MultiDisciplinary
Anterior Cruciate Ligaments
By Alexis Jenkins
Alexis Jenkins definitely is very active from the start. Sports have always been her passion; growing up, you could always catch her outside at the softball field playing with her high school, tournament team, or family. This all was until she had an almost career-ending injury occur not just once but twice. Luckily, she could continue to play two years of college softball, but she always wondered why tearing your ACL, also known as your Anterior Cruciate Ligament, was such a big deal. Now years later, she is a Senior here at Millersville studying Sports Journalism. After graduation, she plans to work her way into the ESPN world to eventually become an ESPN Broadcast Journalist.
Gust Front Detection Using NeuroFuzzy Algorithm with Polarimetric WSR-88D
by Amber Liggett
Detecting gust fronts is important because of the strong wind shear, turbulence, and cross-winds, which pose significant safety hazards, including destruction of property and air traffic delays (Hwang, 2013, p. 6, Liggett and Yu, 2015, p. 1). Doppler radar is important in detecting gust fronts, because it allows meteorologists to see characteristics such as wind convergence, debris along the front, and changes in wind speed and direction. Gust fronts produce signatures that are observable by Doppler weather radars (Campbell and Olsen, 1987, p. 5, Klingle and Smith, 1986, p. 905, Wakimoto, 1982, p. 1060). By examining images generated by radars, experienced human observers can reliably detect and track gust fronts.
Since the upgrade of Weather Surveillance Radar 1988 Doppler (WSR-88D) to polarimetric capabilities, artificial intelligence has been utilized as the leading detection method of gust fronts. There are several limitations to artificially detecting gust fronts motivating researchers to improve algorithms for more accurate gust front detection. Visual identifications by professional operational forecasters are critical in providing guidelines to algorithms for improving lead times and detection. Ergo, this study focused on the analysis of how visual detection by operational meteorologists is a valid, reliable method for detecting gust fronts (Delanoy and Troxel, 1993, p. 150).
About the Author
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Amber Liggett is a junior Meteorology major and Mathematics minor from Beaver, PA. Her involvement on MU’s campus includes President of the MU American Meteorological Society student chapter, Weather Watch talent member, and Campus Weather Service Lead Forecaster/Streaming Video personnel.
Two factors motivated Amber to do this project; first, being in the MU Earth Science Research Fellowship. Second, the topic heavily stemmed from her previous internship in the 2015 Research Experience for Undergraduates at The National Weather Center. From detecting gust fronts via the Neuro Fuzy Gust-front Detection Algorithm (NFGDA), Amber found areas of improvement for the algorithm. Last semester, she decided to further study her previously found cases to qualitatively identify whether or not the cases had organized gust front signatures.The specific goal was to offer refinements for improved algorithms so that gust fronts are accurately forecasted. In summary, her project successfully met its goal to qualitatively identified gust front signatures.
Amber’s meteorological passion is to be hands-on in communicating high impact severe weather with both public officials including emergency managers and the general public. For the past 11 years, she has been the CEO of Amber’s Amazing Animal Balloons (www.ambersballoons.com). As part of her business and outreach, Amber discusses the importance/purpose of weather balloons in meteorology to children for engagement in STEM fields. As a career, her interests include becoming both a consultant and forensics meteorologist, emergency management, weather reporting/writing, and researching societal impacts of high impact severe weather and risk communication.