Wind velocity is of great importance for weather monitoring, aviation hazard alerting, wind energy exploring, and etc. Doppler radar is widely used to measure wind under rainy condition by sensing the raindrops entrained by the background wind. However, the Doppler velocity, which is a reflection of the raindrops velocity in radial direction, is not coincident with the background wind because of the strong inertia of raindrops. Efforts should be made to distinguish the difference between the raindrops' velocity and the background wind velocity. In this paper, we try to establish a relationship between the background wind velocity and the raindrops' velocity by introducing a definition of the raindrops characteristic size, which is related to the velocity characterized by the strongest Doppler spectral component. It is found that the fusion of differential reflectivity and the depolarization ratio can serve as a good proxy for the estimation of the characteristic size. Simulation results for S/C/X band radars and radar measurements verify the good performance of the proposed retrieval model for the characteristic size, which lays a solid foundation for the retrieval of the background wind velocity.

Under rainy conditions, atmospheric settling particles have significant depolarization effects on radar waves and thus affect the target detection performance. This paper establishes an extended target detection model for fully polarized radar under complex rainy conditions and proposes two polarization and waveform optimization methods by taking into account the transmission effect of rainfall. If the rainfall information is known, the polarization and waveform for transmitting and receiving are optimized by maximizing the system Signal-to-Clutter/Noise Ratio (SCNR). While the rainfall information is absent, the optimal transmitting polarization and waveform are obtained by directly compensating the transmission effect from non-rainfall optimization results using fully polarized radar observation parameters. Using measured data from the T-72 tank, The experimental results verify the effectiveness and robustness of the two methods in real scenarios and 4 dB on the improvement of SCNR can be achieved.

Accurate marine wind detection under all-weather conditions is crucial for maritime activities. The joint detection of lidar and radar is supposed to be a potential way to carry out the all-weather sensing of wind. However, their performance analysis has not been well studied, particularly in the far sea area, where the wind-tracing particles are quite different from those inland. Based on the particle distributions above the sea surface under different weather conditions, this study investigated the scattering and attenuation effects of lidar and radar waves in open sea areas with the Mie theory and T-matrix method. Then, the maximum detection range and velocity accuracies of lidar/radar were comprehensively analyzed based on detection principles to optimize the combination of lidar and radar. According to the simulation results, it was difficult to maintain the detection capability of a single lidar/radar under all-weather conditions, and 1.55 $\mathsf{\mu }$m lidar and W-band radar presented a promising joint detection scheme, as they exhibited optimal weather adaptability in clear sky and precipitation conditions, respectively.

Typically, a monostatic radar can only measure the Doppler velocity, which is the projection of full velocity on the LOS (line of sight). To enable measurement of the tangential wind velocity, this paper proposes an interferometric detection method (IDM) based on spaced phase centers of digital phased array radar, which can measure the radial and tangential Doppler velocities simultaneṇously over wide angular ranges. The masscontinuity constraint is further employed to adjust the measured tangential velocity in a closed-form, and no iterative optimization is required. Moreover, since the measurement and adjustment are all computationally inexpensive, the proposed method can be operated in real-time. Simulation experiments for representative scenarios verify the higher temporal resolution and better performance of the proposed method in comparison with traditional methods.

Variational methods are widely used in the meteorological community to retrieve 3D-wind from single LiDAR observations, but the weights for loss function components are static and determined empirically, and iteratively minimizing the loss function is computationally expensive. In this paper, we propose a hybrid method, the Model Function-based Multi-Regularization parameter selection with Adaptive Weights (MFMR-AW) for the background field components to overcome these challenges. MFMR-AW employs the multi-parameter regularization method to combine the Lidar observations with the background field. The combined effect of MFMR and AW determines the weights of background field components. Then the variational principle is used to transform the function minimization process into a system of linear equations, which can be well handled with the truncated singular value decomposition method. Simulations and field observations are employed to validate the proposed method. Results show that the mean RMSEs of MFMR-AW can be reduced by up to 66.9% compared to traditional assumption-based background fields, and the proposed method can get comparable or even superior accuracy with only about 1% of the computation cost of the traditional variational method.