Brenda Philips

An end-user-responsive sensor network architecture for hazardous weather detection, prediction and response

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 end-user-driven systems.

Evaluation of Distributed Collaborative Adaptive Sensing for Detection of Low-Level Circulations and Implications for Severe Weather Warning Operations

Abstract 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 weak...

Distributed Collaborative Adaptive Sensing for Hazardous Weather Detection, Tracking, and Predicting

A new data-driven approach to atmospheric sensing and detecting/predicting hazardous atmospheric phenomena is presented. Dense networks of small high-resolution radars are deployed with sufficient density to spatially resolve tornadoes and other dangerous storm events and overcome the earth curvature-induced blockage that limits today’s ground-radar networks. A distributed computation infrastructure manages both the scanning of the radar beams and the flow of data processing by dynamically optimizing system resources in response to multiple, conflicting end-user needs. In this paper, we provide a high-level overview of a system architecture embodying this new approach towards sensing, detection and prediction. We describe the system’s data rates, and overview various modes in which the system can operate.

Distributed Collaborative Adaptive Sensor networks for remote sensing applications

Enabled by a dense network of Doppler weather radars with overlapping coverage, Distributed Collaborative Adaptive Sensing (DCAS) represents a new paradigm in remote sensing. Rather than each radar periodically sampling its surroundings with sit-and-spin volume coverage patterns as with todays NEXRAD weather radars, DCAS is an end-user driven approach that targets sensitivity when and where the needs of its end-users are greatest. The advantage is that by adaptively allocating sensitivity, higher quality measurements are possible due to the ability to dwell longer in volumes where echoes are weak, sample faster in volumes with rapidly evolving dynamics, and obtain multi-Doppler looks for high accuracy wind field retrieval. This paper describes the multiuser, multi-attribute utilities-based approach being used to coordinate the scanning activities of the weather radars in the first prototype DCAS system being fielded by the National Science Foundation sponsored Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA- ERC).

User Evaluations of Adaptive Scanning Patterns in the CASA Spring Experiment 2007

The Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is creating a new paradigm for weather observation based on low cost, densely spaced networks of X-band radars. These networks adapt their scanning strategy based on the evolving weather and user needs for data. This paper presents the results of an evaluation by National Weather Service forecasters and academic researchers of the dynamically reconfigurable radar scanning patterns that operated in CASAs prototype test bed in southwest Oklahoma in spring 2007. The evaluation demonstrates that a pilot group of users were satisfied overall with CASAs scanning patterns. The evaluation also uncovered needed improvements to the scanning strategy that have been subsequently implemented. Through this iterative cycle of design, implementation, evaluation, we have demonstrated the flexibility of the system architecture and our ability to modify existing and add new capabilities to increase the benefits of CASA radar systems.

Impact of increased spatio-temporal radar data resolution on forecaster wind assessments

This study examines the impact of increased spatio-temporal resolution weather radar data on the judgment accuracy and warning decisions of forecasters. In a static part-task setting, weather forecasters were provided with high resolution radar data in addition to conventional radar data and asked to forecast ground level winds two to five minutes into the future. When given these additional data, subjects significantly increased wind speed assessments, decreased absolute error, increased confidence ratings, and changed the number of affirmative warning decisions.

Coverage comparison of short range radar networks vs. conventional weather radars: Case study in the northwestern United States

The West Coast of Washington and the NE and SW comers of Wyoming are regions of the contiguous United States where NEXRAD coverage is incomplete. One approach to addressing these gaps is to install additional NEXRAD-class radars. Another potential approach is to install small radar networks of the type being investigated in the CASA project. This paper compares these two approaches. We provide a meteorological and user-need assessment of present radar coverage in these regions (based on a recent feasibility study led by J. Brotzge [1]) as well as an objective assessment of the radar-coverage that would be achieved using the large radar and small radar approaches. For this evaluation we consider two classes of radar: long-range radars having similar attributes to the WSR-88D (i.e., 10 cm wavelength, >250 km maximum range, 1 degree beamwidth, −500 kW peak power); and short-range radars having attributes similar to those operating in CASAs Oklahoma prototype network (i.e., 3 cm wavelength, 40 km maximum range, 2 degree beamwidth). We first establish the number of both types of radar that would be needed to provide coverage over a given rectangular ground-domain. Next, we quantify the coverage-versus-altitude for both weather-event detection and precipitation estimation over these regions, considering the blockage caused by both the curved earth and the local terrain.

Distributed Collaborative Adaptive radar network: Preliminary results from the CASA IP1 testbed

Current weather radar surveillance networks are based upon conventional sensing paradigm of widely-separated, standalone sensing systems using long range radars that operate at wavelengths in 5-10 cm range. Such configuration has limited capability to observe close to the surface of the earth because of the earthpsilas curvature but also has poorer resolution at far ranges. The center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is founded on the transforming paradigm of distributed collaborative adaptive sensing (DCAS) networks designed to overcome this fundamental limitation. The Integrated Project 1 (IP1) is a four-node end-to-end system test bed aimed at precipitation and hazardous wind-sensing in southwestern Oklahoma. Some of the early observations of severe thunderstorm from the DCAS network in CASA IP1 test bed are presented in this paper.

A method for investigating real-time distributed weather forecaster-emergency manager interaction

Researchers have conducted few studies of the joint decision-making processes of forecasters and emergency managers during severe weather events. Emergency managers are difficult to study due to lack of standardization and the jurisdictional nature of their work This research describes an operational concept, requirements, design, and implementation of a forecaster, communicator and emergency computer system and work flow that support the study of forecaster-emergency interactions. The experiment supported by the system and workflow assume that forecasters are available to be experiment participants (foregoing their typical jobs responsibilities) while emergency managers must continue to support their job responsibilities while simultaneously considering the experimental protocol. Thus the operational concept imposes limited additional efforts on the part of the emergency managers. Toward that end, we take advantage of communication methods such as Twitter as well as those already used in the field (such as chat). Limitations of this current effort and ideas for future work are also presented.

The Dallas Fort Worth urban remote sensing network

The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) and the North Central Texas Council of Governments (NCTCOG) are embarking on a five-year, project to create the Dallas Fort Worth (DFW) Urban Demonstration Network. The goals of the program are: 1) To develop high-resolution, three-dimensional mapping of the atmospheric conditions, focusing on the boundary layer, to detect and forecast severe wind, tornado, hail, ice, and flash flood hazards. 2) To create impacts-based, neighborhood-scale warnings and forecasts for a range of public and private decision-makers that result in measureable benefit for public safety and the economy. 3) To demonstrate the value of collaborative, adaptive X-band radar networks to existing and future National Weather Service sensors, products, performance metrics, and decision-making; and assess optimal combinations of observing systems. The centerpiece of the Dallas Fort Worth Urban Demonstration Network will be an 8-node, boundary-layer, dual polarized, multi-Doppler X-band CASA radar network. Additional in-situ and remote sensors will enable fusion of observations from all sensors. Data products will include single and multi-radar data, vector wind, Quantitative precipitation estimation, nowcasting, and analysis and numerical weather prediction products. Research and Research to operations in the DFW Urban Demonstration Network will occur in a quasi-operational environment. New technology and products will be integrated into operational platforms for evaluation by a variety of users during real-time weather events. Users include NWS forecasters and emergency managers; users from transportation, utilities, regional airports, arenas, and the media will be added in the near future. In this way, CASAs multidisciplinary team - engineers, computer scientists, social scientists (sociologists, geographer, economist), meteorologists, hydrologists - will conduct “end-to-end” research from sensor observation, to product development and validation linked to end user decision-making, response and value.