Aeolus wind products became available to the public on 12 May 2020. In this study, Aeolus wind observations, L-band radiosonde (RS) data, and the European Centre for Medium-Range Weather Forecasts fifth-generation atmospheric reanalysis (ERA5) data were used to analyze the seasonality of Aeolus wind product performance over China. Based on the Rayleigh-clear and Mie-cloudy data, the data quality of the Aeolus effective detection data was verified, and the results showed that the Aeolus data were in good agreement with the L-band RS and ERA5 data. The Aeolus data relative errors in the four regions (Chifeng, Baoshan, Shapingba, and Qingyuan) in China were calculated based on different months (July to December 2019 and May to October 2020). The relative error in the Rayleigh-clear data in summer was significantly higher than that in winter, with the mean relative error parameter in July 174 % higher than that in December. The mean random error increased by 0.97 m s⁻¹ in July compared with December, which also supported this conclusion. In addition, the distribution of the wind direction and high-altitude clouds in different months (July and December) was analyzed. The results showed that the distribution of the angle between the horizontal wind direction of the atmosphere and the horizontal line of sight had a greater proportion in the high error interval (70–110^∘) in summer, and this proportion was 8.14 % higher in July than in December. The cloud top height in summer was approximately 3–5 km higher than that in winter, which might decrease the signal-to-noise ratio of Aeolus. Therefore, the wind product performance of Aeolus was affected by seasonal factors, which might be caused by seasonal changes in wind direction and cloud distribution.

Accurate and rapid spectrum fitting is very important for quantitatively analyzing laser-induced breakdown spectroscopy (LIBS). The Voigt function is often used to fit LIBS spectral lines. We propose a new approximate Voigt function formula. Based on the classic Lorentz–Gauss linear combination formula, a summation term was added that contained a specific convolution operation to improve the Voigt function’s calculation and fitting accuracy. This formula can be used for the approximate calculation of the Voigt function with an overall accuracy of 0.31% and a full width at half-maximum internal accuracy of 0.25% when the ratio of Lorentzian linewidth to Gaussian linewidth is 1:1. The formula was then applied to LIBS data processing to fit four element spectral lines of calcium (Ca-393.37, 396.85, and 422.67 nm) and potassium (K-766.49 nm). The fitting results showed that this new approximate formula could fit at least seven data points, and compared with the complex plane partition method and the classic linear combination formula, the new formula had better fitting speed and accuracy.

The remote sensing of water clouds is useful for studying their spatial and temporal variations and constraining physical processes in climate and weather prediction models. However, radar-only detection provides inadequate information for the cloud droplet size distribution. Here, we propose a novel lookup-table method, which combines lidar (1064, 532 nm) and radar (8.6 mm) to retrieve profiles of cloud optical (backscatter coefficient and extinction coefficient) and microphysical properties (effective diameter and liquid water content). Through the iteration of the extinction-to-backscatter ratio, more continuous cloud optical characteristics can be obtained. Sensitivity analysis shows that a 10% error of the lidar constant will lead to a retrieval error of up to 30%. The algorithm performed precise capture of the ideal cloud signal at a specific height and at full height and the maximum relative error of the backscatter coefficients at 1064 nm and 532 nm were 6% and 4%, respectively. With the application of the algorithm in the two observation cases on single or multiple cloud layers, the results indicate that the microphysical properties mostly agree with the empirical radar measurements but are slightly different when larger particles cause signal changes of different extents. Consequently, the synergetic algorithm is capable of computing the cloud droplet size distribution. It provides continuous profiles of cloud optical properties and captures cloud microphysical properties well for water cloud studies.

Complexly distributed aerosol particles have significant impacts on climate and environmental changes. As one of the vital atmospheric power sources, the wind field deeply affects the distribution and transport of aerosol particles. For a more comprehensive investigation of the aerosols flux and transport mechanism, two retrieval methods of aerosol optical properties (backscatter coefficient and extinction coefficient at 1550 nm) from coherent Doppler lidar (CDL) observation are proposed in this paper. The first method utilizes the calculated aerosol backscatter coefficient (532 nm) from Mie-scattering lidar datasets and the iterative Fernald method to retrieve aerosol optical property profiles during joint measurements with CDL and Mie-scattering lidar. After verifying the correctness of the first method compared with AERONET datasets, we proposed the second retrieval method. Using the forward integral Fernald method with near-ground reference aerosol extinction coefficient calculated by atmospheric visibility, aerosol optical properties at 1550 nm could be obtained. Thirty-six-day joint measurements with two lidars were specially designed and conducted to verify the correctness of these retrieval methods. The validation results of these two methods indicate great performances, where the mean relative errors are 0.0272 and 0.1656, and the correlation coefficients are 0.9306 and 0.9197, respectively. In conclusion, the feasibility of these two retrieval methods extends the capability of CDL to detect aerosol optical properties and also provides a possibility to observe the aerosol distribution and transport process comprehensively, which is a great promotion of aerosol transport studies development.

Although it is quite challenging to image and analyze the spatial distribution of bioaerosols in a confined space, a three-dimensional (3D) modeling system based on the planar laser-induced fluorescence (PLIF) technique is proposed in this paper, which is designed to analyze the temporal and spatial variations of bioaerosol particles in a confined chamber. The system employs a continuous planar laser source to excite the fluoresce, and a scientific complementary metal oxide semiconductor (sCMOS) camera to capture images of 2048 × 2048 pixels at a frame rate of 12 Hz. While a sliding platform is moving back and forth on the track, a set of images are captured at different positions for 3D reconstruction. In this system, the 3D reconstruction is limited to a maximum measurement volume of about 50 cm × 29.7 cm × 42 cm, with a spatial resolution of about 0.58 mm × 0.82 mm × 8.33 mm, and a temporal resolution of 5 s. Experiments were carried out to detect the PLIF signals from fluorescein aerosols in the chamber, and then 3D reconstruction was used to visualize and analyze the diffusion of aerosol particles. The results prove that the system can be applied to clearly reconstruct the 3D distribution and record the diffusion process of aerosol particles in a confined space.