Publications
ITWS microburst prediction algorithm performance, capabilities, and limitations
November 13, 1996
Conference Paper
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Workshop on Wind Shear and Wind Shear Alert Systems,. Oklahoma City, 13-15 November, 1996.
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Summary
Lincoln Laboratory, under funding from the Federal Aviation Administration (FAA) Terminal Doppler Weather Radar program, has developed algorithms for automatically detecting microbursts. While microburst detection algorithms provide highly reliable warnings of microbursts. there still remains a period of time between microburst onset and pilot reaction during which aircraft are at risk. This latency is due to the time needed for the automated algorithms to operate on the radar data, for air traffic controllers to relay any warnings and for pilots to react to the warnings. Lincoln Laboratory research and development has yielded an algorithm for accurately predicting when microburst outflows will occur. The Microburst Prediction Algorithm is part of a suite of weather detection algorithms within the Integrated Terminal Weather System. This paper details the performance of the Microburst Prediction Algorithm over a wide range of geographical and climatological environments. The paper also discusses the full range of the Microburst Prediction Algorithm's capabilities and limitations in varied weather environments. This paper does not discuss the overall rationale for a prediction algorithm or the detailed methodology used to generate predictions.
Summary
Lincoln Laboratory, under funding from the Federal Aviation Administration (FAA) Terminal Doppler Weather Radar program, has developed algorithms for automatically detecting microbursts. While microburst detection algorithms provide highly reliable warnings of microbursts. there still remains a period of time between microburst onset and pilot reaction during which aircraft are at...
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Machine intelligent gust front algorithm for the Terminal Doppler Weather Radar (TDWR) and Integrated Terminal Weather System (ITWS)
November 13, 1996
Conference Paper
Published in:
Workshop on Wind Shear and Wind Shear Alert Systems, 13-15 November, 1996.
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Summary
Thunderstorms often generate gust fronts that can have significant impact on airport operations. Unanticipated changes in wind speed and direction are of concern from an air traffic safety viewpoint (hazardous wind shear) as well as from an airport planning point of view (runway configuration). Automated gust front detection is viewed by FAA and the air traffic community as an important component of current and future hazardous weather detection systems including the Terminal Doppler Weather Radar (TDWR), ASR-9 with Weather Systems Processor (ASR-9 WSP), and the Integrated Terminal Weather Systems (ITWS) for which TDWR is a principal sensor. In cooperation with the FAA, Lincoln Laboratory has successfully developed and tested a real-time Machine Intelligent Gust Front Algorithm (MIGFA) for use with Doppler weather radars. This algorithm resulted from the successful fusion of two complementing technologies developed at Lincoln Laboratory: computer vision/machine intelligence techniques originally developed for automated target recognition, and automated product-oriented weather radar data processing. Using these techniques, a version of MIGFA designed for use with TDWR has demonstrated substantial improvement over the existing TDWR gust front algorithm, detecting more and greater extents of gust fronts with fewer false alarms. MIGFA is slated to eventually replace the existing TDWR gust front algorithm and will be used as the gust front algorithm for the planned ITWS and ASR-9 WSP systems. A brief overview of techniques used by MIGFA to identify and track gust fronts will bre presented in this paper. More details, along with recent detection performance results, can be obtained from prior publications. However, detection and tracking of a gust front is only part of the task. Once the location of a gust front has been determined, the associated wind shear estimate and wind shift forecast must be computed. Several issues arises. For example, a gust front can be tens of kilometers in length, with outflow strength and contrasting environmental winds varying considerably along its length. Where along the front should the wind shear analysis be performed? Also, for airport planning purposes, air traffic controllers and managers need to plan runway configuration based on winds that may change suddenly when a gust front moves over the airport. Depending on the nature of the gust front, some of these winds are relatively transient while others are more persistent. How should the wind shift advisory produced by the algorithm take this into account? MIGFA uses a consensus derived from a variety of estimation techniques as a robust means of generating wind shear and wind shift estimates for detected gust fronts. These techniques, and some of their limitations, are discussed. Results of comparisons of MIGFA-generated wind shear and wind shift reports against observations are also presented. The paper concludes by outlining planned enhancements to incorporate additional information available under ITWS that should further improve the quality of MIGFA's wind shear and wind shift forecasts.
Summary
Thunderstorms often generate gust fronts that can have significant impact on airport operations. Unanticipated changes in wind speed and direction are of concern from an air traffic safety viewpoint (hazardous wind shear) as well as from an airport planning point of view (runway configuration). Automated gust front detection is viewed...
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Data processing techniques for airport surveillance radar weather sensing
May 8, 1995
Conference Paper
Published in:
Proc. IEEE 1995 Int. Radar Conf., 8-11 May 1995, pp. 521-528.
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Discusses data processing techniques that can provide high quality, automated weather information using the FAA's existing Airport Surveillance Radars (ASR-9). The cost of modifying the ASR-9 is significantly less than that for deployment of the dedicated terminal Doppler weather radar. These techniques have been implemented on a prototype ASR-9 weather surveillance processor (WSP) and have been tested operationally at the Orlando, FL and Albuquerque, NM air traffic control towers. The key to the success of this system has been the development of innovative data processing techniques that accommodate the non-optimum parameters of the ASR as a weather sensor. The authors motivate the development of the ASR-9 WSP system and describe in detail the data processing techniques that have been employed to achieve an operationally useful capability. They provide an overview of the WSP and the ongoing system development and test program. They provide specifics on the data processing algorithms that have been key to successful implementation of this capability.
Summary
Discusses data processing techniques that can provide high quality, automated weather information using the FAA's existing Airport Surveillance Radars (ASR-9). The cost of modifying the ASR-9 is significantly less than that for deployment of the dedicated terminal Doppler weather radar. These techniques have been implemented on a prototype ASR-9 weather...
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Machine intelligent gust front detection for the Integrated Terminal Weather System (ITWS)
January 15, 1995
Conference Paper
Published in:
Sixth Conf. on Aviation Weather Systems, 15-20 January 1995, pp. 378-383.
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Summary
The Integrated Terminal Weather System (ITWS), currently in development by the FAA, will produce a fully-automated integrated terminal weather information system to improve the safety, efficiency and capacity of terminal area aviation operations. The ITWS will acquire data from FAA and National Weather Service sensors as well as from aircraft in flight in the terminal area. The ITWS will provide products to Air Traffic personnel that are immediately usable without further meteorological interpretation. These products include current terminal area weather and short-term (0-30 minute) predictions of significant weather phenomena. The Terminal Doppler Weather Radar (TDWR) will serve as a principle sensor providing data to a number of the ITWS algorithms. One component of the ITWS will be an algorithm for detecting gust fronts and wind shifts. A gust front is the leading edge of a cold air outflow from a thunderstorm. The outflow, which is deflected at the ground, may propagate many miles ahead of the generating thunderstorm, and may persist as an outflow boundary long after the original storm has dissipated. Gust fronts can have a significant impact on air terminal operations since they often produce pronounced changes in wind speed and direction, forcing a change in active runway configuration and rerouting of aircraft within in the terminal airspace. In addition, wind shear, turbulence, and cross-winds along the frontal boundary pose significant safety hazards to departing and landing aircraft. Reliable detection and forecasting of gust fronts and wind shifts will both improve air safety and reduce costly delays. Lincoln Laboratory has developed an Initial Operational Capability (IOC) Machine Intelligent Gust Front Algorithm (MIGFA) for the ITWS which currently utilizes TDWR and LL WAS or ASOS anemometer data and makes use of new techniques of knowledge-based signal processing originally developed in the context of automatic target recognition [Verly, 1989]. Extensions to the IOC to incorporate additional sensor or product data available under the ITWS (e.g., NEXRAD, terminal winds) are currently under development. MIGFA was first developed for the Airport Surveillance Radar with Wind Shear Processor (ASR-9 WSP). Its design and performance have been documented in previous reports by the authors [Delanoy 1993a]. This paper focuses on the design of the more recently developed TDWR MIGFA and its extension and adaptation to the ITWS (a more detailed description of the TDWR MIGFA can be found in Troxel [1994]). An overview of the signal processing techniques used for detection and tracking is presented, as well as a brief discussion of the wind analysis methods used to arrive at the wind shift and wind shear estimates. Quantitative performance analyses using data collected during recent field testing in Orlando, FL and Memphis, TN are presented. Test results show that MIGFA substantially outperforms the gust front detection algorithm used in current TDWR systems [Hermes, 1993] (MIGFA is currently under consideration as an upgrade option for TDWR).
Summary
The Integrated Terminal Weather System (ITWS), currently in development by the FAA, will produce a fully-automated integrated terminal weather information system to improve the safety, efficiency and capacity of terminal area aviation operations. The ITWS will acquire data from FAA and National Weather Service sensors as well as from aircraft...
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Automated microburst wind-shear prediction
September 1, 1994
Journal Article
Published in:
Lincoln Laboratory Journal, Vol. 7, No. 2, Fall 1994, pp. 399-426.
Summary
We have developed an algorithm that automatically and reliably predicts microburst wind shear. The algorithm, developed as part of the FAA Integrated Terminal Weather System (ITWS), can provide warnings several minutes in advance of hazardous low-altitude wind-shear conditions. Our approach to the algorithm emphasizes fundamental principles of thunderstorm evolution and downdraft development and incorporates heuristic and statistical methods as needed for refinement. In the algorithm, machine-intelligent image processing and data-fusion techniques are applied to Doppler radar data to detect those regions of growing thunderstorms and intensifying downdrafts which lead to microbursts. The algorithm then uses measurements of the ambient temperature/humidity structure in the atmosphere to aid in predicting a microburst's peak outflow strength. The algorithm has been tested in real time as part of the ITWS operational test and evaluation at Memphis, Tennessee, and Orlando, Florida, in 1994.
Summary
We have developed an algorithm that automatically and reliably predicts microburst wind shear. The algorithm, developed as part of the FAA Integrated Terminal Weather System (ITWS), can provide warnings several minutes in advance of hazardous low-altitude wind-shear conditions. Our approach to the algorithm emphasizes fundamental principles of thunderstorm evolution and...
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A microburst prediction algorithm for the FAA Integrated Terminal Weather System
April 4, 1994
Conference Paper
Published in:
SPIE, Vol. 2220, Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles, 4-5 April 1994, pp. 194-204.
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Lincoln Laboratory is developing a prototype of the Federal Aviation Administration (FAA) Integrated Terminal Weather System (ITWS) to provide improved aviation weather information in the terminal area by integrating data and products from various FAA and National Weather Service (NWS) sensors and weather information systems. The ITWS Microburst Prediction product is intended to provide and additional margin of safety for pilots in avoiding microburst wind shear hazards (Fig. 1). The product is envisioned for use by traffic managers, supervisors, controllers, and pilots (directly via datalink). Our objective is to accurately predict the onset of microburst wind shear several minutes in advance. The approach we have chosen in developing the ITWS Microburst Prediction algorithm emphasizes fundamental physical principles of thunderstorm evolution and downdraft development, incorporating heuristic and/or statistical methods as needed for refinement. Image processing and data fusion techniques are used to produce an "interest" image (Delanoy etal., 1991, 1992) that reveals developing downdrafts. We use Doppler radar data to identify regions of growing thunderstorms and probable regions of downdraft, and combine these with measures of the ambient temperature structure (height of the freezing level, lapse rate in the lower atmosphere; Wolfson 1990), total lightning flash rate, and storm motion to predict the microburst location, timing, and outflow strength. There is also a simple feedback system based on the results of the Microburst Detection algorithm that desensitizes prediction thresholds if false predictions are being reported. The following slides describe the preliminary ITWS Microburst Prediction algorithm design, and show examples of feature detector, and the algorithm output on one test case. Results from off-line testing on 17 days of data from Orlando are also presented.
Summary
Lincoln Laboratory is developing a prototype of the Federal Aviation Administration (FAA) Integrated Terminal Weather System (ITWS) to provide improved aviation weather information in the terminal area by integrating data and products from various FAA and National Weather Service (NWS) sensors and weather information systems. The ITWS Microburst Prediction product...
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Machine intelligent approach to automated gust front detection for Doppler weather radars
April 4, 1994
Conference Paper
Published in:
SPIE, Vol. 2220, Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles, 4-5 April 1994, pp. 182-193.
Summary
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.
Summary
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...
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Machine Intelligent Gust Front Algorithm
November 4, 1993
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-196
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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.
Summary
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...
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A machine intelligent gust front algorithm for Doppler weather radars
May 24, 1993
Conference Paper
Published in:
26th Int. Conf. on Radar Meteorology, 24-28 May 1993, pp. 654-656.
Summary
Gust fronts generated by thunderstorms can seriously affect the safety and efficiency of airport operations. Lincoln Laboratory, under contract with the Federal Aviation Administration (FAA), has had a significant role in the development of two Doppler radar systems that are capable of detecting low altitude wind shears, including gust fronts, in the airport terminal control area. These systems are the latest generation Airport Surveillance Radar, enhanced with a Wind Shear Processor (ASR-98 WSP) and the Terminal Doppler Weather Radar (TDWR).
Summary
Gust fronts generated by thunderstorms can seriously affect the safety and efficiency of airport operations. Lincoln Laboratory, under contract with the Federal Aviation Administration (FAA), has had a significant role in the development of two Doppler radar systems that are capable of detecting low altitude wind shears, including gust fronts...
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Automated gust front detection using knowledge-based signal processing
April 20, 1993
Conference Paper
Published in:
Proc. 1993 IEEE Natl. Radar Conf., 20-22 April 1993, pp. 150-155.
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Summary
For reasons of aviation safety and airport operations efficiency, gust front detection and tracking is an important product of Doppler weather radars developed for use in airport terminal areas. Previous gust front algorithms, which have relied on the detection of one or two conspicuous signatures in Doppler radar imagery, have worked reasonably well in images generated by the high-resolution, pencil-beam Terminal Doppler Weather Radar (TDWR). The latest Airport Surveillance Radar, enhanced with a Wind Shear Processor (ASR-9 WSP), is being developed as a less expensive alternative weather radar. Although gust fronts are visible to human observers in ASR-9 WSP imagery, the lower sensitivity and less reliable Doppler measurements of this radar make automated gust front detection a much more challenging problem. Using machine intelligence and knowledge-based signal processing techniques developed in the context of automatic target recognition, a Machine Intelligent Gust Front Algorithm (MIGFA) has been constructed that is radically different from the previous algorithms. Developed initially for use with ASR-9 WSP data, MIGFA substantially outperforms a state-of-the-art gust front detection algorithm based on earlier approaches. These results also indirectly suggest that MIGFA performance may be nearly as good as human performance. Preliminary results of an operational test period (two months, approximately 15000 scans processed) are presented.
Summary
For reasons of aviation safety and airport operations efficiency, gust front detection and tracking is an important product of Doppler weather radars developed for use in airport terminal areas. Previous gust front algorithms, which have relied on the detection of one or two conspicuous signatures in Doppler radar imagery, have...
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