Significance  Wind shear, a severe atmospheric dynamic phenomenon characterized by violent spatial gradients in wind speed and direction, poses a critical threat to aviation safety, particularly during aircraft take-off and landing. Historical incidents, such as the Capital Airlines JD5759 event at Macau Airport (2018), the low-level wind shear incident at Xining Airport (2022), and the runway-related events at Urumqi Airport (2023), underscore the urgent need for real-time monitoring and accurate identification to mitigate flight risks. Coherent Doppler wind lidar (CDWL) technology, which leverages the optical Doppler effect to measure radial wind velocities with high spatiotemporal resolution by detecting interactions with atmospheric aerosols or molecules, has revolutionized wind shear detection. Its capability to operate effectively in clear-air conditions without precipitation enables the precise capture of subtle wind field variations, providing essential support for early warning systems. This technology aligns with the growing demands of both civil aviation safety and the low-altitude economy, making it a pivotal tool for enhancing operational safety in aviation.

Progress  The technological evolution of coherent Doppler wind lidar represents a significant advancement in atmospheric detection capabilities. Internationally, the technology has progressed from early CO₂ laser systems developed by Raytheon in the 1968 to modern all-fiber systems operating at the eye-safe 1.5 μm wavelength. Key milestones include Lockheed Martin's WindTracer deployment at Hong Kong International Airport (2002) and Mitsubishi Electric's achievement of 30 km detection range. Domestic progress in China has been particularly remarkable, with institutions including the 27th Research Institute of CETC and Nanjing University of Information Science & Technology developing complete technological systems that have been deployed at 26 airports across China, achieving significant breakthroughs in system miniaturization and operational reliability.

The operational effectiveness of wind shear detection relies on sophisticated scanning methodologies and advanced data processing techniques. Four primary scanning strategies have been developed for different application scenarios: Slant Path (SP) for real-time glide path monitoring, Plan Position Indicator (PPI) for horizontal wind field analysis, Doppler Beam Swinging (DBS) for vertical profiling, and Range Height Indicator (RHI) for cross-sectional analysis. These scanning modes are complemented by innovative data processing methods including adaptive filtering algorithms, wavelet transform denoising techniques, and spatial interpolation methods. The integration of machine learning approaches, particularly U-Net convolutional neural networks for signal enhancement under low signal-to-noise conditions, has significantly improved data quality and detection reliability.

Algorithmic development has undergone substantial evolution from traditional methods to intelligent data-driven approaches. Traditional algorithms include slope detection (P.W. Chan), F-factor analysis (Bowles), and regional divergence methods (Hon, K.K.), which were subsequently refined through domestic research including Jiang Lihui's dual-slope detection mechanism and Chen Xing's modified F-factor algorithm. The field has been transformed by machine learning applications, beginning with texture-based classification and progressing to sophisticated deep learning models such as Attention-based Temporal Convolutional Networks achieving 92.3% accuracy. Recent advances focus on multi-source data fusion architectures that integrate lidar data with satellite observations, weather radar information, and computational fluid dynamics models, significantly enhancing detection capabilities in complex meteorological conditions.

Conclusions and Prospects  In conclusion, CDWL technology has matured into a critical tool for wind shear identification, with domestic achievements in China demonstrating large-scale deployment and technological self-reliance. However, challenges remain, including high false alarm rates, limited adaptability to complex terrain and extreme weather, and over-reliance on pilot reports for validation. Future efforts must prioritize deep learning algorithms for enhanced pattern recognition, multi-source data fusion frameworks for comprehensive atmospheric monitoring, and dynamic multi-dimensional feature modeling to enable probabilistic risk assessment. Addressing these directions will advance wind shear identification systems toward greater intelligence, reliability, and adaptability, ultimately strengthening aviation safety in increasingly complex operational environments.