Aerosol optical properties retrieved using polarization Raman lidar observations play an increasingly vital role in meteorology and environmental protection. The quality of the data products directly affects the impact of relevant scientific applications. However, the quality of aerosol optical properties retrieved from polarization Raman lidar signals is difficult to assess. Various factors, such as hardware system performance, retrieval algorithm, and meteorological conditions at the observation site, influence data quality. In this study, we propose a method that allows for assessing the reliability of aerosol optical properties derived from polarization Raman lidar observations. We analyze the factors that affect the reliability of retrieved aerosol optical properties. We use scoring methods combined with a weight-assignment scheme to evaluate the quality of the retrieved aerosol optical properties. The scores and weights of each factor are arranged based on our analysis of a simulation study and the characteristics of each factor. We developed an automatic retrieval algorithm that allows for deriving homogeneous aerosol optical data sets. We also assess with this method the quality of retrieved aerosol optical properties obtained with different polarization Raman lidars under different measurement scenarios. Our results show that the proposed quality assurance method can distinguish the reliability of the retrieved aerosol optical properties.

Aerosol lidars play a vital role in the investigations of atmospheric pollution formation and meteorological processes. The intensity of lidar return signals in the near range changes much faster compared with the one in the far range, so extremely wide dynamic outputs from the photomultiplier tube (PMT) are needed to avoid saturation in the near range. Usually, to obtain the wide dynamic range, simultaneously, a transient digitizer (Licel) is applied to provide an analog detection chain for strong signals and a photon counting (PC) detection chain for weak signals. However, the near-range signals are still often saturated due to the very high aerosol loading. In this paper, we proposed to use a new PMT module with eight orders of magnitude for a low blind zone lidar, which can achieve both analog and PC separately. A comprehensive evaluation of this potential PMT, which could perform better in near-range detection, compared with the ordinary PMT was tested, but similar features are maintained in the far-range. The photon count rate and signal-to-noise ratio were tested for both the new PMT module and the ordinary PMT module. The results showed that the new PMT module is useful to extend the dynamic range of lidar detection.