In recent years a number of aircraft accidents have resulted from a small scale, low altitude wind shear phenomena known as a microburst. Microbursts are produced within thunderstorms and are characterized by intense downdrafts which spread out after impacting the earth's surface, displaying strong divergent outflows of wind. They are often associated with heavy rainfall, but can occur without surface rainfall (Wolfson, 1988). The Terminal Doppler Weather Radar (TWDR) program is the first system developed to detect microbursts from a ground-based radar in the airport terminal area. Improving safety is its primary goal, and test operations in Denver, Kansas City, and Orlando have shown it to be highly successful in identifying microbursts. In general, this identification has been performed with a > 90% probability of Detection (POD) and a < 10% Probability of False Alarm (PFA) (Merritt et. al., 1989). The Integrated Terminal Weather System (ITWS) will introduce several new low-level wind shear products. These products include the Microburst Prediction product, the Microburst Trend product, and an improved Microburst Detection Product. The Microburst prediction product will provide estimates of the future location, onset time, and peak intensity of microbursts before their surface effects are evident (Wolfson et. al., 1993). The Microburst Trend product is responsible for warning users about expected increases, over a two minute interval, in wind shear intensity along the approach and departure corridors of a runway. This two minute time period approximates the delay between pilot receipt of an alert and the time of actual encounter with the event. The trend product should serve to improve pilot information when making decisions involving a wind shear event. This is particularly important for currently weak, but rapidly intensifying, wind shears. The Improved Microburst Detection Algorithm being developed under the ITWS program attempts to build on the performance of the TDWR Microburst algorithm by improving POD and PFA and providing fiier localization capabilities. More importantly, enhancements to the TDWR algorithm are necessary in order to 1. provide a consistent input to the microburst trend algorithm. 2. closely relate the microburst alert to the energy loss that the aircraft will actually experience and to alerts from an on-board forward-looking Doppler radar. The TDWR algorithm does a good job detecting the microburst impacted airspace, but makes no attempt to deduce the number and centers of the events. Since the resultant alert shapes are uncorrelated over time, performing a more detailed meteorological analysis, such as location tracking, and size and intensity projections required by the microburst trend product, are compromised. This motivating factor for the improved Microburst Detection Algorithm is discussed in more detail in other works (Dasey. 1993a. Dasey, 1993b). The focus of this paper is on the second motivating factor listed above: relating the microburst alert more closely with actual aircraft performance. Much of this understanding has evolved from the analysis of data from instrumented aircraft penetrations of microbursts within the Orlando terminal area, coincident withTDWR testbed operation (Matthews and Berke, 1993.Campbell et. al., 1992). The microburst penetration flights were conducted by NASA Langley, the University of North Dakota (UND), and several manufacturers of forward-looking wind shear detection systems, including Bendix, Rockwell-Collins, and Westinghouse. Use of this data has allowed comparison of the alert representation from the TDWR Microburst algorithm with that of the initial ITWS algorithm in terms of its relationship with aircraft performance. Section 2. describes a wind shear hazard index, called the F Factor, and its estimation from a ground-based Doppler radar. The estimated F Factors from the TDWR alert shapes are described in section 3. Direct use of TDWR base data for computing shear is explored in section 4, as is the correlation of that data with aircraft F Factor measurements. Estimation of the F Factor from alert shapes output from the initial ITWS detection algorithm is explored in section 5. Section 6 examines the results and emphasizes future research.