Coherent Doppler Wind Lidar (CDWL) is an effective tool for measuring the atmospheric wind field. However, CDWL is affected by various noises, which can reduce the usable value of the received echo signal. This paper proposes a novel joint denoising algorithm based on SVD-ICEEMDAN-SCC-MF to remove noises in CDWL detection. The SVD-ICEEMDAN-SCC-MF consists of singular value decomposition (SVD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), Spearman correlation coefficient (SCC), and median filtering (MF). Specifically, the SVD first separates the signal from the noise by retaining the main feature (large singular value) and removing the remained components (small singular value) to achieve the initial signal reconstruction. Then, ICEEMDAN is used for decomposition to distinguish the intrinsic mode function (IMF) of the signal and the noise. The SCC of the retained components is calculated to determine the correlation of the reconstructed signal. Furthermore, low correlation components of the reconstructed signal are denoised again by median filtering (MF). Finally, the complete denoised signal is obtained by combining the components after MF and the high correlation components in the previous stage. The validity of the SVD-ICEEMDAN-SCC-MF is verified in simulated and real data, and the denoising effect is significantly better than other algorithms. In simulation cases, the $SN{R}_{out}$ of the proposed method is improved by 20.5117 dB at most, from −5 dB to 15.5117 dB, and the $RMSE$ is only 0.5174. After denoising the power spectrum of the real CDWL signal, the detection range is extended from 3 km to more than 3.6 km.

Multi-range-gate wind retrieval with coherent Doppler wind LiDAR (CDWL) remains challenging under a low signal-to-noise ratio (SNR) and precipitation: low SNR, spectral-edge peaks and system gain drift destabilize the radial frequency estimation, and multi-beam synthesis further amplifies the errors, directly affecting the reliability of vertical wind profiles. This study develops a multi-range-gate wind retrieval framework that couples physical constraints with data-driven regression. After spectral accumulation and quality control (QC), an adaptive super-noise centroid (sup-adap) estimator extracts radial velocities, and a CNN-LSBoost model maps the multi-beam radial velocities of adjacent range gates to the horizontal wind. The framework was evaluated with prescribed winds in simulations and validated against independent meteorological-tower observations from a field experiment under moderate rain, with training and test hours strictly separated. Against the tower, CNN-LSBoost achieved R² = 0.983, RMSE = 0.42 m/s and P95 = 0.53 m/s, outperforming KNN + GA, RF + GWO, CNN and Transformer baselines. The results indicate that the proposed framework provides a validated route to reliable wind profiling under low-SNR and precipitation conditions.