A wake vortex is a form of irregular airflow generated by a flying aircraft, which can cause a severe hazard for aviation. To quantify the hazard of a wake after fully roll-up and before rebound, this paper proposes an algorithm to retrieve its characteristic parameters (circulations, vortex-core positions, and vortex-core radii) with a scanning Doppler Lidar. In the algorithm, a governing equation related to the Doppler velocities and characteristic parameters is established based on the aerosols’ weak inertia, from which the target parameters are solved with an optimization method. During the process, the distortion of Doppler velocity caused by the scanning of the Lidar beam is adjusted by the Doppler acceleration to achieve better estimations of the target characteristic parameters. Good performance of the algorithm has been verified by simulation and field detection data.

Aircraft wake is a pair of strong counter-rotating vortices generated behind a flying aircraft. It might be very hazardous to a following aircraft and the real-time detection of it is of great interest in aviation safety field. Vortex-core positions and velocity circulations, which respectively represent the location and strength of a wake, are two characteristic parameters that have attracted the main attention in wake vortex detection. This paper introduces a new algorithm, the Path Integration (PI) method, to retrieve the characteristic parameters of wake vortex. The method uses Doppler velocity distribution to locate the vortex-core positions, and the integration of Doppler velocity along a LOS (line-of-sight) is derived as a linear expression about the circulations. From this expression, the circulations can be solved with the least square method. Moreover, an vortex-core position adjusting method is proposed to compensate the compressing and expanding effects of wake vortex caused by the scanning of Lidar beam. Basically, the use of Doppler velocity integration can improve the method’s adaptability in turbulence environment and mitigate the impact of noise. Numerical examples and field detection data from Hong Kong international airport and Tsingtao Liuting airport have well verified the good performance of the method, in terms of both accuracy and efficiency.

Aircraft wake is a pair of counter-rotating vortices generated behind the aircraft, which can greatly impact the safety of fast takeoff and landing of aircraft and limit the improvement of airport capacity. The current wake parameter retrieval methods cannot locate the wake vortex's position and estimate its strength level in real time. To deal with this issue, a novel algorithm based on YOLOv5s deep learning network is proposed. The new algorithm establishes single vortex locating concept to adapt the wake vortex's evolution at complicate background wind field conditions, and proposes strength-based classification standard which can represent the real-time hazard of wake vortex to shorten the takeoff and landing intervals. Meanwhile, EIOU loss function is introduced to improve the precision of YOLOv5s network. Compared with the state-of-the-art object detection approaches, such as Cascade R-CNN, FCOS, and YOLOv5l, the superiority of new method is demonstrated in terms of accuracy and robustness by using the field detection data from Hong Kong International Airport.

Fine-scale wind field nowcasting is of great significance in air traffic management, power grid operation, and so on. In this article, an indirect wind field nowcasting scheme based on lidar observation is presented, which contains an encoder-forecaster network based on the convolutional long short-term memory with balanced structure and a mask branch. The proposed nowcasting network is trained and evaluated based on the lidar observations throughout 2020 at Hong Kong International Airport. Comprehensive comparison with nine methods including the widely used optical flow technique and classic neural network show the good performance of the new network. It can capture the spatio-temporal features in the lidar observations and obtain better nowcasting results up to 27 min with a resolution of 100 m. The nowcasting errors are smaller than the retrieval errors reported in recent literature, demonstrating that the lidar observation nowcasting based on the new network can get fine-scale wind field nowcasting results with high efficiency.

The identification of aviation hazardous winds is crucial and challenging in air traffic management for assuring flight safety, particularly during the take-off and landing phases. Existing criteria are typically tailored for special wind types, and whether there exists a universal feature that can effectively detect diverse types of hazardous winds from radar/lidar observations remains as an open question. Here we propose an interpretable semi-supervised clustering paradigm to solve this problem, where the prior knowledge and probabilistic models of winds are integrated to overcome the bottleneck of scarce labels (pilot reports). Based on this paradigm, a set of high-dimensional hazard features is constructed to effectively identify the occurrence of diverse hazardous winds and assess the intensity metrics. Verification of the paradigm across various scenarios has highlighted its high adaptability to diverse input data and good generalizability to diverse geographical and climate zones.

To better describe the main features of the complex airflow in the atmospheric boundary layer, a hybrid wind field retrieval method based on machine learning (ML) and data assimilation (DA) is proposed. Based on the joint measurement of lidar and in situ measurements, a 3-D variational data assimilation (3DVAR) method is used to retrieve the fine-scale wind field. To address the iterative interpolation problem in the traditional DA methods, this article isolates the interpolation from the optimization and uses the regression methods in ML to estimate the interpolated observations on analysis grids. More specifically, the supervised regression and semisupervised regression are, respectively, used for lidar and in situ observations according to their heterogeneity. Simulation and field measurement results indicate that, compared with the traditional DA methods, the proposed method can better estimate both 2-D and 3-D velocities, by an improvement of more than 42.3% on average.