ITWS and ITWS/LLWAS-NE runway alert performance at Dallas-Ft. Worth and Orlando
September 11, 2000
Conference Paper
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Ninth Conf. on Aviation, Range, and Aerospace Meteorology, 11-15 September 2000, pp. 590-595.
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ITWS and ITWS/LLWAS-NE runway alert performance at Dallas-Ft. Worth and Orlando
Summary
The Integrated Terminal Weather System (ITWS) provides runway-orientated wind shear and microburst alerts to enhance the safety of flight operations at major U.S. airports. The alerts are reported as either losses or gains of airspeed, representing performance decreasing or performance increasing wind shears. The performance of ITWS as a stand-alone system has been thoroughly documented in previous research. During the 1994 ITWS Demonstration and Validation testing, the probability of detection (POD) and probability of false alarm (PFA) at Memphis (MEM) and Orlando (MCO) for all loss events were > 90 and < 5 percent, respectively, based on single-Doppler truth. The Low-Level Windshear Alert System-Network Expansion (LLWAS-NE) also generates runway alerts in the same format as ITWS. LLWAS-NE is not subject to viewing angle problems such as those experienced by single-Doppler radar. However, false alarms caused by LLWAS-NE sensor failures at some Terminal Doppler Weather Radar (TDWR) sites have reduced user confidence in the system. At those ITWS sites with an LLWAS-NE, the ITWS alerts derived from TDWR data will be integrated with LLWAS-NE alerts, hopefully to improve the performance. The ITWS integration algorithm is identical to the TDWR version, with the exception of a few adaptable parameter changes. The ITWS/LLWAS-NE parameters were modified slightly to account for ITWS and TDWR algorithm performance differences. In this paper, the performance of a stand-alone ITWS and the ITWS/LLWAS-NE integration algorithm at the MCO and Dallas-Ft. Worth (DFW) demonstration sites will be discussed. This assessment is considered unique since the radar and anemometer data were combined to create the runway truth. The focus of this research is to identify the shortcomings of both systems in order to recommend modifications that will improve the integration algorithm performance.