A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm’s performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.