Publications

The Airport Surveillance Radar (ASR-9) is a rapid-scanning terminal aircraft detection system deployed at airports around the United States. To provide cost-effective wind shear detection capability at medium-density airports, the Weather Systems Processor (WSP) was developed and added on to the ASR-9 at 35 sites. The WSP on the ASR-9 is capable of utilizing dual fan-beam estimates of reflectivity and velocity in order to detect low-level features such as gust fronts, wind shear, and microbursts, which would normally be best detectable by a low-scanning pencil beam radar. An upgrade to the ASR-9 WSP, which is currently ongoing, allows for additional computational complexity in the front-end digital signal processing algorithms compared to previous iterations of the system. This paper will explore ideas for improving velocity estimates, including low-level dual beam weight estimation, de-aliasing, and noise reduction. A discussion of the unique challenges afforded by the ASR-9's block-stagger pulse repetition time is presented, along with thoughts on how to overcome limitations which arise from rapid-scanning and the inherent lack of pulses available for coherent averaging.

Multifunction phased array radars (MPARs) of the future that may replace the current terminal wind-shear detection systems will need to meet the Federal Aviation Administration's (FAA) detection requirements. Detection performance issues related to on-airport siting of MPAR, its broader antenna beamwidth relative to the Terminal Doppler Weather Radar (TDWR), and the change in operational frequency from C band to S band are analyzed. Results from the 2012 MPAR Wind-Shear Experiment are presented, with microburst and gust-front detection statistics for the Oklahoma City TDWR and the National Weather Radar Testbed (NWRT) phased array radar, which are located 6 km apart. The NWRT has sensitivity and beamwidth similar to a conceptual terminal MPAR (TMPAR), which is a scaled-down version of a full-size MPAR. The micro-burst results show both the TDWR probability of detec-tion (POD) and the estimated NWRT POD exceeding the 90% requirement. For gust fronts, however, the overall estimated NWRT POD was more than 10% lower than the TDWR POD. NWRT data are also used to demonstrate that rapid-scan phased array radar has the potential to enhance microburst prediction capability.

Multifunction phased array radars (MPARs) of the future that may replace the current terminal wind-shear detection systems will need to meet the Federal Aviation Administration's (FAA) detection requirements. Detection performance issues related to on-airport siting of MPAR, its broader antenna beamwidth relative to the TDWR, and the change in operational frequency from C band to S band are analyzed. Results from the 2012 MPAR Wind-Shear Experiment (WSE) are presented, with microburst and gust-front detection statistics for the Oklahoma City TDWR and the National Weather Radar Testbed (NWRT) phased array radar, which are located 6 km apart. The NWRT has sensitivity and beamwidth similar to a conceptual terminal MPAR (TMPAR), which is a scaled-down version of a full-size MPAR. The microburst results show both the TDWR probability of detection (POD) and the estimated NWRT POD exceeding the 90% requirement. For gust fronts, however, the overall estimated NWRT POD was more than 10% lower than the TDWR POD. NWRT data is also used to demonstrate that rapid-scan phased array radar has the potential to enhance microburst prediction capability.

The John F. Kennedy International Airport (JFK) and LaGuardia Airport (LGA) are protected from wind shear exposure by the New York Terminal Doppler Weather Radar (TDWR), which is currently located at Floyd Bennet Field, New York. Because of a September 1999 agreement between the Department of the Interior and the Department of Transportation, this location is required to be vacated no later than January 2023. Therefore, a study based on model simulations of wind shear detection probability was conducted to support future siting selection and alternative technologies. A total of 18 candidate sites were selected for analysis, including leaving the radar where it is. (The FAA will explore the feasibility of the latter alternative; it is included in this study only for technical analysis.) The 18 sites are: Six candidate sites that were identified in the initial New York TDWR site-survey studies in the 1990s (one of which is the current TDWR site), a site on Staten Island, two Manhattan skyscrapers, the current location of the WCBS Doppler weather radar in Twombly Landing, New Jersey, and eight local airports including JFK and LGA themselves. Results clearly show that for a single TDWR system, all six previously surveyed sites are suitable for future housing of the TDWR. Unfortunately, land acquisition of these sites will be at least as challenging as it was in the 1990s due to further urban development and likely negative reaction from neighboring residents. Evaluation results of the on-airport siting of the TDWR (either at JFK or at LGA) indicate that this option is feasible if data from the Newark TDWR are simultaneously used. This on-airport option would require software modification such as integration of data from the two radar systems an dimplementation of "overhead" feature detection. The radars on the Manhattan skyscrapers are not an acceptable alternative due to severe ground clutter. The Staten Island site and most other candidate airports are also not acceptable due to distance and/or beam blockage. The existing Airport Surveillance Radar (ASR-9) Weather Systems Processor (WSP) at JFK and the Bookhaven (OKX) Weather Surveillance Radar 1988-Doppler (WSR-88D, commonly known as NEXRAD) on Long Island cannot provide sufficient wind shear protection mainly due to limited wind shear detection capability and/or distance.

Over a decade ago the FAA identified a need to detect and forecast movement of wind shear hazards such as gust fronts that impact the terminal air space. The Machine Intelligent Gust Front Algorithm (MIGFA) was developed to address this need (Delanoy and Troxel, 1993). The MIGFA product provides the position, the forecasted positions, and the strength of each wind shear detection to support air traffic control safety and planning functions. MIGFA will realize a new capability for NEXRAD but was originated for use with the FAA's Airport Surveillance Radar Model 9 (ASR-9) Weather Systems Processor (WSP) as described in Troxel and Pughe (2002). Subsequently, a second version was developed for the FAA's Terminal Doppler Weather Radar (TDWR) and is a component of the FAA's Integrated Terminal Weather System (ITWS). Most of the larger U.S. airports have ITWS installations. The ASR-9s are associated with medium-sized airports. MIGFA in NEXRAD is intended to further expand MIGFA support of air traffic control functions. There are significant algorithmic differences between the ASR-9 WSP and TDWR versions of MIGFA, primarily because of the different beam types of the two radars. Physically, the TDWR's pencil beam allows for good vertical resolution in a spatial volume of data. The ASR-9's vertical fan beam results in poor vertical resolution. Nonetheless, a key tenet in developing these two versions of MIGFA was to use the same core image processing analysis techniques (Morgan and Troxel, 2002) central to the MIGFA functionality. This same core is also central to MIGFA in NEXRAD. The Massachusetts Institute of Technology's Lincoln Laboratory (LL) has been tasked by the FAA to transfer MIGFA technology to NEXRAD. The goal is to enable a NEXRAD MIGFA capability at airports within about 70 km of any NEXRAD. LL has been developing NEXRAD algorithms to address the FAA's weather systems' needs since the Open Radar Product Generator (ORPG) was fielded in 2001. FAA sponsored, LL-developed NEXRAD algorithms generate the following products: the Data Quality Assurance (DQA), the High Resolution VIL (HRVIL), and the High Resolution Enhanced Echo Tops (HREET) (Smalley et al., 2003). These algorithms have proven useful to non-FAA users of NEXRAD products such as the National Weather Service (NWS) and the Department of Defense (DoD). Similarly, the NWS and DoD are developing plans to use MIGFA. MIGFA is slated to be included in the ORPG Build 9 baseline that is scheduled to be released in the Spring of 2007. In the following sections, we will discuss the salient features of MIGFA; the tuning of MIGFA to NEXRAD data; a comparison of detection performance of the TDWR and NEXRAD MIGFA versions; and some examples of MIGFA in operation.

The Weather Systems Processor (WSP) is being deployed by FAA at 35 medium and high-density ASR-9 equipped airports across the United States. The Machine Intelligent Gust Front Algorithm (MIGFA) developed at Lincoln Laboratory provides important gust front detection and tracking capability for this system as well as other FAA systems including Terminal Doppler Weather Radar (TDWR) and Integrated Terminal Weather System (ITWS). The algorithm utilizes multidimensional image processing, data fusion, and fuzzy logic techniques to recognize gust fronts observed in Doppler radar data. Some deficiencies in algorithm performance have been identified through ongoing analysis of data from two initial limited production WSP sites in Austin, TX (AUS) and Albuquerque, NM (ABQ). At AUS, the most common cause of false alarms is bands of low-reflectivity rain echoes having shapes and intensities similar to gust front thin line echoes. Missed or late detections have occasionally occurred when gust fronts are near or embedded in the leading edge of approaching line storms, where direct radar evidence of the gust front (e.g.. thin line echo, velocity convergence) may be fragmented or absent altogether. In ABQ, "canyon wind" events emanating, from mountains located just east of the airport occur with very little lead time, and often with little or no radar signatures, making timely detection on the basis of the radar data alone difficult. MIGFA is equipped with numerous parameters and thresholds that can be adjusted dynamically based on recognition of the local or regional weather context in which it is operating. Through additional contextual weather information processing, this dynamic sensitization capability has been further exploited to address the deficiencies noted above, resulting in an appreciable improvement in performance on data collected at the two WSP sites.