Study of Network Expansion LLWAS (LLWAS-NE) fault identification and system warning optimization through joint use of LLWAS-NE and TDWR data
Low level wind shear has been identified as an aviation hazard which has caused or contributed to a significant number of aircraft accidents (Soffer, 1990). To protect aircraft from hazardous wind shear, the Federal Aviation Administration (FAA) developed a system called the Low Level Wind Shear Alert System (LLWAS), containing a collection of anemometers as well as data processing logic (Wilson and Gramzow, 1991). The LLWAS has undergone several advancements in both design and algorithmic computation. The latest deployment, known as the Network Expansion Low Level Wind Shear Alert System (LLWAS-NE), consists of additional sensors to the original LLWAS network, providing better coverage of the airfield. In addition, the LLWAS-NE is capable of providing runway-oriented wind shear and microburst alerts with loss and gain values. The alerts from LLWAS-NE will be integrated with those from the Terminal Doppler Weather Radar (TDWR) and the Integrated Terminal Weather System (ITWS) at locations where all systems are available (Cole, 1992; Cole and Todd, 1994). An analysis was undertaken at Orlando (MCO) and Dallas/Ft. Worth (DFW) International Airports to assess the accuracy of wind shear alerts produced by LLWAS-NE and the TDWR/LLWASNE integration algorithm. Identifying improvements that can be made to either system is important, as LLWAS-NE alert information is anticipated to be integrated with ITWS in an ITWS/LLWAS-NE integration algorithm. As currently specified, the ITWS/LLWAS-NE integration algorithm will work the same as the TDWR/LLWAS-NE version. The ITWS/LLWAS-NE algorithm is an area where additional work is necessary to ascertain if the integration parameters should be modified to account for performance differences between the ITWS and TDWR algorithms. We suggest that ongoing assessment of the LLWAS-NE should use both LLWAS-NE data and TDWR base data, when possible. Comparing both data sets also will facilitate optimization of LLWAS-NE parameters used in the computation of the alerts.