A network of four weather radars was deployed as part of the Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA). The radars operate at the X-band frequency and are capable of polarimetric measurements. The rainfall polarimetric self-consistency principle for absolute calibration is evaluated and applied to the CASA radars' data to estimate any bias in the reflectivity (Z) measurements. Moreover, prior to the application of the self-consistency principle, bias error correction of differential reflectivity (ZDR) measurements is required. Two different approaches were evaluated for ZDRbias correction, the dry aggregated snow approach and the light rain approach. Results of ZDRcalibration show an accuracy of 0.2 dB or better in bias estimation for the two events analyzed when both methods are compared. Moreover, results show a calibration accuracy of 0.6 dB or better for the Z bias estimated using the self-consistency principle. For verification of results, Z bias estimates from the self-consistency principle are compared with Z bias estimated from the comparison between the CASA X-band and the two nearby WSR-88D S-band radars' data. Comparison of the two approaches shows a difference in the Z bias estimation of 0.61 dB or better, which validates the use of the self-consistency principle in rainfall for the absolute radar calibration of the CASA radars. Results show that the self-consistency principle provides a means to estimate bias errors in the radar power measurements from a specific observed medium with a set of space-time characteristics that are not taken into account in the radar hardware relative calibration.

Operational weather radars in the U.S. and other countries in the world are challenged in providing low-altitude observations of rainfall due to the Earth's curvature and their deployment in “sparse” networks spaced hundreds of km apart. Given this limitation, work is underway to explore the feasibility of “dense” networks of small X-band radars. One approach developed by a student team from the U.S. Engineering Research Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) uses low-cost networks of simple, single-polarization radars that are not dependent on existing infrastructure, operating using solar energy and ad-hoc wireless networks, providing gap-filling data with improved temporal and spatial resolution. This “off-the-grid” (OTG) concept is one that might offer a means to monitor rainfall and provide useful data where it is not feasible or cost-effective to deploy more costly and more accurate radars. This paper describes the OTG concept and design, and presents examples of collected data and respective comparisons from this OTG network with measurements from an S-band NEXRAD radar as well as rainfall data from a set of rain gauges located in Puerto Rico. Results show that CASA OTG radars can provide improved spatial and temporal rainfall estimates with consistent or smaller estimated errors when compared to the S-band radar. End user validation was demonstrated in collaboration with the U.S. National Weather Service during system deployment for the XXI Central American and Caribbean Games celebrated at Mayaguez, Puerto Rico during the Summer of 2010.

A novel analytical method for evaluating the electrical performance of a flat, tilted radome for a dual-polarized phased-array antenna under rain conditions is presented. Attenuation, reflections and induced cross-polarization are evaluated for different rainfall conditions. A new radome model is presented which takes into account the properties of the skin surface, area, inclination, radome structure, and rainfall rate. The radome is modeled as consisting of multiple layers, including a wet layer. Attenuation and propagation effects through the radome are characterized using a transmission line equivalent circuit model. Knowledge of the rainfall rate and surface properties of the radome is used to determine the radome performance. Calculated results are compared with radar data obtained with the NEXRAD and CASA systems, where good agreement between measurements and simulations was found.

In this paper the evaluation of dual-polarized scanning performance of a large planar array antenna for a solid state radar for weather is discussed. The antenna array is designed to operate at 9.36 GHz ±50 MHz, and the transmission and reception mode is configured to work alternatively. The antenna array architecture based on a series-fed array configuration of Dual-Polarized Aperture Coupled Patch Antennas (DP-ACPA) was designed and implemented to achieve the required radar polarimetric performance at low cost. Measured patterns of the array in the elevation and azimuth plane are used to evaluate the two principal polarimetric radar parameters (Zdrand LDR) over the scanning range in azimuth plane. It is shown that the biases in the differential reflectivity due the cross-polarization of this antenna configuration are negligible in comparison with the biases produced for the mismatch antenna patterns (H and V).

The Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) has deployed a Distributive, Adaptive and Collaborative Sensing (DCAS) network of four radars in central Oklahoma. The radars operate at the X-band frequency and are capable of polarimetric and Doppler measurements. The radar network is being evaluated for Quantitative Precipitation Estimation (QPE). QPE algorithms based on radar power measurements (e.g. ZHand ZDR) require bias correction. The polarimetric self-consistency principle is applied to the CASA radar data to estimate any bias in ZH. Results show a ZHcalibration accuracy of 0.6 dBZ or less for two the analyzed events. ZHbias estimates from the self-consistency principle in rainfall are compared and validated with ZHbias estimated from the comparison of the X-band and the S-band radars' data. Comparison of the two approaches shows a difference in the ZHbias estimation of 0.61 dBZ or less and validates the use of the self-consistency principle in rainfall for the absolute radar calibration of the CASA radars.

In recent years, it becomes increasingly possible to move the operating frequency of weather radar systems from non-attenuating lower frequencies, such as at S-band, to attenuating higher frequencies, such as at X-band. However, wave is more easily extinct in rain at higher frequencies in which case there will be missing observations. Therefore, rain attenuation is one of the important metrics in radar system design and an extra attenuation margin needs to be applied to the allocation of power budget to meet the required sensitivity. The NSF Engineering Research Center (ERC) for Collaborative and Adaptive Sensing of the Atmosphere (CASA) is advancing a new sensing paradigm using networked short-range radar systems to avoid problematic earth curvature blockage. The CASA ERC has developed a networked radar test bed-Integrated Project 1 (IP1)-in southwestern Oklahoma, using 4 X-band radar of 40 km range to cover an area of 7, 000 km2. In this paper the attenuation margin performance are analyzed in the network context and the metric to design a networked radar system is formed.

This paper discusses the systems architecture of the CASA Phased Array Radar System, the Phase-Tilt Escan Radar System, for deployment in a CASA DCAS network of low power, solid-state phased array radars. The paper highlights the high-level system's architecture accompanied by measured data from the subsystems.

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) 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 CASA's 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.

The Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) has deployed a Distributive, Adaptive and Collaborative Sensing (DCAS) network of four radars in central Oklahoma working as a closed-loop system since 2006. The radars operate at the X-band frequency and are capable of polarimetric and Doppler measurements. The radar network is being evaluated for Quantitative Precipitation Estimation (QPE). QPE algorithms based on radar power measurements (e.g. ZHand ZDR) require bias correction. ZDRcalibration is required prior to any application of the self-consistency principle. Two different methods were evaluated for ZDRbias correction. The intrinsic properties of dry aggregated snow present above the melting layer and light rain measurements close to the ground are used for the study. Results show a ZDRcalibration accuracy of 0.2 dB or less for both analyzed events when both methods are compared.

A dense weather radar network is an emerging concept advanced by the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). A major goal of CASA is to develop an entirely new paradigm, referred to as Distributed Collaborative Adaptive Sensing (DCAS), for improving the coverage of the lowest portion of the atmosphere through coordinated scanning of low-power, short-range, networked radars. The CASA enterprise designs, develops, and deploys system-level test beds to integrate underlying scientific and technical advances and demonstrate the potential to observe, understand, predict and respond to hazardous atmospheric phenomena-with end users involved from the outset. The first one of DCAS test-beds was deployed in south-west Oklahoma, named as Integrated Project 1 (IP1). It is an end-to-end system of a network of four, low-power, short-range, dual polarization, Doppler radar units, aimed at severe weather and hazardous wind sensing. In this paper, a number of aspects in developing a DCAS radar network for these specific applications are reviewed.

Distributed networks of short-range radars offer the potential to observe winds and rainfall at high spatial resolution in volumes of the troposphere that are unobserved by today's longrange weather radars. One class of potential distributed radar network designs includes Off-the-Grid (OTG) weather radar networks. These are short-range radar nodes designed to be deployed as part of an ad-hoc network and to limit their reliance on existing infrastructure. Independence of the wired infrastructure (power or communications) would allow OTG networks to be deployed in specific regions where sensing needs are greatest, such as mountain valleys prone to flash-flooding, geographic regions where the infrastructure is susceptible to failure, and underdeveloped regions lacking urban infrastructure. This paper will present an OTG network testbed being deployed in Western Massachusetts to support experimentation with OTG nodes. This testbed will focus on the energy performance of the OTG network and the virtualization of the radar sensor.

This paper discusses an approach to evaluating network topologies of scalable, low power, solid-state phased array radars that improve the coverage of the lower troposphere (<3 km), which is absent in coverage available with current weather sensing networks.

This paper describes the design of a dual-polarized microstrip series-fed linear array as part of the phase-tilt active planar array antenna being designed for weather sensing for the CASA Engineering Research Center. The dual-polarized planar array antenna is composed of 64 linear array columns, each one formed by 32 aperture coupled patch antenna elements in cascade and excited by a 2 W solid state transmit and receive (TR) module. A straightforward synthesis method is used in order to achieve the desired amplitude and phase excitation of each linear array. Radiation patterns computed using method of moments (MoM) and measured in a compact range system are presented in order to validate the synthesis method proposed.

Many sensor network studies assume that the energy cost for sensing is negligible compared with the cost of communications or computing. Opportunities exist to deploy sensor networks utilizing active sensors with a high energy cost such as radar. For a node utilizing radar as its primary sensor, the actual sensing procedure is the main power consumer. In the worst case almost 50% of the power is consumed by the sensing procedure, while only 3% is used for communication, the remainder is consumed by the computing platform. In this paper we examine a wireless sensor network composed of short range radars used to monitor rainfall. These short-range radar nodes are designed to be deployed as part of an ad-hoc network and to limit their reliance on existing infrastructure. We refer to these networks as "off-the-grid" (OTG) weather radar networks. Independence of the wired infrastructure (power or communications) allows OTG networks to be deployed in specific regions where sensing needs are greatest, such as mountain valleys prone to flash-flooding, geographic regions where the infrastructure is susceptible to failure, and underdeveloped regions lacking urban infrastructure. We present a simulation based investigation of such an OTG sensor network. We focus on power management and energy harvesting for the network. We use these simulations to demonstrate how geographic location, battery capacity, optimization of power consumption, and node density have an impact on the performance and operational lifetime of such a sensor network. In addition to these simulations, we present the design and implementation of an OTG prototype sensor node. Experiences and data gained from the operation of this node are used as input parameters for the simulations.

Distributed networks of short-range radars offer the potential to observe winds and rainfall at high spatial resolution in volumes of the troposphere that are unobserved by today's long-range weather radars. One class of potential distributed radar network designs includes Off-the-Grid (OTG) weather radar networks. These are short-range radar nodes designed to be deployed as part of an ad-hoc network and to limit their reliance on existing infrastructure. Independence of the wired infrastructure (power or communications) would allow OTG networks to be deployed in specific regions where sensing needs are greatest, such as mountain valleys prone to flash-flooding, geographic regions where the infrastructure is susceptible to failure, and underdeveloped regions lacking urban infrastructure. This paper will present a system model and simulation framework for the design of OTG networks. The model estimates the energy requirements of the three major system functions, sensing, communicating and computing, as well as power generated from the solar panel. The simulation will be used to develop an energy cost function to be used in control decisions.

The Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) developed and deployed four polarimetric and Doppler-capable distributed, collaborative and adaptive X-band radars in Oklahoma with the capability to detect and track tornadoes and storms in the lower troposphere with high spatial and temporal resolution. This paper presents an analysis of the performance of X-band rainfall estimation composite algorithms and the conventional reflectivity-rainfall relationship. The polarimetric estimators are developed by different combinations of radar products including horizontal equivalent reflectivity (Zh), differential reflectivity (Zdr) and specific differential phase (Kdp)- The Micronet Little Washita rain gauge network is used for validating rainfall estimates of the KCYR CASA radar. In addition, NEXRAD WSR-88D KFDR radar data are analyzed and compared with the rain gauge network and the CASA radar estimates. Fourteen hours of data collected during August 2006 from three different storms are analyzed. Both convective and stratiform events are represented in the radar data analysis. An accuracy of point radar measurement is assessed for different rain intensities. Results demonstrate an improvement in hourly rainfall accumulation estimation for average rainfall intensities beyond 5 mm/hr with the use of polarimetric estimators. An increase in absolute difference standard error with distance by the use of absolute difference in radar and rain gauges estimates is also evaluated.

2058-2061

The recently established National Science Foundation Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) will be deploying the first generation of an automated network of four low-power, short-range, X-band, polarimetric, Doppler radars, known as NetRad, in central Oklahoma in late 2005. This network is developed with the goal of tracking tornadoes with high spatial and temporal resolution as well as mapping severe weather events in the lowest 2 km of the troposphere. Each radar node has been developed to accomplish this system goal through the coordinated interaction with other radars in the network via a real-time, closed-loop software control system. This paper will describe the characteristics of the individual radar nodes in the system, with emphasis on those aspects of the design that lend themselves toward operation as a coordinated network. Calibration results and performance characteristics of the single node radar of the first generation system will also be presented.

2034-2037

This paper presents a 3D boundary layer wind retrieval algorithm for airborne Doppler radar measurements of precipitation inside hurricanes. The data was collected, inside Hurricane Lili during NOAA's 2002 Atlantic Hurricane Ocean Winds Field Experiment with University of Massachusetts's newly developed Imaging Wind and Rain Airborne Profiler. Two forms of Kalman filters for 3D wind retrieval are analyzed for accuracy using simulations. Preliminary results of the actual 3D wind estimates using the chosen algorithm were obtained and compared with simultaneous and independent wind vector measurements by GPS dropwindsondes, surface wind speed measurements by a microwave radiometer and flight level wind vector measurements

This paper describes a methodology for reflectivity and attenuation retrieval in a networked radar environment. Electromagnetic waves backscattered from a common volume are attenuated differently along the different paths. Solution of the specific attenuation distribution is proposed by solving the integral equation for reflectivity, in a manner similar to that used with a differential phase constraint. The set of governing integral equations describing the backscatter and propagation of common resolution volume are solved simultaneously with constraints on observed total path attenuation. The algorithms developed are evaluated on simulated X-band radar observations in rain obtained from S-band measurements by CSU-CHILL radar. Retrieved reflectivity and specific attenuation using the iterative method show good agreement with intrinsic reflectivity and specific attenuation

This paper presents preliminary results of precipitation effect on microwave scatterometry wind estimates at C and Ku band. The results show that microwave scatterometer underestimates the wind speed by up to 10 m/s for rain rate condition of 15 mm/hr and wind speed condition of 25 to 30 m/s for both frequency bands

A dual-beam along-track interferometric synthetic aperture radar that is entirely self-contained within an aircraft pod has been developed by the University of Massachusetts to study sea surface processes in coastal regions. The radar operates at 5.3 GHz with a bandwidth of up to 25 MHz. System hardware is described. Initial test flights aboard the National Oceanic and Atmospheric Administration's WP-3D research aircraft were performed to evaluate system performance over land and water surfaces. Imagery were collected for fore and aft squinted beams, though no interferometric data were collected. Notable look-angle dependences are observed in the sea surface normalized radar cross section under very low wind conditions.

We present results of dual-polarized radar scattering measurements of the Chesapeake Bay outflow plume. Near-unity polarization ratios (ratios of horizontally polarized radar echoes over vertically polarized ones) are observed in large incidence angle (60/spl deg/ to 80/spl deg/) radar echoes from the outflow plume and its frontal boundary (normally referred to as a front) under strong surface current convergence (0.008-0.02 S/sup -1/), suggesting the existence of steepened and breaking waves in the regions. Cumulative distribution functions of the horizontally polarized radar returns from the front show approximately 90% of the radar echoes are from steepened and breaking waves. Vertically polarized echoes do not show this effect. These experimental results substantiate early modeling investigators' speculation of featured scattering contributing to horizontally polarized radar signatures of oceanic fronts. Our results also suggest that horizontal radar polarization can be used to remotely sense additional hydrodynamic processes such as wave trapping, blocking, and breaking near oceanic fronts better than what is possible with only vertical polarization.

Airborne ocean backscatter dual polarization measurements at C and Ku-band obtained in high wind speed conditions (20 to 60 m/s) are presented. The VV and HH NRCS measurements are compared. The preliminary comparisons show that the HH NRCS measurements are slightly more sensitive to the surface wind speeds for high wind speed and precipitation conditions.

The University of Massachusetts (UMass), with support from ONR, NOAA and NASA, has developed a novel radar system called the Imaging Wind and Rain Airborne Profiler (IWRAP). IWRAP is a dual frequency (C/Ku band) dual polarized airborne radar that profiles the volume and surface backscatter and Doppler simultaneously at 30, 35, 40 and 50 degrees incidence, while conically scanning at 30 to 90 rpm. Its range resolution can be set at 15, 30, 60 or 120 m. From these measurements the ocean surface wind field, 3-D boundary layer winds within rain bands can be mapped. IWRAP was flown during the 2002 NOAA/NESDIS/ORA Hurricane Ocean Winds Experiment, which was conducted in conjunction with the 2002 NOAA/AOML/HRD Hurricane Field Program. This paper presents the system design, radar processing algorithms and initial results from the 2002 hurricane flights.

A dual-beam along-track interferometric synthetic aperture radar which is self contained within an aircraft pod has been developed to study coastal regions. System hardware is described. Initial test flights aboard the NOAA WP-3D research aircraft were performed to evaluate system performance over land and water surfaces. Notable look-angle dependencies are observed in the sea surface NRCS under very low wind conditions.

Recent advances in DNA sequencing technology have created large data sets upon which phylogenetic inference can be performed. However, current research is limited by the prohibitive time necessary to perform tree search on even a reasonably sized data set. Some parallel algorithms have been developed but the biological research community does not use them because they don’t trust the results from newly developed parallel software. This paper presents a new phylogenetic algorithm that allows existing, trusted phylogenetic software packages to be executed in parallel using the DOGMA parallel processing system. The results presented here indicate that data sets that currently take as much as 11 months to search using current algorithms, can be searched in as little as 2 hours using as few as 8 processors. This reduction in the time necessary to complete a phylogenetic search allows new research questions to be explored in many of the biological sciences.

This paper presents high-resolution, dual-polarized Doppler radar measurements of oceanic fronts made from a shipborne platform along the northern boundary of the Gulf Stream during the second Office of Naval Research (ONR)/Naval Research Laboratory (NRL) High Resolution Remote Sensing Experiment (HIRES-II). Average radial scatterer velocity and consecutive short-time Doppler spectra are computed from the coherent backscatter data. These are compared with in-situ current records obtained with an Acoustic Doppler Current Profiler (ADCP) and visually detected whitecap events recorded with a closed-circuit video camera. For the case in which dominant waves are propagating toward the radar, the change in the across-front radial scatterer velocity for vertical polarization shows reasonable agreement with current variations observed with the ADCP. Conversely, for the case in which the radar line-of-sight direction is orthogonal to the direction of propagation of the dominant waves, the radial scatterer velocity is sensitive to changes in the wave propagation direction caused by wave-current interaction at the front. For both polarizations, the shape of the Doppler spectrum shows significant variation across fronts, particularly for horizontal polarization. These spectral variations occur coincidentally with changes in the number of visually detected whitecap events per minute obtained from the closed-circuit video recordings. These results demonstrate the potential of dual-polarized Doppler radars operating in the low grazing angle regime for remotely sensing the current variability and the occurrence of wave breaking across oceanic fronts as well as the effect of fronts on wave propagation.

Attempting to understand and predict weather on a local and global basis has challenged both the scientific and engineering communities. One key parameter in understanding the weather is the ocean surface wind vector because of its role in the energy exchange at the air-sea surface. scatterometers, radars that measure the reflectivity of a target offer a tool with which to remotely monitor these winds from tower-, aircraft-, and satellite-based platforms. This paper introduces three current airborne scatterometer systems, and presents data collected by these instruments under low-, moderate-, and high-wind conditions. The paper focuses on airborne scatterometers because of their ability to resolve submesoscale variations in wind fields. Discrepancies between existing theory and the observations are noted and the concerns in measuring low-wind speeds discussed. Finally, the application of using this technology for estimating the surface-wind vector during a hurricane is demonstrated.>

An airborne C-band scatterometer system (C-Scat) has been developed to remotely sense ocean surface winds and improve upon the present understanding of the relationship between normalized radar cross section (NRCS) and ocean surface roughness influences such as wind speed and direction, wave height and slope, and the air-sea temperature difference. The scatterometer utilizes a unique frequency-steered microstrip array antenna that is installed beneath the fuselage of an airplane. The antenna is electronically scanned in elevation, from 20 degrees to 50 degrees off-nadir, and mechanically spins in azimuth. The system is capable of measuring ocean surface NRCS from altitudes as high as 25000 ft. The system is divided into four subsystems: the transmitter and receiver, the spinning antenna, the computer control and data acquisition subsystem, and the digital and analog interface electronics.>