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.
CityWarn™ will deliver user-defined, dynamically changing alerts through a next-generation communications and networking platform. The platform is linked to a cutting edge radar system that provides high-resolution weather information on an urban scale of streets and neighborhoods. A mobile app delivers user-configured, weather information. Our integrated research will focus on Computing & Sensing, Behavioral Sciences, Engineered Systems and Testbeds. The Computing & Sensing thrust will develop new scalability, security, and functional advances within the communication and networking platform, and integrate high-resolution radar products and user-generated observations from the field. Through cognitive task analysis, usability studies, and live experiments, behavioral science researchers will learn how fieldworkers, such as utility workers, police, firefighters, stormwater personnel, use and share weather information in the context of their tasks and organizational constructs. Our engineered systems work will focus on aggregation and sharing of sensed information sources, automation of warning processes to address data overload problems, and user alert customization. By developing a common platform for use by industry and public sector players, we hope to break down silos between existing warning systems and increase inter-agency coordination and improve response time and quality. The main test bed will be a living lab in the Dallas Fort Worth metroplex where weather data will be disseminated to users during actual severe weather events.
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.
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