Lidar has been used for many years to study the optical properties of aerosols, but estimating the boundary values requires solving the lidar elastic scattering equation, which remains a challenge. The boundary values are often determined by fitting to uniform regions of the atmosphere. This method typically excludes low signal-to-noise ratio (SNR) signals because it classifies them as non-uniform, reducing the effective detection range of the lidar. On the other hand, directly fitting low SNR signals to estimate the boundary values can introduce significant errors. The method is based on maximizing the lidar detection distance and determines the boundary value using a new estimation algorithm with the averaging of multiple fitted results in the low SNR region to reduce the impact of noise. Simulations demonstrate that the new method reduces the relative error in the boundary value estimation by approximately 5% and improves the accuracy of the extinction coefficient profile inversion compared with the method of directly fitting all-sample signals. Field comparison experiments with forward-scattering sensors further verify that the algorithm improves the retrieval accuracy by 17.3% under extremely low signal-to-noise ratio (SNR) conditions, while performing comparably to the traditional method in high SNR homogeneous atmospheres. Additionally, based on the scanned lidar signals, the algorithm can provide detailed information on the spatial distribution of sea fog and offer valuable insights for an in-depth understanding of the physical evolution of sea fog.

We propose a novel image preprocessing algorithm (IPA) for coherent Doppler wind lidar (CDWL) wind spectrum inversion under low signal-to-noise ratio (SNR) conditions. By utilizing prior knowledge such as the spatial continuity of wind spectra, this new algorithm significantly improves the reliability of the peak frequency estimation and enhances the wind inversion accuracy and precision. The theoretical analysis shows that the IPA can improve the accuracy and precision of the wind spectrum inversion under low SNR, through preselecting and weighting the Doppler wind spectra. Verified by simulation and real CDWL measurements, the IPA effectively enhances the accuracy and reliability of lidar wind spectrum inversion under low SNR conditions. Specifically, the performance enhancement is equivalent to an approximate improvement in the SNR of the data by 2.5 and 5 dB. The measurements with real CDWL measurements show the maximum effective detection range can be increased by more than 2 km. The IPA is low complexity, demanding low computation resources, and can be applied to high temporal resolution Doppler wind estimation.