Water vapor is an active trace component in the troposphere and has a significant impact on meteorology and the atmospheric environment. In order to meet demands for high-precision water vapor and aerosol observations for numerical weather prediction (NWP), the China Meteorological Administration (CMA) deployed 49 Raman aerosol lidar systems and established the first Raman–Mie scattering lidar network in China (CARLNET) for routine measurements. In this paper, we focus on the water vapor measurement capabilities of the CARLNET. The uncertainty of the water vapor Raman channel calibration coefficient (Cw) is determined using an error propagation formula. The theoretical relationship between the uncertainty of the calibration coefficient and the water vapor mixing ratio (WVMR) is constructed based on least squares fitting. Based on the distribution of lidar in regions with different humidity conditions, the method of real-time calibration and quality control based on radiosonde data is established for the first time. Based on the uncertainty requirements of the World Meteorological Organization for water vapor in data assimilation, the calibration and quality control thresholds of the WVMR in regions with different humidity conditions are determined by fitting real-time lidar and radiosonde data. Lastly, based on the radiosonde results, the calibration algorithm established in this study is used to calibrate CARLNET data from October to December 2023. Compared with traditional calibration results, the results show that the stability and detection accuracy of the CARLNET significantly improved after calibration in regions with different humidity conditions. The deviation of the Cw decreased from 12.84~18.47% to 5.41~11.54%. The inversion error of the WVMR compared to radiosonde decreased from 1.05~0.46 g/kg to 0.82~0.34 g/kg. The reliability of the improved calibration algorithm and the CARLNET’s performance have been verified, enabling them to provide high-precision water vapor products for NWP.

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.

During the Beijing Winter Olympics, three sets of aerosol lidar in different regions were used to conduct continuous observations to study the optical properties and vertical distribution characteristics of atmospheric aerosol. Based on the lidar data and sun photometer data, the accurate lidar ratio was determined to improve the inversion accuracy of the aerosol backscattering coefficient. The influence of meteorological conditions such as temperature, relative humidity, wind speed, and wind direction on the optical properties of aerosol were analyzed, and the type of aerosol was classified by the depolarization ratio of aerosol particles and CALIPSO data. Furthermore, the backward trajectory analysis, potential source contribution function (PSCF), and concentration weighted trajectories (CWT) were employed to explore the sources and transport mechanisms of pollutants. The analysis found that the extinction coefficient under the atmospheric boundary layer during the Winter Olympics had a spatial distribution pattern of high value in the southeast and low value in the northwest. The occurrence of aerosol pollution events is not only caused by local emissions, but is also related to regional transmission.

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.

Atmospheric lidar has become a powerful tool for atmospheric detection due to its advantages of high spatiotemporal resolution, multiple parameters and high-precision detection. In this paper, the application status of lidar in China’s meteorological observation is introduced, and the prospects for the development of lidar applications are presented. For better observation of atmospheric clouds, aerosol, and water vapor parameters, the China Meteorological Administration(CMA) has laid out and constructed a Raman-Mie aerosol lidar network with 49 stations from 2021 to 2024 and has solved several key technical problems such as data quality control, parameter inversion, and quantitative calibration. In order to achieve high-precision observations with a time resolution at the minute level, more than 10 standard specifications have been formulated for calibration, observation, and data transmission. The lidars for network applications normally use a three-wavelength laser covering 355 nm, 532 nm, and 1064 nm,and they can achieve a detection distance of more than 10 km, an accuracy of aerosol backscattering coefficient of less than 20%（0.5-2 km）and 40%（2-5 km）, and an accuracy of water vapor concentration of less than 1g/kg (0.5-3 km).In the field of wind observation, the CMA has laid out and constructed a wind lidar network with 372 stations by 2024. The lidars use the coherent detection with a laser wavelenth of 1550nm, and they can achieve a maximum detection distance of more than 3km, a horizontal wind speed error less than 0.8m/s, and a horizontal wind direction error less than 8°.The layout and application of aerosol lidar network and wind lidar network have greatly improved China's meteorological observation capability and application levels in aerosol and wind fields. In recent years, the CMA has actively cooperated with various universities and scientific research institutes to carry out key technological studies in atmospheric temperature and humidity lidar, high spectral resolution lidar, middle and upper atmosphere lidar, and airborne/spaceborne lidar. Through planning and constructing meteorological business application platforms in the future, the comprehensive three-dimensional observation of multiple parameters such as temperature, humidity, wind and aerosols will be developed to improve China's meteorological observation ability and provide strong support for research in meteorological services, atmospheric science and climate change.