Publications

Automated gust front detection is an important component of the Airport Surveillance Radar with Wind Shear Processor (ASR-9 WSP) and Terminal Doppler Weather Radar (TDWR) systems being developed for airport terminal areas. Gust fronts produce signatures in Doppler radar imagery which are often weak, ambiguous, or conditional, making detection and continuous tracking of gust fronts challenging. Previous algorithms designed for these systems have provided only modest performance when compared against human observations. A Machine Intelligent Gust Front Algorithm (MIGFA) has been developed that makes use of two new techniques of knowledge-based signal processing originally developed in the context of automatic target recognition. The first of these, functional template correlation (FTC), is a generalized matched filter incorporating aspects of fuzzy set theory. The second technique is the use of "interest" as a medium for pixel-level data fusion. MIGFA was first developed for the ASR-9 WSP system. Its design and performance have been documented in a number of earlier reports. This paper focuses on the more recently developed TDWR MIGFA, describing the signal-processing techniques used and general algorithm design. A quantitative performance analysis using data collected during recent real-time testing of the TDWR MIGFA in Orlando, Florida is also presented. Results show that MIGFA substantially outperforms the gust front detection algorithm used in current TDWR systems.

This paper describes an enhanced weather processor for the Federal Aviation Administration's Airport Surveillance Radar (ASR-9) that will include Doppler wind estimation for the detection of low altitude wind shear, scan-to-scan tracking to provide estimates of the speed and direction of storm movement and suppression' of spurious weather reports currently generated by the ASR-9's six-level weather channel during episodes of anomalous radar energy propagation (AP). This ASR-9 Wind Shear Processor (WSP) will be implemented as a retrofit to the ASR-9 through the addition of interfaces, receiving chain hardware and high-speed digital processing and display equipment. Thunderstorm activity in terminal airspace (the volume extending approximately 30 nmi from an airport and to 15,000 feet altitude) is an obvious safety issue and makes a significant overall contribution to delay in the United States commercial aviation industry. Analysis and on-line testing of the prototype ASR-9 WSP has confirmed that the system can provide operationally beneficial detection of low-altitude wind shear phenomena and enhanced weather situational awareness for Air Traffic Control teams.

The Federal Aviation Administration may acquire a new Airport Surveillance Radar-ASR-11-to replace aging ASR-7s and ASR-8s with a digital terminal radar consistent with Advanced Automation System requirements. A survey of the radar manufacturing industry suggests that a solid-state transmitter will likely be a component of this radar. The ASR-11 will feature a digital weather processing channel to measure and display six calibrated levels of precipitation reflectivity. An additional weather surveillance goal is the capability to support detection of low altitude wind shear phenomena. Use of a low peak power, solid-state transmitter and associated pulse compression technology raises several issues with respect to the capability of ASR-11 to meet these weather measurement objectives: 1. ASR-11 sensitivity will be degraded by approximately 16 to 20 dB relative to the Klystron-based ASR-9 at short range. This results because it is not feasible to use pulse compression waveforms to compensate for low peak transmitter power at short range; 2. Stability of a solid state ASR-11 transmitter may significantly exceed that of previous vacuum tube ASR transmitters. Increased clutter suppression capability associated with this enhanced stability could partially offset the reduced sensitivity of ASR-11 in meeting weather detection goals; 3. Pulse compression range sidelobes may resilt in "ghost" images of actual weather features, displaced in range by as much as 10 km. In some circumstances, these could result in false indications of operationally significant weather features such as thunderstorm-induced gust fronts. We examine these issues through straightforward analyses and simulation. Our assessment depends heavily on Doppler weather radar measurements of thunderstorms and associated wind shear phenomena obtained with Lincoln Laboratory's Terminal Doppler Weather Radar and ASR-9 testbeds. Overall, our assessment indicates that a solid-state transmitter ASR-11 can provide six-level weather reflectivity data with accuracy comparable to that of the ASR-9. Detection of low altitude wind shear phenomena using a solid-state transmitter ASR is more problematic. Reduced sensitivity at short range--the range interval of primary operational concern for an on-airport ASR--results in significant degradation of its capability to measure the reflectivity and Doppler velocity signatures associated with gust fronts and "dry" microbursts. This degradation is not offset by the enhanced clutter suppression capability provided by a solid-state transmitter. Although pulse compression range sidelobes do not appear to be a major issue if they are held to the -55 dB level, simulations are presented where range sidelobes result in a false gust front wind shear signature.

The Federal Aviation Administration has sponsored research and development of algorithms for automatic gust front detection as part of a suite of hazardous weather detection capabilities for airports. These algorithms are intended for use with Doppler radar systems, specifically the Terminal Doppler Weather Radar (TDWR) and the Airport Surveillance Radar enhanced with a Wind Shear Processor (ASR-9 WSP). Although gust fronts are observable with fairly reliable signatures in TDWR data, existing gust front detection algorithms have achieved only modest levels of detection performance. For smaller airports not slated to receive a dedicated TDWR, the ASR-9 WSP will provide a less expensive wind shear detection capability. Gust front detection in ASR-9 SP data is an even more difficult problem, given the reduced sensitivity and less reliable Doppler measurements of this radar. A Machine Intelligent Gust Front Algorithm (MIGFA) has been constructed at Lincoln Laboratory that is a radical departure from previous design strategies. Incorporating knowledge-based, signal-processing techniques initially developed at Lincoln Laboratory for automatic target recognition, MIGFA uses meterological knowledge, spatial and temporal context, conditional data fusion, delayed thresholding, and pixel-level fusion of evidence to improve gust front detection performance significantly. In tests comparing MIGFA with an existing state-of-the-art algorithm applied to ASR-9 WSP data, MIGFA has substantially outperformed the older algorithm. In fact, by some measures, MIGFA has done as well or better than human interpreters of the same data. Operational testing of this version was done during 1992 in Orlando, Florida. The desing, test results, and performance evaluation of hte ASR-9 WSP version of MIGFA are presented in this report, which was prepared as part of the documentation package for the ASR-9 WSP gust front algorithm.

The ASR-9 Wind Shear Processor (WSP) is intended as an economical alternative for those airports that have not been slated to receive a Terminal Doppler Weather Radar (TDWR) but have, or will be receiving, an ASR-9 radar. Lincoln Laboratory has developed a prototype ASR-9 WSP system which has been demonstrated during the summer months of the past three year in Orlando, Florida. During the operational test period, microburst and gust front warnings, as well as storm motion indications, were provided to the Air Traffic Control in real time. The ASR-9 Microburst Detection Algorithm (AMDA) is based on the earlier TDWR Microburst Detection Algorithm but has been substantially modified to match better the particular strengths and weaknesses of the ASR-9 rapid-scanning fan-beam radar. The most significant additions included a capability to detect overhead microbursts, a reflectivity processing step used to help detect velocity signatures that have been biased by overhanging precipitation, and a modification to some of the shear segment grouping and thresholding parameters to accommodate better the typical on-air siting of the ASR-9. In addition, the AMDA has been designed to be as efficient as possible to allow it to run at the radar's 4.8 seconds/scan antennas rotation rate on a single-board computer. A detailed description of AMDA, as well as the performance evaluation strategy and results, are presented in this report.

Low level wind shear has been identified as a cause or contributing factor in a significant number of aviation accidents. Research has shown that the most dangerous type of wind shear is the microburst (Fujita, et al., 1977 and 1979). Briefly, a microburst is an intense local downdraft that results in a strong divergent outflow near the surface. The diameter of the outflow region may vary from 3 to 10 Km. Although many of these accidents were nonfatal, six of them resulted in a total of 550 lives lost. During the past 17 years, the mainstay of the effort by the Federal Aviation Administration (FAA) to provide wind shear warnings to pilots has been the Low Level Wind Shear Alert System (LLWAS). The system has been redesigned, based on extensive operational experience and new knowledge about the nature of the aviation wind shear hazard (Goff and Gramzow, 1989). In parallel development, the Terminal Doppler Weather Radar (TDWR) has provided a capable alternative for ground-based microburst detection (Turnbull, et al., 1989). Recent studies on the integration of LLWAS with TDWR have established the value of a combined TDWR/LLWAS wind shear detection system (Cole and Todd, 1993) The LLWAS system is being developed in four phases, I, II, III, and IV, which reflect the chronology of operational deployments. The original LLWAS, now called LLWAS I, was designed for the detection of frontal shears under the assumption that hazardous wind shear is associated with large-scale meteorological features (Goff and Gramzow, 1989). This system was deployed at 110 airports between 1977 and 1987. LLWAS I had no microburst detection capability and had excessive false alerts. LLWAS II was developed to reduce the false alert rate of LLWAS I and to provide a modest microburst detection capability. It is a direct response to recommendations by the National Research Council (NRS-NAS, 1983), following the 1982 microburst crash in New Orleans. This upgrade, deployed by modifying the software in LLWAS I, provided an improvement that would not suffer the delays and costs of the major construction that is required for off-airport LLWAS III sensors. These upgrades to LLWAS I were installed between 1988 and 1991. LLWAS II will be the operational wind shear detection system at many airports until the late '90s. LLWAS III was developed in response to the requirements that LLWAS have a microburst detection capability (NRS-NAS, 1983). This system was designed by a combination of computer simulation studies (Wilson and Flueck, 1986) and a successful field test of a prototype at Stapleton International Airport, Denver in Augist 1987 (Smythe, et al., 1989 and Wilson et al., 1991). LLWAS III combines a dense sensor network and a sophisticated Wind Shear/Microburst (WSMB) detection algoritohm to provide a substantial microburst detection capability. The prototype LLWAS III has continued to operate at Stapleton International Airport, Denver since 1987 and has been credited with the "save" of a commercial airliner on July 8, 1989. Nine LLWAS IIIs are being installed this year. LLWAS IV will be deployed at 83 airports in the late '90s. The LLWAS IV wind shear and microburst detection algorithms will be identical to LLWAS III. This system features a full hardware upgrade. Major imporvements include an ice-free sensor and hardware that is more reliable and maintainable. This report provides an evaluation of the effectiveness of LLWAS II and LLWAS III. The TDWR operational test bed at Orlando International Airport, Orlando (MCO) provides a unique data set for this evaluation. This test-bed features data from a 14-sensor LLWAS, the prototype TDWR, FL-2C, operated by MIT/LL, and the University of North Dakota meteorolgical radar (UND). Data from this test bed in the summers of 1991 and 1992 are used to provide an evaluation of LLWAS II and LLWAS III. Since LLWAS IV uses the same wind shear detection algorithm, it is expected that LLWAS III and LLWAS IV will have comparable wind shear detection capabilities.

In recent years a number of aircraft accidents have resulted from a small scale, low altitude wind shear phenomena known as a microburst. Microbursts are produced within thunderstorms and are characterized by intense downdrafts which spread out after impacting the earth's surface, displaying strong divergent outflows of wind. They are often associated with heavy rainfall, but can occur without surface rainfall (Wolfson, 1988). The Terminal Doppler Weather Radar (TWDR) program is the first system developed to detect microbursts from a ground-based radar in the airport terminal area. Improving safety is its primary goal, and test operations in Denver, Kansas City, and Orlando have shown it to be highly successful in identifying microbursts. In general, this identification has been performed with a > 90% probability of Detection (POD) and a < 10% Probability of False Alarm (PFA) (Merritt et. al., 1989). The Integrated Terminal Weather System (ITWS) will introduce several new low-level wind shear products. These products include the Microburst Prediction product, the Microburst Trend product, and an improved Microburst Detection Product. The Microburst prediction product will provide estimates of the future location, onset time, and peak intensity of microbursts before their surface effects are evident (Wolfson et. al., 1993). The Microburst Trend product is responsible for warning users about expected increases, over a two minute interval, in wind shear intensity along the approach and departure corridors of a runway. This two minute time period approximates the delay between pilot receipt of an alert and the time of actual encounter with the event. The trend product should serve to improve pilot information when making decisions involving a wind shear event. This is particularly important for currently weak, but rapidly intensifying, wind shears. The Improved Microburst Detection Algorithm being developed under the ITWS program attempts to build on the performance of the TDWR Microburst algorithm by improving POD and PFA and providing fiier localization capabilities. More importantly, enhancements to the TDWR algorithm are necessary in order to 1. provide a consistent input to the microburst trend algorithm. 2. closely relate the microburst alert to the energy loss that the aircraft will actually experience and to alerts from an on-board forward-looking Doppler radar. The TDWR algorithm does a good job detecting the microburst impacted airspace, but makes no attempt to deduce the number and centers of the events. Since the resultant alert shapes are uncorrelated over time, performing a more detailed meteorological analysis, such as location tracking, and size and intensity projections required by the microburst trend product, are compromised. This motivating factor for the improved Microburst Detection Algorithm is discussed in more detail in other works (Dasey. 1993a. Dasey, 1993b). The focus of this paper is on the second motivating factor listed above: relating the microburst alert more closely with actual aircraft performance. Much of this understanding has evolved from the analysis of data from instrumented aircraft penetrations of microbursts within the Orlando terminal area, coincident withTDWR testbed operation (Matthews and Berke, 1993.Campbell et. al., 1992). The microburst penetration flights were conducted by NASA Langley, the University of North Dakota (UND), and several manufacturers of forward-looking wind shear detection systems, including Bendix, Rockwell-Collins, and Westinghouse. Use of this data has allowed comparison of the alert representation from the TDWR Microburst algorithm with that of the initial ITWS algorithm in terms of its relationship with aircraft performance. Section 2. describes a wind shear hazard index, called the F Factor, and its estimation from a ground-based Doppler radar. The estimated F Factors from the TDWR alert shapes are described in section 3. Direct use of TDWR base data for computing shear is explored in section 4, as is the correlation of that data with aircraft F Factor measurements. Estimation of the F Factor from alert shapes output from the initial ITWS detection algorithm is explored in section 5. Section 6 examines the results and emphasizes future research.

In the past decade, a great deal of effort has been invested in developing ground based wind shear detection systems for major U.S. airports. However, there has been a lack of research in developing a quantitative relationship between the wind shear hazards detected by ground based systems and the actual hazard experienced by an aircraft flying through the affected air space. To date, the main thrust of the verification efforts for ground-based systems has been to ensure that the system accurately detect and report the presence of the meteorological phenomena that cause potentially important hazardous windshear. There is a subtle, but potentially important difference between detecting the presence or a microburst and detecting the presence of an aviation hazard. With this in mind, it would seem prudent to rigorously determine what correlation exists between the wind shear warnings that are generated from ground systems and the performance impact on aircraft flying through the impacted airspace. The operational demonstration of the testbed Terminal Doppler Weather Radar (TDWR) in Orlando, Florida along with the testing of airborne Doppler radar systems created a unique opportunity to compare extensively the ground based windshear reports with in-situ aircraft measurements. This paper presents the results from 69 microburst penetrations flown in 1990 and 1991 by the University of North Dakota (UND), the National Aeronautics and Space Administration (NASA) Langley Research Center, and Rockwell Collins under surveillance of the Lincoln-operated TDWR testbed radar. The primary goal of the research was to determine the relative accuracy of several methods designed to generate a numerical microburst hazard index, called the F factor, from ground-based Doppler radar data. It is hope that this work will provide both a qualitative and quantitative basis for the discussion and assessment of microburst hazard reporting for ground-based microburst detection systems. The Integrated Airborne Wind Shear Program is a joint NASA/FAA program with the objective to provide the technology base that will permit low altitude windshear risk reduction through airborne detection, warning, and avoidance. Additionally, the program aims to demonstrate the practicality and utility of real-time assimilation and synthesis of ground-derived windshear data to support executive level cockpit warning and crew-centered information display. Lincoln Laboratory joined this effort and provided the weather radar ground support and some of the post-flight data analysis for NASA's microburst penetration flights in Orlando, Florida.

The Terminal Doppler Weather Radar (TDWR) system which has been developed by Raytheon Co. for the Federal Aviation Administration (FAA), provides automatic detection of microbursts and low-altitude wind shear. Microburst- and gust front-induced wind shear can result in a sudden, large change in airspeed which can have disastrous effect on aircraft performance. during take off or landing. The second major function of TDWR is to improve air traffic management through forecasts of wind shifts, precipitation and other weather hazards. The TDWR system generates Doppler velocity, reflectivity, and spectrum width data. The base data are automatically dealiased and clutter is removed through filtering and mapping. Precipitation and windshear products, such as microbursts and gust fronts, are displayed as graphic products on the Geographic Situation Display which is intended for use by Air Traffic Control supervisors. Alphanumeric messages indicating the various windshear alerts and derived airspeed losses and gains are sent to a flat panel ribbon display which is used by the controllers in the control tower. The TDWR proof-of-concept and operational feasibility have been demonstrated in a number of FAA-sponsored tests and evaluations conducted by Massachusetts Institute of Technology's Lincoln Laboratory (MIT/LL) in Memphis, TN (1985); Huntsville, AL (1986); Denver, CO (1987, 1988); Kansas City, MO (1989, and Orlando, FL (1990-1992). In order to verify that the TDWR meets FAA operational suitability and effectiveness requirements, an Operational Test & Evaluations (OT&E) was conducted at the Oklahoma City site during the period from 24 August to 30 October 1992. The testing addressed National Airspace System (NAS)-SS-1000 requirements, weather detection performance, safety, operational system performance, maintenance, instruction books, Remote Maintenance Monitoring System (RMMS), system adaptable parameters, bullgear wear, and limited Air Traffic (AT) suitability. The TDWR OT&E Integration and Operational testing was conducted using a variety of methods dependent on the area being tested. This paper discusses primarily the weather detection performance testing. A rough analysis was performed on the algorithm output and the base data to determine the performance of the TDWR in detecting wind shear phenomena. Final results will be available after additional testing, which is scheduled for Spring of 1993, and post analysis in conducted.

An operational test of a Wind Shear Processor (WSP) add-on to the Federal Aviation Administration's airport surveillance radar (ASR-9) took place at Orlando International Airport during July and August 1991. The test allowed for both quantitative assessment of the WSP's signal processing and wind shear detection algorithms and for feedback from air traffic controllers and their supervisors on the strengths and weaknesses of the system. Thunderstorm activity during the test period was intense; low-altitude wind shear impacted the runways or approach/departure corridors on 40 of the 53 test days. As in previous evaluations of the WSP in the southeastern United States, microburst detection performance was very reliable. Over 95% of the strong microbursts that affected the Orlando airport during the test period were detected by the system. Gust front detection during the test, while operationally useful, was not as reliable as it should have been, given the quality of gust front signatures in the base reflectivity and radial velocity data from the WSP. Subsequent development of a Machine Intelligent gust front algorithm has resulted in significantly improved detection capability. Results from the operational test are being utilized in ongoing refinement of the WSP.