Wind patterns analysis on temporal scales for safe UAV operations using statistical approaches

The wind is one of the major factors that may cause unmanned aerial vehicles (UAVs) to crash and pose fatality risk to the population and property damage risk to infrastructures. This paper investigates wind patterns on temporal scales to identify high-risk periods in terms of wind conditions for safe UAV operations in urban airspace. The research starts with the historical wind speed data analysis using statistical approaches. As the wind speed data does not follow normal distribution after checking, a nonparametric approach of the Kruskal-Wallis test is applied for hypothesis testing to see if there is a significant difference in the median wind speed in different years. Regression analyses are also performed for monthly wind speed data to check any significant trends that could facilitate the predictions of average wind speed in the long term. This study will contribute to safe air traffic management for UAV operations in low-altitude urban airspace by mitigating adverse wind effects.