Demonstrates an effective method for assessing lidar data quality in a nationwide network, suggesting improvements for operational applications.
The China Aerosol Raman Lidar Network (CARLNET), developed by the China Meteorological Administration, currently comprises 49 multiwavelength polarization Raman lidars used for meteorological and atmospheric-environment monitoring. Timely and automatic quality assessment of the lidar raw signal is vital for a large atmospheric lidar network. This study proposes a quality assessment method of lidar raw data for the CARLNET. By scoring three factors, signal saturation at near-range, Rayleigh fit and effective detection range, and weighting each influence factor according to its importance, each lidar raw data is tagged by a composite score. These scores reflect the quality of lidar raw data, as well as potential issues of lidar systems. Three lidars under three typical weather scenarios are used to analyze the impact of observation scenarios on lidar raw data, and the results show that the proposed method can effectively distinguish the lidar raw data quality under different scenarios. By analyzing the scores of lidar raw data, two potential hardware issues (optical-axis misalignment and signal-receiving issues) are identified, which provide guidance for equipment maintenance. In addition, we applied the method to one-year CARLNET measurement data. Temporally, five representative sites were selected for analysis of their annual data, revealing the seasonal and overall scores of the raw data. Spatially, the signals at the 355 nm, 532 nm, and 1064 nm channels of 49 nationwide distributed lidars were evaluated and categorized into six groups based on their scores, which provides support for lidar network data quality monitoring, operational applications, and scientific research.
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Bu et al. (2026) studied this question.