Abstract. Accurate wind measurement is critical for atmospheric and environmental sciences; however, achieving high spatiotemporal resolution with operational flexibility remains challenging. This study develops and validates an approach for estimating horizontal wind speed and direction based on the flight dynamics of an unmanned aerial vehicle (UAV). Through controlled wind wall experiments, we established a relationship between UAV attitude (e.g., roll, pitch, and yaw) and wind speed. This relationship varies significantly with relative wind direction (with respect to UAV orientation) and payload configuration due to the built-in flight control system and asymmetric airframe of the UAV deployed, demonstrating the necessity of platform-specific calibration for practical application. The performance of this attitude-based method was compared against measurements from a calibrated onboard ultrasonic anemometer. While the sensor-based method achieved good accuracy for hovering and low-speed vertical flights, its performance degraded at higher vertical speeds (>2 m s−1) due to rotor-induced airflow interference. In contrast, the attitude-based method maintained robust accuracy across all flight regimes. Furthermore, a machine learning model was developed to deliver high-fidelity wind estimates (R2>0.90). The model integrated attitude data, flight dynamics, and environmental parameters (e.g., air pressure) and was trained on data from UAV flights during a 20 d field campaign. Validation against measurements from a meteorological tower confirmed the reliability of the machine learning method. This work presents a sensor-free, computationally efficient framework for obtaining high-resolution wind data. By addressing the critical, platform-specific factors affecting estimation accuracy, our approach enhances the applicability of UAVs for advanced environmental monitoring, atmospheric research, and safety assessments in the emerging low-altitude economy.
Chen et al. (Tue,) studied this question.