Utilizing GIS in Aviation for Heavy Precipitation Events: A Hurricane Michael (2018) Case Study
Cargile, L., Lee, W., Klotz, B., Cha, T.. (2025). Utilizing GIS in Aviation for Heavy Precipitation Events: A Hurricane Michael (2018) Case Study. , doi:https://doi.org/10.5065/ft6d-aq13
| Title | Utilizing GIS in Aviation for Heavy Precipitation Events: A Hurricane Michael (2018) Case Study |
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| Genre | Manuscript |
| Author(s) | Lilly Cargile, Wen-chau Lee, Bradly Klotz, Ting-Yu Cha |
| Abstract | Weather is one of the greatest hazards in aviation, responsible for over 50% of aircraft-related incidents (Cao et al. 2014). One of the most common weather-related hazards is heavy rain, which can lead to aquaplaning due to standing water. Across different types of runway pavements, the minimum amount of standing water conducive to this phenomenon ranges from 0.05 to 0.12 inches (Williams 1969). The low threshold means it is increasingly important to have accurate forecasts of precipitation accumulation. This paper aims to develop a method, utilizing ArcGIS Pro, to accurately predict standing water accumulations on runways. ArcGIS Pro is a user-friendly platform that is widely accessible, creating easy-to-understand images and interactive maps, allowing it to be utilized by pilots and air traffic control personnel when making crucial decisions. This study utilized three separate analysis methods in ArcGIS Pro to analyze the case study of Hurricane Michael (2018). This event delivered high amounts of precipitation to the Florida panhandle region, making it well-suited for the goals of this study. Utilizing two Automated Surface Observation Stations (ASOS) located in the region, this study compared the ground observation data to Next Generation Weather Radar (NEXRAD)-derived rain rates from reflectivity. When comparing one-to-one time steps and predictive time steps, it was found that NEXRAD is better used as a predictive indicator for rain accumulation. Results showed significant increases in R2 values when using NEXRAD as a predictive indicator across both ASOS stations, such as moving from 0.0046 to 0.6316. These findings and analysis methods are not limited to aviation applications and can be later expanded to investigate widespread precipitation patterns across the United States. |
| Publication Title | |
| Publication Date | Aug 1, 2025 |
| Publisher's Version of Record | https://doi.org/10.5065/ft6d-aq13 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7xk8m19 |
| OpenSky Listing | View on OpenSky |
| MMM Affiliations | DPM |