Yichao Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yichao Chen has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Dongxu Yang
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2 shared publications - Huanji Zhang
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2 shared publications - Junji Cao
Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China
2 shared publications - Sihong Zhu
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2 shared publications - Tonghui Zhao
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2 shared publications - Yi Liu
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2 shared publications
Latest Publications
- Open Access
Correction: Zhu et al. UAVs’ Flight Dynamics Is All You Need for Wind Speed and Direction Measurement in Air. Drones 2025, 9, 466
There was an error in the original publication
Open AccessCorrection: Zhu et al. UAVs’ Flight Dynamics Is All You Need for Wind Speed and Direction Measurement in Air. Drones 2025, 9, 466
There was an error in the original publication
- Open Access
UAVs’ Flight Dynamics Is All You Need for Wind Speed and Direction Measurement in Air
The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization.
Open AccessUAVs’ Flight Dynamics Is All You Need for Wind Speed and Direction Measurement in Air
The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization.