CASA's intense operation period (IOP), with a special focus on evaluating the benefits and weaknesses of CASA radar system deployment and DCAS scanning strategy of detecting and tracking low-level circulations. Data collected from nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) and CASA radars are compared for mesoscyclones, misocyclones, and low-level vortices. Initial results indicate that the dense, overlapping coverage at low levels provided by the CASA radars and the high temporal (60 s) resolution provided by DCAS give forecasters more detailed feature continuity and tracking. Moreover, the CASA system is able to resolve a whole class of circulations-misocyclones-far better than the WSR-88Ds. In fact, many of these are probably missed completely by the WSR-88D. The impacts of this increased detail on severe weather warnings are under investigation. Ongoing efforts include enhancing the DCAS data quality and scanning strategy, improving the DCAS data visualization, and developing a robust infrastructure to better support forecast and warning operations.
Abstract. We present an architecture for a class of systems that perform distributed, collaborative, adaptive sensing (DCAS) of the atmosphere. Since the goal of these DCAS systems is to sense the atmosphere when and where the user needs are greatest, end-users naturally play the central role in determining how system resources (sensor targeting, computation, communication) are deployed. We describe the meteorological command and control components that lie at the heart of our testbed DCAS system, and provide timing measurements of component execution times. We then present a utility-based framework that determines how multiple end-user preferences are combined with policy considerations into utility functions that are used to allocate system resources in a manner that dynamically optimizes overall system performance.We also discuss open challenges in the networking and control of such enduser-driven systems.
Despite considerable interest in the weather enterprise, there is little focused research on the “false alarm effect.” Within the body of research that does exist, findings are mixed. Some studies suggest that the false alarm effect is overstated, while several recent efforts have provided evidence that FAR may be a significant determinate of behavior. This effort contributes to the understanding of FAR through a sociological analysis of public perceptions and behavioral responses to tornadoes. This analysis begins by addressing public definitions of FAR and then provides two statistical models, one focused on perception of FAR and one focused on behavioral response to tornado warnings. The authors’ approach incorporates a number of sociological and other social science concepts as predictors in both of these models. Findings provide a number of important insights. Most notably, it is found that 1) there is a wide degree of variation in public definitions of false alarm, 2) actual county FAR rates do not predict perception of FAR, 3) actual county FAR rates do predict behavioral response, and 4) planning and family characteristics are also influential. Another major contribution is to illustrate the significant complexity associated with analysis of false alarms. Conclusions discuss the limits of this analysis and future direction for this type of research.
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