David J. McLaughlin

Short wavelength technology and the potential for distributed networks of small radar systems

The NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is advancing a new approach to radar network design based on dense networks of short-range radars. The centers concept is to deploy small radars atop communication towers, rooftops, and other elements of the infrastructure as a means to comprehensively map winds, rainfall, and other atmospheric and airborne objects throughout the atmosphere with resolution, low-altitude coverage, Doppler wind vector measurement, and other capabilities that are substantially beyond the current state-of-the-art. The technology has the potential to supplement - or perhaps replace - the large long-range civil infrastructure radars in use today.

CASA and LEAD: adaptive cyberinfrastructure for real-time multiscale weather forecasting

Two closely linked projects aim to dramatically improve storm forecasting speed and accuracy. CASA is creating a distributed, collaborative, adaptive sensor network of low-power, high-resolution radars that respond to user needs. LEAD offers dynamic workflow orchestration and data management in a Web services framework designed to support on-demand, real-time, dynamically adaptive systems

Performance of the GLRT for adaptive vector subspace detection

The problem of adaptively detecting a signal confined to a given vector subspace in interference modeled as a zero-mean complex Gaussian N-vector is considered. The correlation properties of interference are not known but are estimated from a given set of secondary (or reference) vectors. The dimension of the known signal subspace is N/sub s/, where 1/spl les/N/sub s//spl les/N. The Generalized Likelihood Ratio Test (GLRT) is cast in a slightly different setting to show that it belongs to a class of invariant tests. The maximal invariants for the class of invariant tests are identified and the joint probability density function of the maximal invariants under both the null hypothesis H/sub 0/ and the alternate hypothesis H/sub 1/ are derived. These expressions are used to show that for 1/spl les/N/sub s/<N, there exists no uniformly most powerful invariant (UMPI) test for the given signal detection problem. Expressions for characterizing the performance of the GLRT are derived and the detection performance of this test when the signal to be detected is a random vector confined to the given vector subspace is evaluated.

Airborne scatterometers: investigating ocean backscatter under low- and high-wind conditions

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. >

The CASA Integrated Project 1 Networked Radar System

Abstract This paper describes the Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project 1 (IP1) weather radar network, the first distributed collaborative adaptive sensing test bed of the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere. The radar network and radar node hardware and software architectures are described, as well as the different interfaces between the integrated subsystems. The system’s operation and radar node control and weather data flow are explained. The key features of the radar nodes are presented, as well as examples of different data products.

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.

An airborne, real aperture radar study of the Chesapeake Bay outflow plume

An airborne, real aperture radar (RAR) has been used to study the fronts associated with the Chesapeake Bay outflow plume during spring outflow conditions. The RAR produced images of the ocean surface with a range resolution of 10 m, an azimuthal resolution of approximately 30 m, and an image size of 2.5 km × 24 km. Two sampling strategies were utilized: one to synoptically map the entire mouth of the Chesapeake Bay at roughly hourly intervals; and a second to capture the rapid evolution of particular features. In addition, flight times were chosen such that over the course of the entire experiment, data were collected over all phases of the semidiurnal tidal cycle. Three distinct frontal signatures were observed in the imagery. A primary front extended from inside the estuary along the Chesapeake Channel to an anticyclonic turning region east of Cape Henry, and then extended southward along the coast toward Cape Hatteras. This is the classic expression of the plume front, inertial turning region, and coastal jet. A second front with a north-south orientation was observed approximately 20 km east of the bay mouth. This secondary front appears to mark the residual offshore density gradient. A third front was identified east and south of Cape Henry, within 2 km of the coast. This front appears to mark the inshore edge of the plume and has not been documented previously. Time sequences of the imagery indicate that when moving in a clockwise sense around the primary front, the frontal translation speed varies systematically from 20 cm/s in the northern section to 50 cm/s in the south. The position of the primary front and the locations and trajectories of small-scale frontal cusps suggest that bathymetry may be both a significant determinant of the front location as well as a source of along-front variability. These observations are possible due to the airborne RARs ability to collect high-frame rate image sequences, a capability that is not shared by present space-based radar systems.

Frequency dependence of electromagnetic bias in radar altimeter sea surface range measurements

Range measurements made by satellite radar altimeters experience a bias toward the troughs of ocean waves. A series of aircraft flights during February–April 1989 measured this electromagnetic (EM) bias at three radar frequencies and the UV under a variety of wind and wave conditions, and provided the first airborne open-ocean measurements at the 13.6-GHz and 5.3-GHz operating frequencies of the NASA altimeter on the TOPEX/Poseidon satellite. The data suggest that the mean EM bias decreases linearly with increasing radar frequency between 5.3 and 36 GHz, according to the expression: EM bias (% of significant wave height) = (3.0–0.0617 F)(1±0.5), where F is in gigahertz. EM bias is fairly constant over a mesoscale region on a given day but can fluctuate significantly from one day to another. It shows a strong increase at all radar frequencies with increasing wind speed, although other sea state conditions, such as the wind direction relative to the wave direction, are also factors.

High resolution polarimetric radar scattering measurements of low grazing angle sea clutter

This paper presents fully polarimetric radar scattering measurements of low grazing angle sea clutter. The measurements were obtained at a three degree grazing angle using a high range resolution (1.5 m) X-Band polarimetric radar operated from a shore site overlooking the Chesapeake Bay. The radar employs pulse-to-pulse switching between orthogonal transmitted polarizations and simultaneously measures two orthogonally polarized components of the backscattered wave to obtain full polarimetric information about the scattering process. The complete Stokes matrix, computed by averaging successive realizations of the polarization scattering matrix, is used to obtain polarization signatures and to determine the polarization dependence of the clutter. Sea spike echoes are shown to be weakly polarized and to exhibit polarization signatures indicative of multiple independent scattering mechanisms. Clutter echoes in the absence of sea spikes are shown to be highly polarized and to exhibit polarization signatures indicative of a single dominant scattering mechanism. >

Collaborative adaptive sensing of the atmosphere : New radar system for improving analysis and forecasting of surface weather conditions

An Engineering Research Center for the Collaborative Adaptive Sensing of the Atmosphere (CASA) was formed in the fall of 2003 by the National Science Foundation to develop a dense network of small, low-cost, lowpower radars that could collaboratively and adaptively sense the lower atmosphere (0 to 3 km above ground level). Such a network is expected to improve sensing near the ground dramatically through a process called distributive collaborative adaptive sensing. The CASA network is a dynamic, data-driven application system, whereby strategy for scanning is an optimized network solution among competing end-user needs and weather constraints. Decision making is made in real time, with end users providing automated or manual input, or both, to the system. Furthermore, each radar will have dual-polarization capability and signal processing designed to minimize ground clutter contamination. Data collected from the CASA network will be assimilated in real time for use in detection algorithms, numerical weath...