臺灣被國際視為「自然災害很多，極度高風險的地區」，其中的天然災害，包括颱風。由於臺灣位於颱風路徑要衝，每年飽受颱風的威脅，根據氣象局的統計，從西元1911年至2015年總計有360個颱風侵襲臺灣，平均每年有3到4個，颱風引進的西南氣流或是伴隨而來的強降雨，更經常造成許多脆危地區嚴重災損。 現今科技的進步，預報技術日新月異，讓民眾可提早得知颱風警報，尤其電視新聞中有氣象報告之後，氣象主播提供天氣、颱風動態和影響的解說，更成了民眾日常生活中獲得天氣資訊的主要管道，根據國外文獻調查，觀眾對電視新聞最感興趣就是收看氣象。 螢光幕前負責氣象報告的氣象主播可以說是氣象科學、電視傳播媒體和社會大眾之間的重要橋樑，面對全球極端氣候，災變性氣候頻率增加，有許多天氣的最新動態、科學方面的知識和氣象素養更需要詳盡的解說，氣象主播的重要職責就是做好氣象傳播，提供民眾預警訊息。 西元1978年開始，臺灣出現專業的氣象主播，電視產業從早年的三臺壟斷到現今有線電視蓬勃發展，競爭白熱化的電視新聞生態，氣象報告時間也成了新聞戰場，各臺氣象主播更是搶收視率的重，要利器。氣象新聞相互較勁，每一家電視臺氣象主播使出渾身解數並強調自己的解說最權威，最精準，但民眾怎麼看？氣象主播所播報的訊息對於民眾做好防颱準備真的有幫助嗎？為了收視率，播報颱風動態，氣象主播創造新用語或是誇大形容，強化預警效果，民眾認同度又是如何？面對災變性天氣，氣象主播在風險溝通過程中，怎麼看待自己的定位？ 當網路科技興起之後，電腦或行動裝置日益改變民眾接受訊息的習慣，收看氣象主播播報天氣資訊的觀眾，收視行為是否也會有所轉變？本文將以蘇迪勒颱風災害防救的個案分析角度，一來了解氣象主播在災害防救上發揮怎麼樣的關鍵影響力，同時也希望在氣象主播的主題上做先探性的研究。 / Taiwan has been regarded by international as a high-risk area where suffers a lot of natural disasters such as typhoons. According to the Bureau of Meteorology statistics, from 1911 to 2015, a total amount of 360 typhoons attacked Taiwan, with an average of three to four per year. The southwest air flow introduced by typhoons as well as accompanied heavy rains have contributed serious damage to many areas. Thanks to today’s technology and the rapid advance of forecasting technology, people can access the typhoon information in advance, especially from the TV weather forecast. The weather anchor provides information on weather, typhoon dynamics, and impact with the audience. Watching weather report has become part of many people’s daily routine. According to foreign literature survey, the audience is most interested in weather report when they watch TV news. The weather anchor can be viewed as a person who builds the bridge between meteorological science, broadcast media and the public. Due to extreme weather and climate change and the increased rates of catastrophic natural disasters, the audience gets an urge to learn more about the weather and knowledge related to meteorology. Thus, the major duty of a weather anchor is to provide accurate weather information to help prepare the audience for natural disasters. Since 1978, we have had professional weather anchor in the local broadcast industry. Taiwan’s TV industry has gone from the three television stations (TTV, CTV, and CTS), representing the central government’s monopoly on television broadcasting, to booming cable TV today. TV Weather forecasting has become a battlefield in today’s broadcast industry— every weather anchor is competing for the ratings. Every weather anchor claims that they deliver the most authoritative and accurate weather information. However, what does the audience think of it? Is the information provided by the weather anchor helpful for them when it comes to disaster prevention? For the ratings, most weather anchors create fancy words or rather prone to exaggeration, how does the public respond to it? Facing the extreme weather and climate change, how does a weather anchor identify themselves in the process of weather communication? Today the internet has changed the way people access information. Does it also affect people’s behavior when it comes to watching weather news on TV? In this paper, we aim to focus on what the role a weather anchor plays in disaster prevention using a case study approach. Also, we hope our explorations and insights will contribute to your understanding of the role of a weather anchor.

<p dir="ltr">Weather conditions such as thunderstorms, wind shear, snowstorms, turbulence, icing, and fog can create potentially hazardous flying conditions in the National Airspace System (NAS) (FAA, 2021). In general aviation (GA), hazardous weather conditions are most likely to cause accidents with fatalities (FAA, 2013). Therefore, it is critical to communicate weather conditions to pilots and controllers to increase awareness of such conditions, help pilots avoid weather hazards, and improve aviation safety (NTSB, 2017b). Pilot Reports (PIREPs) are one way to communicate pertinent weather conditions encountered by pilots (FAA, 2017a). However, in a hazardous weather situation, communication adds to pilot workload and GA pilots may need to aviate and navigate to another area before feeling safe enough to communicate the weather conditions. The delay in communication may result in PIREPs that are both inaccurate and untimely, potentially misleading other pilots in the area with incorrect weather information (NTSB, 2017a). Therefore, it is crucial to enhance the PIREP submission process to improve the accuracy, timeliness, and usefulness of PIREPs, while simultaneously reducing the need for hands-on communication.</p><p dir="ltr">In this study, a potential method to incrementally improve the performance of an automated spoken-to-coded-PIREP system is explored. This research aims at improving the information extraction model within the spoken-to-coded-PIREP system by using underlying structures and patterns in the pilot spoken phrases. The first part of this research is focused on exploring the structural elements, patterns, and sub-level variability in the Location, Turbulence, and Icing pilot phrases. The second part of the research is focused on developing and demonstrating a structured two-level Named Entity Recognition (NER) model that utilizes the underlying structures within pilot phrases. A structured two-level NER model is designed, developed, tested, and compared with the initial single level NER model in the spoken-to-coded-PIREP system. The model follows a structured approach to extract information at two levels within three PIREP information categories – Location, Turbulence, and Icing. The two-level NER model is trained and tested using a total of 126 PIREPs containing Turbulence and Icing weather conditions. The performance of the structured two-level NER model is compared to the performance of a comparable single level initial NER model using three metrics – precision, recall, and F1-Score. The overall F1-Score of the initial single level NER model was in the range of 68% – 77%, while the two-level NER model was able to achieve an overall F1-Score in the range of 89% – 92%. The two-level NER model was successful in recognizing and labelling specific phrases into broader entity labels such as Location, Turbulence, and Icing, and then processing those phrases to segregate their structural elements such as Distance, Location Name, Turbulence Intensity, and Icing Type. With improvements to the information extraction model, the performance of the overall spoken-to-coded-PIREP system may be increased and the system may be better equipped to handle the variations in pilot phrases and weather situations. Automating the PIREP submission process may reduce the pilot’s hands-on task-requirement in submitting a PIREP during hazardous weather situations, potentially increase the quality and quantity of PIREPs, and share accurate weather-related information in a timely manner, ultimately making GA flying safter.</p>