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 CASA, the Center for Collaborative Adaptive Sensing of
the Atmosphere, seeks to revolutionize the way we detect, monitor and
predict atmospheric phenomena by creating a distributed collaborative
adaptive sensor network that sample the atmosphere where and when end
user needs are greatest. This system has the potential of having
a profound impact on the society in terms of lives, property and the
economy.
Our goal is to dramatically increase the warning time
and forecast accuracy for tornadoes, flash  floods, land-falling hurricanes, and other airborne
hazards that impact millions of people across the US every single day.
CASA will engineer an entirely new approach based on a dense
network of low-power radars that overcome curvature blockage and
achieve significantly enhanced resolution compared to today's systems.
The system will have a new generation of meteorological software
that allows the radars to focus their beams down onto individual storms
and actually track those events. The CASA concept is referred to as
distributed collaborative adaptive sensing technology, or DCAS.
 The regions of the atmosphere most
critical to our safety are inadequately sampled by today's observing
systems. These regions include the lower third of the troposphere and,
in particular, the atmospheric boundary layer. Potential hazards to
public health and well-being - such as thunderstorms, tornadoes,
snowstorms, and floods - form in these regions. This is also
where lofted radiological, chemical and biological agents are of
potentially great concern. Current observing approaches are based
upon a long-established paradigm of widely-spaced functionally
autonomous sensors that operate independent of phenomena observed.
These systems are fundamentally constrained in spatial resolution and
sensitivity, prevented by Earth's curvature from sampling the critical
lower atmosphere, and are unable to measure thermodynamic states.
Our ERC proposes a revolutionary new paradigm in
which transforming systems of distributed, collaborative, and adaptive
sensing (DCAS) networks are deployed to overcome fundamental
limitations of current approaches. Here, distributed refers
to the use of large numbers of appropriately spaced sensors capable of
high spatial and temporal resolution throughout the entire troposphere.
These systems will operate collaboratively within a dynamic
information technology infrastructure, adapting to changing
conditions in a manner that meets competing end-user needs. These
systems will achieve breakthrough improvements in sensitivity and
resolution leading to significant reductions in tornado false-alarms,
vastly improved precipitation estimates for flood prediction,
fine-scale wind field imaging and thermodynamic state estimation for
use in airborne hazard dispersion prediction and other applications.
 Successful implementation of DCAS
systems will require fundamental breakthroughs consistent with the NSF
Technical Merit Review Criteria. Among these breakthroughs
will be integration and sharing of knowledge across disciplines; design
and fabrication of low-cost, multi-beam, solid-state radars; creation
of a systems-based architecture to organize sensing, computing, and
communications resources; development of two-way end-user interface
that dynamically target system resources; deployment of integrative
test beds to validate assumptions and understand emergent system
behavior; implement cross-linked hierarchical data storage and
processing; and improved understanding of small-scale atmospheric
processes.
 To achieve
these breakthroughs, we have assembled leading engineering and computer
science experts from the University of Massachusetts Amherst. They will
work in partnership with scientists and engineers from the University
of Oklahoma , Colorado State University and the University of Puerto
Rico, Mayagüez, and corporate partners including Raytheon, IBM,
Vaisala and federal and state government agencies to create the Center
for Collaborative Adaptive Sensing of the Atmosphere. We will create
scalable prototype test beds to demonstrate the potential for DCAS to
revolutionize our understanding, detection, and prediction of hazardous
atmospheric phenomena-with end users involved from the outset.
 CASA meets the NSF Broader
Impacts Review Criteria through: comprehensive education and
outreach programs that introduce systems-based engineering to K-12
students via the mandated engineering/technology curriculum in
Massachusetts, and serves as the mechanism for expanding participation
by under-represented groups in engineering and scientific endeavors at
all levels. Further, it will engage first-responders and other
end users through the provision of both technology and training.
CASA will address the observation, prediction and response of weather,
an issue that affects between 10 percent and 30 percent of the U.S.
gross national product.
UPRM will work in collaboration with UMass at Amherst,
Colorado State University and University of Oklahoma in this new
Engineering Research Center. Dr. Sandra Cruz Pol is the PI for
the center at UPRM and the educator and remote sensing coordinator, Dr.
José Colom is the Director and radar systems coordinator, Dr.
Rafael Rodríguez is the antenna design coordinator, Dr.
Walter Díaz is the social science coordinator, and Dr.
Lionel Orama is leading a student led testbed in Puerto Rico.
The NSF award consists of $17M for the initial five
years of a ten year program plus matching funding from the institutions
and industry.
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