ERAD THE SIXTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

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1 The CASA IP1 Test-bed after 5 Years Operation: Accomplishments, Breakthroughs, Challenges and Lessons Learned V. Chandrasekar 1, David McLaughlin 2, Mike Zink 2, Jerry Brotzge 3, Brenda Philips 2, Yanting Wang 1, Sanghun Lim 1, Francesc Junyent 1, Nitin Bharadwaj 1, Eric Lyons 2, David Westbrook 2, Sandra Cruz-Pol 4 1 Colorado State University, Fort Collins, CO University of Massachusetts, Amherst, MA University of Oklahoma, Norman, OK University of Puerto Rico,Mayaguez, PR V. Chandrasekar 1. Introduction Weather Radar has played a critical role in the monitoring and surveillance of weather, because of its capability to observe and collect data over large areas as well as provide frequent updates. In a conventional operational weather radar network, each radar serves a large coverage area extending up to a few hundred kilometers. The radar observations are subject to fundamental limitations in low-level coverage and spatiotemporal resolution as the radar operation range increases (McLaughlin et al, 2009). Reducing this range and networking multiple radars for a large coverage area can mitigate these observational limitations (Junyent and Chandrasekar 2009). Networked radar sensing is a new paradigm compared to the conventional weather radar technology, requiring development of distributed system architecture and automated command and control systems (Chandrasekar and Jayasumana 2001). CASA (Collaborative Adaptive Sensing of the Atmosphere) is an NSF Engineering Research Center dedicated to revolutionizing our ability to observe, understand, predict, and respond to hazardous weather events, especially in the lower atmosphere. The center has pursued an innovative densely networked sensing paradigm, called DCAS (Distributed Collaborative Adaptive Sensing), to overcome the resolution and coverage limitations of traditional weather radars using low-cost, densely networked radar systems (McLaughlin et al. 2009). In order to avoid the resolution degradation with range, as well as the limitation on the lowest observation altitude, due to earth curvature each radar node in a DCAS system is designed to serve a relatively smaller range compared to conventional radars. Distributed refers to the use of a number of small size radars, appropriately spaced to overcome beam blockage and resolution limitations. Collaborative means the radars are capable of performing sensing tasks jointly. When a storm system passes the radar network, all the radars develop information about the spatial movement of the storm to observe the right locations. Adaptive refers to the dynamic manner of the sensing tasks, where all the radars have information on the changing atmospheric conditions to reconfigure them to optimally meet end-user needs. The DCAS system also enables the improvement of temporal resolution, ultimately making it possible to sample a small scale atmospheric events with adequate spatial and temporal resolution. The development of DCAS networks involves multiple technology enhancements. In order to make the whole system cost effective, the cost of individual radar has to be reduced, demanding smaller size and shorter wavelength radar (McLaughlin et al. 2009). Therefore, rain attenuation becomes one of the important design considerations and needs to be mitigated. Each radar must be capable of reconfiguration and a possibly large number of radars need to be commanded and controlled through a reliable architecture. Real-time analysis algorithms will drive the data processing, feature detection, and serve end-users needs, as well as define the observation strategy. The DCAS concept and all its enabling technologies developed by the CASA enterprise are being validated and evaluated in prototype system-level test-beds, integrated with end-users. The first and the main test-bed of this kind, known as IP1 (Integrated Project 1), is deployed in southwestern Oklahoma that spans over part of the Tornado Alley (Brotzge et al. 2005). The development, deployment and operation of the CASA test-bed is part of the evolving engineering process of CASA, where new technologies are integrated and new findings and emerging user requirements are consolidated. Supporting this process, the CASA IP1 test-bed has been in operation for about 5 years tasked by user preferences from forecasters, emergency managers and researchers through all storm seasons since its deployment in In this paper, we summarize the accomplishments and breakthroughs over the 5 years of operation and review the key challenges and lessons we learned. 2. Overview of DCAS and Networked Radar System Logically, the IP1 test-bed can be subdivided into three thematic areas namely, the Networked radar System observing the severe storm systems, operated under a DCAS paradigm, tasked by the end user system. The

2 distributed, network of small-size radars observing the severe storms form the functioning physical layer of the IP1 test-bed. They are part of the system providing the source of sensing data of the storm phenomena. The system additionally needs to be equipped with a CPU to bring spatial and temporal variability information and user preferences to the physical layer. While this part of the System is termed CPU, the networking infrastructure that links the computation facility, forms the distributing physical layer to drive the sensors within the distributed network. More importantly, the whole system has to be an automated network and adaptively adjust its sensing and computational resources according to the changing weather condition and end-user requirements. This makes the sensing paradigm totally different from the current operational system, where each radar is operated in sit-and-spin mode, such as the WSR-88D Next Generation Doppler Radar (NEXRAD) system, sampling the atmosphere according to predefined volume coverage patterns (VCP). There is some element of adaptivity, even in WSR-88D system, where the clear air scans are different from storm scans. Its system update time is around 5 minutes, and is not in synchronization with the evolution of short-lived tornadic events. In contrast, DCAS system scans where the specific scan task is issued keeping up with the events on hand. In the event of tornado breakouts, the relevant radars can be pointed towards a specifically localized hot zone. As a result, the fast movement of a tornado can be sufficiently sampled and tracked. The goal of the adaptive sensing is to sample the atmosphere when and where the user needs are greatest. While this process may appear obvious at the onset, it will be seen later that the user need concept itself could be changed due to the new set of high resolution observations in space and time, making this system evolution a non-linear and evolving process. To operate this distributed network as a DCAS system, a reliable system architecture is essential to orchestrate the system resource and end-user needs (Kurose et al. 2006). Fig.1 illustrates the fundamental system architecture in a DCAS system. It includes networked radar subsystem, distributing subsystem, detecting and predicting subsystem, and end-user warning and response subsystem, formed as a closed-loop sensing and control system. The CPU of the system is powered by a suite of real-time algorithms in SOCC (System Operations Control Center) as the MC&C (Meteorological Command and Control) system (Zink et al. 2010). All the user requirements are consolidated by the MC&C, which operates based on the current observation. Within a short system update time, called a heartbeat, the new scan tasks are issued to the distributed radar nodes for optimized scans, which are determined dynamically by several factors including: (i) detections from data obtained in the present heartbeat, (ii) historical detections from earlier heartbeats, and (iii) end-user requirements. FIG. 1. System architecture of a DCAS system. In this new sensing paradigm, end-users are an integral part of the DCAS control loop and are involved in the development process. User requirements play the central role in determining how the system resource is optimally allocated at any given space-time point. A DCAS system is able to pinpoint on particular regions of the atmosphere and disseminate information for decision-making to multiple end-users, such as emergency managers, forecasters and first responders. Competing end-user requirements need to be consolidated according to policy (Philips 2007). Public response to warnings is an integrated part of the evaluation process with the assessment of sociological impact. Ongoing user feedback on weather products and scanning strategies is incorporated into CASA s interactive research and design process (Philips 2007, Philips 2008, League 2010). The test-bed stimulates multi-disciplinary interaction among system engineers, atmospheric scientists, social scientists, and end-users such as operational forecasters, emergency mangers. Thus the test-bed is a natural environment for practicing multi-disciplinary collaboration. 3. The IP1 Network and its Operation Integrated Project 1 (IP1) is a four-node end-to-end system test bed aimed at observing precipitation and hazardous winds in the tornado alley over southwestern Oklahoma. This system is end-to-end in that end user groups are integrated into this test bed. The IP1 Test Bed covers a 7,000 square km region that receives an average of

3 four tornado warnings and 53 thunderstorm warnings per year. This four-node DCAS system is being operated in conjunction with an end user group comprised of the National Weather Service Forecast Office in Norman, OK, a group of emergency managers who have jurisdictional authority within and upstream of the test bed area, and CASA s researchers themselves. The four radar nodes are installed along Interstate 44, Southwest of Oklahoma City, OK, and are under the coverage of the KFDR and KTLX NEXRAD radar units. The NEXRAD observations afford important dataset for comparison study and cross validation. The physical SOCC is located in the National Weather Center building in Norman, OK. Fig.2 presents the IP1 network layout. The four radar nodes are located in the towns of Chickasha, Rush Springs, Cyril and Lawton, OK, and each radar node is approximately 30 km away from the next unit. Microwave links provide Internet connectivity to the radar node sites with a maximum guaranteed bandwidth of 4 MBps for real-time transfer of radar measurement parameters. The radar nodes can be completely controlled from any remote location in the Internet with authorized access. Based on a client server architecture, the radar user, which can be the MCC algorithms running at the SOCC or any other authorized entity connecting to it, can send high-level commands specifying radar parameters and actions. Positioning controls for a scan task include start and stop angles, scan speeds, increment steps, axis of motion, and time of execution. Arbitrary waveforms can be generated to control the transmitter and receiver settings, and the number of range gates, decimation factor, and passband filter can be set on the data acquisition system (Junyent et al 2010). Attenuation corrected radar data are streamed and distributed over the Internet in real-time. Data are then driven through the system following the DCAS architecture. The MC&C continuously ingests and stores the radar data files received from each radar node, detects the relevant weather features in the individual and merged radar data, and creates a list of candidate scanning tasks associated to the detected features. The candidate scanning tasks are used to generate optimized scan strategies, based on the quality of the scan and its importance to users, that are then fed-back into the radar nodes with a 60 second periodicity. Final products are perused by end-users such as NWS forecasters and local emergency managers, who often study the observations side-by-side with the current operational NEXRAD observations as part of the NOAA Hazardous Weather Test Bed. The live evaluation by the user group is an important part of the IP1 development process. All the radar nodes are kept energized and ready for operation at all times. Intensive operations had been scheduled for each spring storm season, called IP1 Spring Experiments. During these experiments, a multi disciplinary committee plans the daily operation with specific pre-determined emphasis and the CASA team collectively evaluates and analyzes the system for each event. The overarching goal of these experiments was to demonstrate the value-added by a DCAS system relative to the state-of-the-art (sit and spin, long-range, open-loop, non-networked radars) and the potential impact on current severe weather warning processes. The investigation items are articulated in operational documents yet the investigation focus and end-user policy may change along the course. FIG. 2. IP1 weather radar network layout in the southwestern Oklahoma. 4. Highlights of Spring Experiments Multiple Spring Experiments had been conducted from 2007 to 2010, while the first two years were focused on the validation of the test-bed itself. In both 2009 and 2010, tornados occurred in the network and the IP1 system successfully detected and tracked the tornado development with excellent resolutions. In this section, selected observations from these tornado cases are presented to highlight the DCAS capability in sensing small-scale highimpact weather. 4.1 Radar Node Processing Radar nodes form the source of the observations. In a DCAS system multiple unique challenges exist for sensing data acquisition. Operated at lower elevation angles and short ranges, the radar observations are subjected to severe clutter contamination, which can mask the small-scale signature of tornado funnel cloud. Usage of short wavelength

4 imposes extra limitation on the range-doppler ambiguity. At the same time, rain attenuation can be large and attenuation correction is a necessary procedure for assuring the function of detection and quantitative retrievals. Each IP1 radar node is equipped with advanced waveforms and novel real-time signal processing, featuring second-trip echo suppression, Doppler velocity unfolding, high performance clutter filtering and attenuation correction (Baradwaj et al 2010). Fig.3 shows the radar reflectivity and horizontal wind velocity observed by the network, overlaid with streamlines of wind direction that highlight the rotation. It also shows the PPI plot of co-polar correlation coefficient at 1 o elevation from one of the radars. The waveform is generated at dual PRFs with random phase coding. The radial wind velocity can be measured up to 38 m/s. The clutter mitigation is performed using advanced adaptive Gaussian model filter. Dual-polarization technology is adopted to estimate the rain attenuation and compensate them in radar data (Liu et al. 2008). All together, the IP1 radar delivers clean tornado features such as the hook echo, the Doppler couplet, and lower correlation coefficient as seen from Fig.3. Lower correlation coefficient is a dual-polarization signature associated with the tornado touchdown. At the elevation angle as low as 1 o, the IP1 radar observation after clutter filtering clearly captures this fine feature as shown in Fig Vector Doppler Wind Observations A DCAS network also features with highly overlapped coverage among neighboring radar nodes. This enables vector wind observations. The IP1 radar network was operated in a networked sensing mode for dual-doppler wind velocity measurements, to take advantage of the overlapping coverage for multiple-doppler synthesis. Of the four IP1 radar nodes, multiple candidate pairs exist for most of the IP1 coverage. The system resource is optimized to issue the next scan task for measuring wind vectors of the detected features. The best dual-doppler observation pair is evaluated in real time during each system heart beat, which computes the best viewing angles for dual-doppler synthesis as well as satisfies range and elevation constraints (Wang et al. 2008). Two-dimensional horizontal wind fields have been operationally generated in real-time during the weather events. This horizontal wind product was sent to the forecasters and emergency managers to peruse for operational evaluation. The retrieved wind velocity can be further used to improve the kinematical analysis of severe storms. The MC&C system is equipped for dual- Doppler observations, with a specific dual-doppler task routinely conducted. FIG. 3. IP1 radar observations of (a) attenuation corrected radar reflectivity, (b) network retrieved horizontal wind velocity, and (c) co-polar correlation coefficient from KSAO at 1 o elevation, for a tornado case on May 14, Quantitative Precipitation Estimation Quantitative precipitation estimation (QPE) is an important application of weather radar systems and a means to forecast flooding risk. It is well known that rainfall estimation is a very challenging task. Under the DCAS paradigm, the radar observations at high spatiotemporal resolutions will improve the accuracy of rainfall estimation and eventually flood warnings. The key investigations are: (1) whether the X-band radar network, with each node covering a small region, can serve reliable QPE products; (2) how much improvement can be achieved from the high resolution, low-level dataset. In the IP1 network, multiyear rainfall events since 2007 were analyzed to investigate QPE performance. IP1 rainfall estimates for 29 storm events from 3 years were compared to the ground in-situ measurements from ARS s Little Washita gauge network (Wang and Chandrasekar 2010). The cross-comparison was performed over each rain gauge station for each storm event. This multiyear study reveals almost negligible estimation bias and small standard errors. The NSE (normalized standard error) for instantaneous rainfall rate is 42.41% and the corresponding error for hourly rainfall is 15.78%. These results are more than a factor of two improvement compared to the same set of studies conducted over the same basin by KOUN radar for hourly rainfall accumulations ( Ryzhkov et al 2005). The excellent performance of the IP1 QPE demonstrates the benefits from

5 measurements close to the ground and the networked sensing operation. The study also proves the reliability of X- band weather radar system achieved through a dense network deployment for QPE. 4.4 Networked Reflectivity Retrieval Dual-polarization based mitigation is well established attenuation correction technique. In a DCAS system, an alternate technique was invented to take advantage of the observation diversity inside the network. Its implementation will provide the correction mechanism in case of single polarization radar network. Conventionally, all multiple views of a common radar volume are merged as a mosaic and processing is done with respect to each radar, using a radar-centered coordinate system. Using radar observations collected from different directions, a fundamental shift can be made where the coordinate system is moved from the radar to the observing volume and the attenuated observations through multiple paths are used simultaneously to retrieve the intrinsic reflectivity (Chandrasekar and Lim 2008). The algorithm was tested for real time implementation successfully 4.5 MC&C System The MC&C system is closely evaluated during the experiments to verify if the overall architecture performs to the DCAS closed-loop control. A number of benchmarking and verification tests were performed to quantitatively evaluate the behavior of the MC&C during observed storm events. The visualization of DCAS scan specifications are provided online along with the IP1 radar observations to researchers and end-users. They are archived for the important cases for further analysis and interdisciplinary discussion. The CASA team from all different disciplines collaboratively analyzes the performance of the IP1 system through operational debriefings. The key investigation includes whether the MC&C was able to satisfy the end-user rules, whether the closed-loop control was executed in real-time, and how the system dealt with resource contention. The statistics of optimization time, task timing, and resource conflicts form an important baseline for the specification of end-user policy and new sensing functionality. 4.6 Nowcasting and Scan strategy A nowcasting system was developed over an innovative space-time model and spectral domain solver, named as Dynamic Adaptive Radar Tracking System (DARTS) (Ruzanski et al. 2009). The DARTS was implemented to provide short-time nowcasting of severe weather events up to 20-minute lead time. During earlier experiment, the DCAS network was optimized based on the observations from past scans. This latency was found to be detrimental during fast-moving weather systems and was significant enough that the automated scans sometimes cut off the leading edges of moving storms. To correct this, DARTS based nowcasting was introduced into the closed-loop to generate fields of predicted reflectivity. This gave a better estimation of storm locations for scan optimization. As a result, the DCAS scans can capture the leading edge of a moving storm. 4.7 Tracking Tornadoes Down Streets The vector wind product serves as a critical piece of additional information to forecasters and emergency managers. Especially, the high-resolution wind field, acquired close to the ground with the DCAS paradigm, will improve the detection of tornado touchdowns and the tracking of the rotation centers. A confirmed EF-2 tornado case observed by the IP1 network on the evening of May 13, 2009 (CDT) clearly demonstrates this capability. The storm entered the network from the north and later on spawned a tornado that affected the towns of Gracemont and Anadarko, Oklahoma during the evening. The CASA system captured this event from the beginning and tasked KCYR and KSAO to perform Dual-Doppler optimized wind scan. By 0200 UTC, a strong low-level circulation had formed and by 0215 UTC low-level rotation was observed in the dual-doppler region of KSAO and KCYR, about 10 km north to Anadarko. A damage survey on the following days confirmed an EF2 tornado on the ground approximately between 0220 and 0245 UTC. The hook echo and wind rotation was clearly seen from the real-time display. The wind velocity was synthesized from 250 m AGL and up, helping the weather service issue the tornado warning and confirm the tornado touchdown. After the event, the vorticity was computed from the horizontal wind fields and its peaks were used to locate the damage path. As shown in Fig.4, the tornado path tracked by the IP1 network matches very well the damage path from the ground survey, which spanned over a 12-km swath during 18 minutes. This evaluation proves the capability of DCAS observations for tracking tornadoes down to the street, with higher resolution and lower level coverage, and the potential to improve the warnings.

6 FIG. 4. The IP1 estimated tornado track (marked in ), in comparison to the surveyed damage path (marked by flags) for the Anadarko tornado. 4.8 End-user Evaluation National Weather Service (NWS) forecasters reviewed archived IP1 cases to measure the impact of CASA s high resolution, lower troposphere data on their wind predictions. Severe thunderstorm warning will be issued to alert the public when NWS forecasters expect wind speeds to exceed 50 kts (58 mph). They compared the cases with NEXRAD radar data only (the current technology) and the cases with both CASA and NEXRAD data against the measurements from ground-based sensors. The results show that forecasters using CASA and NEXRAD data made wind assessments that were 30% more accurate in terms of mean absolute error than assessments made with NEXRAD data only (Rude et al 2008). The preliminary evaluation demonstrates the promising impact on the warning process using CASA data. With the addition of CASA data, forecasters said they would issue warnings more often for the cases where actual wind speeds were close to or above the severe threshold. Equally important, forecasters chose not to issue warnings for cases where actual wind speeds were under severe limits. 5. Summary The high-resolution observations, post-event case studies, and fundamental multi-disciplinary research during the 5 years operation in the IP-1 test bed demonstrate the viability of CASA technology and its added value in hazardous weather detection and warning system. The 5 years operation illustrates the key attributes of the overarching DCAS concept such as: (1) that a dense network of short-range radars can provide better resolution views of storms than large radars; (2) that a radar network can be configured as an adaptive system, providing data of higher utility and quality for severe storms; (3) that the data needs of multiple users can be met by a shared radar observing system. Today, parts of the CASA concept is being adopted in several other networks worldwide, such as those in complex terrain or for gap-filling purpose. Nevertheless, much work will be done to continue to advance the CASA concept and deepen its connection to user decision-support in the high impact weather context over the future. Competing end-user requirements need to be consolidated according to policy making and risk management. Public response to warnings is an integrated part of the evaluation process with the assessment of sociological impact. The multi-disciplinary interaction among engineers, end-users, and sociologists is an active area of exploration. The acquired knowledge base will guide the development of future DCAS networks. The major accomplishments and breakthroughs and technical and scientific challenges were listed, throughout the body of this paper along with references. However, perhaps the most important is the lessons learned. A very important lesson learned was the value of multi-disciplinary collaboration. The value of multi disciplinary collaboration is always more than any one s estimate going into the process. Another very important lesson is the value of system engineering based process to solve large complex problems. This process is very valuable. The most important lesson perhaps is the realization, of the difficulty as well as the overarching benefit of multiinstitutional and multi-disciplinary collaboration.

7 Acknowledgment This work was supported by the Engineering Research Centers Program of the National Science Foundation under NSF Award The authors acknowledge the larger CASA community for all their contributions to the various aspects discussed in this paper. References Bharadwaj, Nitin, V. Chandrasekar, Francesc Junyent, 2010: Signal Processing System for the CASA Integrated Project I Radars, J. Atmos. Oceanic Technol., in press. Philips, B., D. Westbrook, D. Pepyne, E. Bass, and D. J. Rude, 2008: Evaluation of the CASA System in the NOAA Hazardous Weather Test Bed. 24 th Conf. on Interactive Information Processing Systems (IIPS) for Meteor., Ocean., and Hydrology, New Orleans, LA. Philips, B., Westbrook, D., Pepyne, D., Bass, E.J., Rude, D.J., Brotzge, J., 2008: User Evaluations of Adaptive Scanning Patterns in the CASA Spring Experiment Proceedings of Intl Geoscience and Remote Sensing Symposium, Boston MA. Philips, B, D. Pepyne, D. Westbrook, E. Bass, J. Brotzge, W. Diaz, K. Kloesel, J. Kurose, D. McLaughlin, H. Rodriguez, and M. Zink: 2007: Integrating End User Needs into System Design and Operation: The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). 16th Conf. Applied Climatol., American Meteorological Society, San Antonio, TX. Brotzge, J., D. Westbrook, K. Brewster, K. Hondl, and M. Zink, 2005: The Meteorological Command and Control Structure of a Dynamic, Collaborative, Automated Radar Network. Preprints, 21st Conf. on Interactive Information Processing Systems (IIPS) for Meteor., Ocean., and Hydrology, San Diego, CA. Chandrasekar V., and A. P. Jayasumana, 2001: Radar design and management in a networked environment. Proc. ITCOMM, Denver, CO, International Society for Optical Engineering, Chandrasekar V., S. Lim, 2008: Retrieval of Reflectivity in a Networked Radar Environment, J. Atmos. Oceanic Technol., pp Chandrasekar V., S. Lim, N. Bharadwaj, W. Li, D. McLaughlin, V. N. Bringi, and E. Gorgucci, 2004: Principles of networked weather radar operation at attenuating frequencies, Proceedings ERAD 2004, pp McLaughlin D. J., V. Chandrasekar, K. Droegemeier, et al., 2005: Distributed collaborative adaptive sensing (DCAS) for improved detection, understanding, and predicting of atmospheric hazards. Proc. Ninth Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), San Diego, CA. McLaughlin D.J., David Pepyne, Brenda Philips, et al., 2009: Short-Wavelength Technology and the Potential For Distributed Networks of Small Radar Systems, Bulletin of the American Meteorological Society, pp Junyent, Francesc, V. Chandrasekar, 2009: Theory and Characterization of Weather Radar Networks, J. Atmos. Oceanic Technol., pp Junyent, Francesc, V. Chandrasekar, D. McLaughlin, E. Insanic, N. Bharadwaj, 2010: The CASA Integrated Project 1 Networked Radar System, J. Atmos. Oceanic Technol., pp Kurose, J., E. Lyons, D. McLaughlin, D. Pepyne, B. Philips, D. Westbrook, and M. Zink, 2006: An end-user responsive sensor network architecture for hazardous weather detection, prediction, and response. Preprints, Asian Internet Eng. Conf. (AINTEC), Pathumthani, Thailand. Rude, D.J., E.J. Bass, B. Philips, 2009: Impact of Increased Spatio-Temporal Radar Data Resolution on Forecaster Wind Assessments IEEE Conference on Systems, Man, and Cybernetics., San Antonio, TX. Ruzanski E., Y. Wang, V. Chandrasekar, 2009: Development of a Real-Time Dynamic and Adaptive Nowcasting System, 25th Conf. Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Phoenix, AZ. Ryzhkov, A.V., S.E. Giangrande, and T.J. Schuur, 2005: Rainfall Estimation with a Polarimetric Prototype of WSR- 88D. J. Appl. Meteor., 44, Liu, Yuxiang, Y. Wang, D. Willie, V. Chandrasekar, and V. N. Bringi, 2007: Operational evaluation of the real-time attenuation correction system for CASA IP1 testbed, 33rd Conference on Radar Meteorology AMS, Cairns, Australia. Wang, Yanting, V. Chandrasekar, 2010: Quantitative Precipitation Estimation in the CASA X-band Dual-polarization Radar Network, J. Atmos. Oceanic Technol., in press. Wang, Yanting, V. Chandrasekar and Brenda Dolan, 2008: Development of Scan Strategy for Dual Doppler Retrieval in a Networked Radar System, IEEE Proceedings of Intl Geoscience and Remote Sensing Symposium, Boston MA. Zink M., E. Lyons, D. Westbrook, J. Kurose, and D. Pepyne, 2010: Closed-loop Architecture for Distributed Collaborative Adaptive Sensing of the Atmosphere: Meteorological Command & Control, International J. Sensor Networks, pp.4-18.