Aerosols are one of the major uncertain sources in radiative forcing assessments of the land-atmosphere system, and aerosol profile data detected by lidar can help quantitatively assess the climate effects of aerosols. In addition to published aerosol observation products, a large amount of aerosol lidar observation data are distributed in the references. However, there is still a lack of integrated analysis of historical aerosol reference data. Thus, we focus on the lidar ratio parameters that are relatively lacking in the existing observation products and propose a fuzzy comprehensive evaluation and analysis method of historical lidar ratio data with aerosol type differences fully considered. The historical data can complement the products of aerosol observation data, and the proposed evaluation and analysis method can help improving the understanding of optical aerosol properties.

Based on the idea of fuzzy comprehensive evaluation, we propose a fuzzy comprehensive evaluation and analysis method for the historical reference data of aerosol lidar ratio, and design the evaluation index of confidence level. The confidence level analysis is shown in Fig. 1. First, the evaluation factors of the historical data are selected, and the analytic hierarchy process (AHP) is employed to determine the contribution proportion of each evaluation factor to the confidence level. Then, according to the characteristics of these factors, the membership function of each factor is determined, and the contribution weights are multiplied by the membership function to get the confidence value. Finally, the confidence values of all historical data are calculated, and the historical data of the same type and wavelength are accumulated to obtain the distribution of the total confidence values of the lidar ratio. To enable comparative evaluation, we normalize the total confidence values to obtain the distribution of confidence level for different types of aerosols lidar ratio over historical data.

All observations of aerosol lidar ratios in the Web of Science database are analyzed with confidence level by the proposed evaluation method. We find that all aerosol types show different aggregation trends similar to Gaussian distribution on the lidar ratio distribution, and the larger amount of historical data lead to a better Gaussian fitting effect. Additionally, the analysis is carried out for sand and dust aerosols from different sources, and the results shown in Fig. 5 indicate that the optical properties of the same aerosol will be different for different sources. Finally, the confidence ranges of the lidar ratios for various aerosol types are summarized in Table 3 for reference, and the results are compared with the simulation data in Fig. 6 with good consistency.

We propose a fuzzy comprehensive evaluation and analysis method for the historical reference data of aerosol lidar ratios, which makes up for the analysis method gap of historical aerosol data and provides references for analyzing the aerosol research basis. Analysis of all the relevant observations in the Web of Science database show that the historical data of lidar ratios of all aerosol types have Gaussian distributions. The traditional aerosol type recognition method is the decision tree, which adopts a fixed threshold to truncate the aerosol data and is prone to cause aerosol type misidentification and discontinuous classification limitation. The lidar ratios of different aerosol types overlap, and they alone are unable to differentiate various aerosol types. Therefore, at least one more classification index should be introduced when aerosol type identification is needed. We present a more comprehensive historical data analysis of the aerosol lidar ratio to improve the understanding of optical aerosol properties and refine the aerosol classification results, providing an accurate reference basis for data analysis of on-board lidars.