Publications

The Low Level Windshear Alert System (LLWAS) is an anemometer-based surface network used for detection of hazardous wind shear and acquisition of operational wind information in the airport terminal area. The quality of wind data provided by the LLWAS anemometers is important for the proper performance of the LLWAS wind shear detection algorithms. This report describes the development of an automated method for anemometer data quality (DQA). This method identifies potential data quality problems through comparison of wind data from each sensor within a network to the mean wind speed and direction of the entire network. The design approach and implementation are described, and results from testing using data from the demonstration Phase III LLWAS network in Orlando, FL are reported. Potential improvements to the automated DQA algorithm are presented based on experience gained during analysis of the Orlando data. These recommended improvements are provided to assist future development and refinement of the DQA methodology to be performed by the FAA Technical Center.

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.

We present an overview of the product development strategy and discuss some of the technical considerations. It will be necessary to overcome significant scientific challenges in order to be successful. Our optimism comes from the improved operational meteorological data in the terminal area, from the ability to access and to process these data rapidly, and from ongoing advances in data assimilation for mesoscale models. Our role is to coordinate the fusion of these technical and scientific advances into operational aviation weather products and to evaluate the effectiveness of these products. Major scientific contributions are anticipated from the Forecast Systems Laboratory (FSL), the National Center for Atmospheric Research (NCAR), Pennsylvania State University, and Colorado State University.

As part of the Aviation Weather Development Program of the Federal Aviation Administration, a high resolution winds analysis system was demonstrated at Orlando International Airport (MCO) in the summer of 1992. The purpose of this demonstration was to illustrate the winds analysis capability possible from operational sensors in the mid '90s. An important part of the design of this system was the development of a procedure for the assimilation of Doppler data from multiple radars. This procedure had to be able to automatically handle regions with missing data from one or more radars, as well as avoid baseline instability. The two operational radars scanning the analysis region were the National Weather Service WSR-88D (NEXRAD) radar located approximately 65 km east and slightly south of MCO, and the MIT prototype Terminal Doppler Weather Radar (TDWR) located 7 km due south of the airport. The base data from these two Doppler radars were the major information component for the analysis system. Our system includes the most recent improvements in the winds analysis portion of the Local Analysis and Prediction System (LAPS) developed by the Forecast Systems Laboratory (McGinely et al., 1991). LAPS is designed to run locally on systems affordable for operational weather offices and takes advantages of all sources of local data at the highest possible resolution. Our implementation for the airport terminal region id called the Terminal-area LAPS (T-LAPS). LAPS formerly had a technique for the assimilation of data from a single Doppler radar. We have modified that technique for the assimilation of data from the two available radars. Our approach, using a Multiple Single Doppler Analysis (MSDA) technique, is more suited for unsupervised operational analysis than traditional Dual Doppler Analysis (DDA), because it is able to handle such problems as incomplete data and baseline instability. We will describe the T-LAPS analysis, with particular attention to our implementation of ASDA, and give some examples from our demonstration.

The Terminal Doppler Weather Radar (TDWR) Gust Front Algorithm provides, as products, estimates of the current locations of gust fronts, their future locations, the wind speed and sirection behind the gust fronts, and the wind shear hazard to landing or departing aircraft. These products are used by air traffic controllers and supervisors to warn pilots of potentially hazardous wind shears during take-off and landing and to plan runway reconfigurations. Until recently, an event-based scoring system was used to evaluate the performance of the algorithm. With the event-based scoring scheme, if any part of a gust front length was detected, a valid detection was declared. Unfortunately, this scheme gave no indication of how much of the gust front length was detected; nor could the probabilities be easily related to the probability of issuing a wind shear alert for a specific approach or departure path which was being impacted by a gust front. To make the scoring metric more nearly reflect the operational use of the product, a new length-based scoring scheme was devised. This scheme computes the length of the gust front detected by the algorithm. When computed over a large number of gust fronts, this length-based scoring scheme yields the probability that any part of the gust front will be detected. As improvements to the algorithm increase the length detected, the probability of detecting any part of a gust front increases. In particular, an improved algorithm means an increased probability of correctly issuing wind shear alerts for the runways impacted by a gust front, and length-based scoring is a more accurate technique for assessing this probability of detection. This paper describes the length-based scoring scheme and compares it with event-based scoring of the algorithm's gust front detection and forecast performance. The comparison of the scoring methods shows that recent enhancements to the gust front algorithm provide a substantial, positive impact on performance.