Accurate monitoring of atmospheric carbon dioxide (CO₂) and its distribution is of great significance for studying the carbon cycle and predicting future climate change. Compared to the ground observational sites, the airborne observations cover a wider area and simultaneously observe a variety of surface types, which helps with effectively monitoring the distribution of CO₂ sources and sinks. In this work, an airborne experiment was carried out in March 2019 over the Shanhaiguan area, China (39–41^∘ N, 119–121^∘ E). An integrated path differential absorption (IPDA) light detection and ranging (lidar) system and a commercial instrument, the ultraportable greenhouse gas analyser (UGGA), were installed on an aircraft to observe the CO₂ distribution over various surface types. The pulse integration method (PIM) algorithm was used to calculate the differential absorption optical depth (DAOD) from the lidar data. The CO₂ column-averaged dry-air mixing ratio (XCO₂) was calculated over different types of surfaces including mountain, ocean, and urban areas. The concentrations of the XCO₂ calculated from lidar measurements over ocean, mountain, and urban areas were 421.11 ± 1.24, 427.67 ± 0.58, and 432.04 ± 0.74 ppm, respectively. Moreover, through the detailed analysis of the data obtained from the UGGA, the influence of pollution levels on the CO₂ concentration was also studied. During the whole flight campaign, 18 March was the most heavily polluted day with an Air Quality Index (AQI) of 175 and PM_2.5 of 131 µg m⁻³. The aerosol optical depth (AOD) reported by a sun photometer installed at the Funing ground station was 1.28. Compared to the other days, the CO₂ concentration measured by UGGA at different heights was the largest on 18 March with an average value of 422.59 ± 6.39 ppm, which was about 10 ppm higher than the measurements recorded on 16 March. Moreover, the vertical profiles of Orbiting Carbon Observatory-2 (OCO-2) and CarbonTracker were also compared with the aircraft measurements. All the datasets showed a similar variation with some differences in their CO₂ concentrations, which showing a good agreement among them.

The Atmospheric Environment Monitoring Satellite (AEMS), also called Daqi-1 or DQ-1, was launched in April 2022; one of its main payloads is a high-spectral-resolution lidar (HSRL) system. This new system enables the accurate measurements of global aerosol optical properties, which can be used in the geoscientific community after the retirement of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Developing a suitable retrieval algorithm and validating retrieved results are necessary. This research demonstrates a retrieval algorithm for aerosol optical properties using the DQ-1 HSRL system. This method has retrieved the aerosol linear depolarization ratio, backscatter coefficient, extinction coefficient, and optical depth. For validation purposes, we compared retrieved results with those obtained through CALIPSO. The results indicate that the profiles of the two datasets are in good agreement, with DQ-1 showing an improved signal-to-noise ratio (SNR). Optical property profiles from National Aeronautics and Space Administration (NASA) Micro-Pulse Lidar Network (MPLNET) stations were selected for validation with the DQ-1 measurements, resulting in a relative error of 25 %. Between June 2022 and December 2022, aerosol optical depth measurements using the DQ-1 satellite and the AErosol RObotic NETwork (AERONET) were correlated and yielded a value of R² equal to 0.803. We use the DQ-1 dataset to initially investigate the transport processes of the Saharan dust and the South Atlantic volcanic aerosols. These validations and applications show that the DQ-1 HSRL system can accurately measure global aerosols and has significant potential for Earth scientific applications.

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.

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.