Preprints
https://doi.org/10.5194/egusphere-2025-4752
https://doi.org/10.5194/egusphere-2025-4752
10 Oct 2025
 | 10 Oct 2025
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Wind Estimation based on Flight Dynamics of Unmanned Aerial Vehicle and Its Environmental Application

Dukun Chen, Weifeng Su, Shaojie Jiang, Honglong Yang, Chunsheng Zhang, Shutong Jiang, Dongyang Chang, Yuxin Liang, Hao Wang, Xin Yang, Tzung-May Fu, Zhenzhong Zeng, Lei Zhu, Huizhong Shen, Chen Wang, and Jianhuai Ye

Abstract. Wind speed and direction are crucial for environmental monitoring and meteorological research, yet current measurement techniques face challenges in obtaining high spatiotemporal-resolution wind data while maintaining operational flexibility and cost-effectiveness. This study presents a wind estimation method based on attitude changes of an unmanned aerial vehicle (UAV) through controlled wind wall experiments. The estimated wind parameters were compared with measurements from an onboard wind sensor. Results from meteorological tower validations and field campaigns demonstrate that both the attitude-based and sensor-based methods achieved good agreement with reference measurements during UAV hovering. However, sensor measurements showed significant errors at high vertical flight velocities, primarily due to increased UAV downwash, while the attitude-based method maintained accuracy during flights. Building on UAV attitude changes, a machine learning algorithm was further developed to estimate wind parameters with high accuracy, offering a practical solution for future field deployments. Successful application in coastal observations showcased that wind estimation based on UAV attitude dynamics provided important spatiotemporal wind data sets that could be used to investigate the fate and dispersion of air pollutants. This work presents a reliable, sensor-free algorithm that enables low-cost, high-resolution wind measurements across diverse operational scenarios. This advancement creates new opportunities at the intersection of environmental science and emerging low-altitude economy applications, which hold promise for urban air mobility safety assessment and microscale meteorology-enhanced environmental monitoring.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Dukun Chen, Weifeng Su, Shaojie Jiang, Honglong Yang, Chunsheng Zhang, Shutong Jiang, Dongyang Chang, Yuxin Liang, Hao Wang, Xin Yang, Tzung-May Fu, Zhenzhong Zeng, Lei Zhu, Huizhong Shen, Chen Wang, and Jianhuai Ye

Status: open (until 21 Nov 2025)

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Dukun Chen, Weifeng Su, Shaojie Jiang, Honglong Yang, Chunsheng Zhang, Shutong Jiang, Dongyang Chang, Yuxin Liang, Hao Wang, Xin Yang, Tzung-May Fu, Zhenzhong Zeng, Lei Zhu, Huizhong Shen, Chen Wang, and Jianhuai Ye
Dukun Chen, Weifeng Su, Shaojie Jiang, Honglong Yang, Chunsheng Zhang, Shutong Jiang, Dongyang Chang, Yuxin Liang, Hao Wang, Xin Yang, Tzung-May Fu, Zhenzhong Zeng, Lei Zhu, Huizhong Shen, Chen Wang, and Jianhuai Ye
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Latest update: 10 Oct 2025
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Short summary
This research turns unmanned aerial vehicles (UAVs) into sensitive weather stations by measuring how wind pushes and tilts them in flight. This method accurately gauges wind speed and direction without extra sensors, providing a low-cost way to map complex wind patterns. The findings are vital for improving air quality forecasts, tracking pollution, and ensuring safe drone operations, supporting smarter environmental management.
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