An Enhanced Three-Dimensional Wind Retrieval Method Based on Genetic Algorithm-Particle Swarm Optimization for Coherent Doppler Wind Lidar

In this paper, a wind retrieval method based on genetic algorithm-particle swarm optimization (GA-PSO) for the coherent Doppler wind lidar (CDWL) is proposed. The algorithm incorporates an advanced optimization framework that considers wind field spatial continuity, simultaneously enhancing retrieval accuracy and computational efficiency. Comprehensive validations of the GA-PSO algorithm are conducted using a 1.5 μm all-fiber CDWL through ground-based and airborne experiments. In ground-based experiments, the GA-PSO algorithm extends the detection range by 20%~30% compared with traditional methods. The validation against meteorological tower data demonstrates excellent agreement, with mean deviations better than 0.27 m/s for horizontal wind speed and 3.07° for horizontal wind direction and corresponding RMSE values better than 0.36 m/s and 6.04°, respectively. During high-altitude airborne experiments at 5.5 km, the GA-PSO algorithm recovers up to 31% more horizontal wind speed and direction information compared with traditional algorithms, demonstrating exceptional performance in low signal-to-noise ratio (SNR) conditions. Both simulation analysis and field experiments demonstrate that the GA-PSO algorithm achieves processing speeds comparable to traditional real-time methods, establishing its suitability for real-time, three-dimensional wind retrieval applications.