Tuesday, 13 January 2004: 3:45 PM
Precision Airdrop System An Emerging Operational Capability
Room 6A
Poster PDF
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Dynamic model high-resolution forecast fields, in-situ measurements and a tailored data assimilation/forecast method are used on-scene to provide short-range forecasts/nowcasts to support high-altitude precision airdrop operations. Historically, ballistic re-supply airdrops and humanitarian airdrop relief operations have been conducted at low altitudes and slow airspeeds. This flight profile places the aircraft at risk in hostile environments. Recently, ballistic airdrop operations have been conducted at high altitudes to avoid ground threats, but with an attendant decrease in delivery accuracy. Current weather forecasting and data assimilation capabilities have been integrated to provide an emerging operational capability for the United States Air Force (USAF). The Precision Airdrop System (PADS) has been developed and tested to enable more accurate delivery of ballistic payloads from altitudes approaching the maximum performance envelope of USAF airdrop aircraft. PADS is a laptop computer-based mission planning system designed for pre-takeoff mission planning, in-flight updates aboard the airdrop aircraft, and mission execution decisions. Precision is achieved through the assimilation of high-resolution 4-dimensional forecast data (winds, density, and pressure), high-resolution topographic data, and in-situ weather data observed near the drop zone. Forecast fields from the Air Force Weather Agency's 5th Generation Mesoscale Model (MM5) are assimilated with in-situ measurements using the NOAA Forecast Systems Laboratory's Local Analysis and Prediction System (LAPS), and extrapolated to planned payload release time. The Computed Air Release Point (CARP) is determined from the application of a payload release and descent trajectory model using the 3-dimensional nowcast field resulting from the LAPS assimilation. Results of high-altitude airdrop tests using PADS, and the analysis of forecast, observed and assimilated data fields for actual and hypothetical data scenarios are presented.
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