Research

Research and research-to-operations. Broad research areas to be addressed in the Urban Test Bed project are:

• Urban flooding and hydrology sensing, forecast, decision making and impacts
• Hydrometeor identification, forecast, decision making and impacts
• Low level wind sensing, forecast, decision making and impacts
• Network of Networks demonstration
• Warn-on-forecast research

We are currently identifying impacts-based, end-to-end projects that could be conducted collaboratively with DFW stakeholders and NWS forecasters.  An example project would be to evaluate whether high resolution QPE coupled with urban-scale hydrological models can determine more accurately which roads will flood, and to develop notification strategies for NWS forecasters and emergency managers that impact the driving behavior of the public.  Metrics to be evaluated could include: the reduction in flash-flood warning size, reduction in hazardous driving practices, more efficient use of emergency and public works personnel, and associated cost savings.  In such a project, the driving public, local EMs, NWS forecasters, and transportation managers would collaborate with academic researchers across all of its disciplines to demonstrate this value.

CASA Urban Test Bed Projects featured on NSF Discoveries website: New Radar System Provides Earlier Severe Weather Alerts

PFI-BIC: CityWarn™ - A Smart Hyperlocal, Context Aware, Hazard Notification Service System

Hazard warning systems are service systems that aim to minimize deaths, injuries, property loss, infrastructure destruction, and service or business disruption. They include the sensors, forecasts, networking and communications, public safety personnel and decision-makers, warning information, and those who receive and respond to the warnings. CityWarn™ addresses three important issues for hazard notification service systems: 1) Coordination and sharing. Public safety agencies, private sector firms and the general public, all have their own hazard warning needs, and over the years, sector-specific, and even hazard-specific warning systems have evolved that may not share important information in efficient and useful ways. 2) Data Explosion. There's an explosion of data from all sources, from fine scale meteorological observations and traffic data, to humans reporting weather on social media. This is challenging for decision-makers who must quickly make sense of all of this information; and 3) Smart phone penetration. There is now a proliferation of smart phones, plus a trend toward hyperlocal, user-selected information. Warning systems have the potential to personalize weather warnings in a way that can make warning response more effective. 

HazardSEES Type 2: Next Generation, Resilient Warning Systems for Tornadoes and Flash Floods

This multi-institution NSF funded project will design, develop, demonstrate and evaluate next-generation, resilient warning systems for rapid-onset hazards, such as tornadoes and flash floods. Using high spatio-temporal observations and short-term forecasts of lower-atmospheric conditions, this system will deliver user-centric, context-aware forecasts and warnings resulting in significant improvements in public response. Warning systems help mitigate the negative socio-economic impacts of natural hazards such as floods and tornados, which over the past three years caused 2303 injuries, 297 deaths and $8.5 billion in property and crop damages across the United States. As technology and its application evolve, and as our understanding of complex interactions among natural hazards, technology and human behavior improves, warnings must also evolve and change. This effort will develop a systems-level framework and underlying technology, firmly grounded in an understanding of human behavioral response, to leverage these changes to better meet the ultimate goal of improving safety for both people and property.

The intellectual merit of this effort centers upon development of new techniques for real-time, high-resolution nowcasts for rain and wind; creation of a new context-sensitive communication architecture for efficiently disseminating user-specific information tailored to various population segments; development and testing of new measures of human behavioral response; creation of a deeper of understanding of myriad influences on public response to weather hazards and warnings; and identification of new ways to link the social and technical components of the warning system. Research results will be integrated into the warning system based on time, space and risk considerations and demonstrated via prototyping next generation warning system concepts. Live warning system experiments in the Dallas Fort Worth Metroplex will provide a unique opportunity to conduct empirical research, validate new technology and theoretical concepts. During severe weather events, high-resolution radar products will be disseminated to NWS forecasters who in turn will disseminate experimental geo-targeted, context-aware real-time warnings to individuals via mobile phones equipped with an app that logs information-seeking activity, communications, location, and movement, and enables post event surveys.

PFI-AIR: CASA Warning System Innovation Institute 

 This NSF Accelerating Innovation Research (AIR) project resulted in an innovation ecosystem between/among public-private partners to provide weather warning systems, technologies, and processes. Assessing the guidelines for optimal scale of rainfall-runoff and employing high resolution analyses of the current state of the atmosphere helped enable pinpointing areas where storm development occured. Additionally, social science methods were used to evaluate human response to flood warnings with recommended warning strategies.

EAGER: Ultra High-speed Bandwidth for Performance Improvements in Radar Networks for Weather and Aircraft Surveillance 

This NSF project demonstrated the benefits of connecting radars to ultra high-speed networks to improve hazardous weather warning and response and the identification and tracking of small, low-flying aircraft by developing new detection algorithms that operate directly on uncompressed, high-bandwidth radar data. Although radar networks are a critical part of the nation's infrastructure for weather observation and aircraft surveillance, today's best-effort Internet is used to transport data from radars to a variety of end users for decision-making. To allow for real-time delivery, the radar data are compressed and important information can be lost during this process. Transmitting uncompressed radar data over ultra high-speed networks could enable advanced, very geographically precise detection and prediction of weather hazards resulting in benefits to public safety and the economy. In addition, the ability to track, small low-flying aircraft is important for drug enforcement and for tracking Unmanned Aircraft Systems used for homeland security and law enforcement that are expected to be prevalent in urban areas in the near future. The project was conducted using a test bed of high resolution, low-cost radar located in the Dallas Fort Worth Metroplex linked to users such as emergency managers and National Weather Service forecasters. To manage the increased demand for processing and networking resources from the usage of high-bandwidth data, techniques related to the usage of on-demand networks (SDN) and compute resources (cloud computing) were studied.