CASA, a National Science Foundation Engineering Research Center, is creating a new type of weather observation system featuring networks of low-power radars that can adaptively and collaboratively collect high resolution data, meeting the evolving needs of users such as the National Weather Service, emergency managers, private meteorology companies, or researchers. These weather observation systems, called Distributed Collaborative Adaptive Sensing (DCAS) networks, will map and forecast winds, rain, and thermodynamic variables in the lower troposphere, supplying real-time, dynamic data to decision makers. DCAS systems have the potential to improve our ability to observe, understand, detect/forecast, and respond to weather hazards. These systems will be instantiated through the deployment of proof-of-concept test beds in Oklahoma, Texas and Puerto Rico.
In the current system configuration, DCAS networks dynamically adjust their radar beams at 30 second intervals to sense the evolving weather optimally, feed data to customized weather detection and forecast algorithms and disseminate information to users based on their changing needs for data. For example, an NWS forecast office may use 360 degree sweeps of radar data close to the ground to monitor the evolution of a storm to determine whether to issue a tornado warning, while at the same time an emergency manager may need DCAS pinpointing capabilities, to locate precisely the most intense part of a storm for public notification or spotter deployment. These varying user information needs require different radar scanning strategies that, in some cases, exceed the resources of the DCAS networks. Which user information needs should the system serve first?
To address these potential resource conflicts, CASA has created an end user policy algorithm that maintains i) user rules, specifying in what manner and how often different kinds of weather phenomena should be scanned by radars and ii) user weights to establish the relative priority of different user groups in case of resource conflict. The end user policy algorithm interacts with the optimization and resource allocation algorithms that resolve resource conflicts and determine the where the radars scan next.
This paper will describe the development the current version of the end user policy algorithm, a multi disciplinary project the involving engineering, meteorology, sociology, and the user community; how simulations were used to demonstrate system capabilities to different user groups, and plans for the future.