S66 Depicting Aviation Weather Threats in the Upper Troposphere in Hurricanes

Sunday, 10 January 2016
Hall E ( New Orleans Ernest N. Morial Convention Center)
Vanessa M. Przybylo, NOAA/AOML/HRD, Queensbury, NY

NOAA's G-IV aircraft, stationed at the Aircraft Operation Center (AOC), flies at high altitudes around tropical systems to collect atmospheric data. Presently, the G-IV only transits the periphery of tropical cyclones, not over the center, because the scope of flight level hazards in that environment is not well known. Gathering atmospheric data over the center of tropical cyclones would greatly enhance the value of the data provided by NOAA's G-IV due to the core of the storm being the foundation for development. Before attempting any such missions, it is essential to understand the risks that are associated with flying at such high altitudes to ensure safe and efficient missions. This research effort attempts to quantify the probability and frequency of discrete flight level hazards around and over tropical cyclones by developing an upper-tropospheric (200 hPa and 100 hPa) climatology using GFDL model data generated for tropical cyclones that formed over the Atlantic ocean between 2008 and 2010. By correlating many model data parameters with the distance away from the center of the storm and the recorded intensity, flight level risks can be better assessed. Large temperature, humidity, and vertical velocity changes affect flight characteristics, produce wear and tear on the aircraft, and inflict hazardous flying conditions for members on board. In order to diminish such effects, multiple cross-sections of the atmosphere were created to accurately depict the upper-troposphere of 20 different storms. This sample size produced extremely successful results validated by the exact depiction of a conceptual model of the upper troposphere of a hurricane. Ultimately, this study will help aircraft safely penetrate directly through the center of a storm instead of flying solely along the outskirts because the environment will be better understood. Moreover, the newly acquired data will be used to improve tropical cyclone forecast tracks and intensities.
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