# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
###### Abstract  
As the applications of unmanned aerial vehicles (UAV) expand, reliable communication between UAVs and ground control stations is crucial for successful missions. However, adverse weather conditions caused by atmospheric gases, clouds, fog, rain, and turbulence pose challenges by degrading communication signals. Although, some recent studies have explored the nature of signal attenuation caused by atmospheric weather variations, studies that compare the attenuation from various weather conditions and analyze the effect on available bandwidth are missing. This work aimed to address this research gap by thoroughly investigating the impact of atmospheric weather conditions on the bandwidth available for UAV communications. Quantitative and qualitative performance analyses were performed for various weather conditions using metrics such as attenuation and the bit error rate of the received signals associated with different modulation schemes and frequencies, using a linearly segmented attenuation model. The results indicate that atmospheric gases and clouds/ fog affect wireless signal propagation; however, the effect of rain on the propagation distances and operating frequencies considered in this study was the most severe. Based on the influence of power transmission, operating frequency, modulation schemes, distance, and adverse weather conditions on the bit error rate and bandwidth suboptimization, we propose an algorithm to select the maximum operating frequency for reliable UAV link operation. U  
A (c) 2025 11 2024 11 2025 11 2  
example, the authors of [9] studied the impact of rainfall on autonomous vehicles and reported that the millimeter wave (mmWave) radar detection range was reduced by up to 45% under severe rainfall conditions. The authors of [10] provided an overview of the effects of atmospheric change on mmWave propagation, specifically the collective attenuation caused by rain, water vapor, dry air, and fog. They concluded that various weather conditions affect the signal propagation. For example, a higher humidity level causes greater path loss owing to water vapor attenuation. The authors of [11] performed real-world experiments to investigate the impact of rain on mmWave propagation at 26 GHz. They collected measurements for one year with 1-min intervals and concluded that rain attenuation was 26.2 dB/km on average, and the total rain attenuation over 1.3 km was 34 dB. Rainfall rate and raindrop size related studies were also reported by the authors of [12; 13; 14], who concluded that higher rainfall rates and larger raindrops lead to higher scattering and absorption. Other researchers focused on other aspects of weather-centric issues that affect wireless signal propagation as well as basic channel attenuation characteristics. For example, the authors of [12; 15] analyzed rain-induced phase changes in orthogonal frequency division multiplexing (OFDM) signals and proposed a pre-demodulation phase compensation method to improve system performance. The authors of [13] considered various weather conditions to evaluate the impact on the channel capacity for a given bandwidth while considering a fixed wireless link. They also evaluated the impact of wind due to vibrations in the antenna structure. The authors concluded that rain-induced attenuation was the dominant factor. The effects of wind and rain were also considered by the authors of [14] with similar observations. Other researchers focused on analyzing the channel capacity under different weather conditions [13; 15]. Artificial intelligence (AI) and machine learning-based (ML) techniques have been researched by several researchers for UAV channel modeling. The authors in [16] used the deep neural networks-based approach for channel modeling and introduced a system combining multiple Gaussian-Bernoulli restricted Boltzmann machines for dimensionality reduction and initial training. Their implementation was integrated with a deep neural network based on autoencoders.