In this paper, we present comprehensive night-time seeing statistics at the Lenghu site using data collected between 2018 and 2021 using a differential image motion monitor. The results show that the median seeing is 0.80 arcsec, which slightly larger than that previously reported due to climate change. Additionally, we investigated the annual, seasonal, and monthly seeing statistics, as well as possible hourly seeing trends in each season. The results demonstrate that the seeing at the Lenghu site is very stable, with monthly median seeing varying from 0.6 arcsec to 0.95 arcsec. The seasonal dependence of seeing shows obvious variations over different years. The best year of seeing was 2019 (0.74 arcsec), and the best seeing occurred in spring (0.68 arcsec) to early summer (June, 0.63 arcsec), while the worst seeing occurred in winter (0.80 arcsec). The minimum seeing value throughout the night is at approximately three hours before dawn. Furthermore, wind speed profiles, especially V₂₀₀ (particularly the wind speed at 200 mbar), are important parameters for evaluating astronomical sites in terms of their suitability for adaptive optics. We first show the complete monthly average behaviours of the wind speed profile and V₂₀₀ using the European Center for Medium-Range Weather Forecast reanalysis datasets. The results demonstrate the annual periodicity and variability of V₂₀₀, and the yearly V₂₀₀ modulation function is also provided. V₂₀₀ maintains large values throughout the autumn (average:35.01 m/s) and winter (average:34.33 m/s), while the minimum V₂₀₀ occurs in spring (average:27.93 m/s).

The aerosol hygroscopic growth (HG) characteristics in coastal areas are very complex, which is one of the main influences on the simulation accuracy of radiation transfer modeling for coastal environments. Previous studies have shown that aerosol HG characteristics are very different in open oceans and inland regions. However, the aerosol HG features in coastal areas are strongly affected by its type. In this work, an aerosol backward trajectory tracing model was used to classify the local aerosol type. Using long-term field campaign data in Qingdao (25 September 2019 to 25 October 2020), the HG characteristics of different types of aerosols (i.e., land source, sea source, and mixed aerosol) under different seasons and different atmospheric environments (i.e., pollution background and clean background) were studied. Quantitative models of aerosol HG factor were established for aerosols from different sources in different seasons and under different pollution background conditions. The major type of local aerosol is terrestrial aerosol, as the marine source only accounts for 10–20%. Seasonal HG characteristics (deliquescence point, DP) of mixed and land source aerosol vary significantly, from around RH = 60% to RH = 85%, while that of the marine aerosol is rather consistent (RH = 80%). When the atmospheric background is relatively clean, the DPs of aerosols from different sources are almost the same (about RH = 80%), but when the pollution is heavy, the DPs of terrestrial aerosols are almost 20% lower than those of marine sources. These models can be directly used to characterize the hygroscopic characteristics of atmospheric aerosols in Qingdao at specific seasons or pollution levels for radiative transfer modeling, remote sensing, and so forth.

Random fluctuations of turbulence bring random fluctuations of the refractive index, making the atmosphere a random fluctuation medium that destroys the coherence of light-waves. Research in atmospheric turbulence is actually the investigation of the atmospheric refractive index. The atmospheric structure constant of refractive index, C_n², is an important parameter denoting atmospheric turbulence. In this paper, C_n² is measured during the day and at night and in all four seasons using a high sensitivity micro-thermal meter QHTP-2. The vertical profile of C_n² in Hefei (0–30 km) is investigated by the analysis of experimental data. The average profile of Cn2 in Hefei exhibits conspicuous day and night differences with increased altitude. The distribution of log(C_n²) is nearly normal and has conspicuous seasonal differences.

Marine atmospheric boundary-layer height (MABLH) is crucial for ocean heat, momentum, and substance transfer, affecting ocean circulation, climate, and ecosystems. Due to the unique geographical location of the South China Sea (SCS), coupled with its complex atmospheric environment and sparse ground-based observation stations, accurately determining the MABLH remains challenging. Coherent Doppler wind lidar (CDWL), as a laser-based active remote sensing technology, provides high-resolution wind profiling by transmitting pulsed laser beams and analyzing backscattered signals from atmospheric aerosols. In this study, we developed a stacking optimal ensemble model (SOEM) to estimate MABLH in the vicinity of the site by integrating CDWL measurements from a representative SCS site with ERA5 (fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts) data from December 2019 to May 2021. Based on the categorization of the total cloud cover data into weather conditions such as clear/slightly cloudy, cloudy/transitional, and overcast/rainy, the SOEM demonstrates enhanced performance with an average mean absolute percentage error of 3.7%, significantly lower than the planetary boundary-layer-height products of ERA5. The SOEM outperformed random forest, extreme gradient boosting, and histogram-based gradient boosting models, achieving a robustness coefficient ($R^2$) of 0.95 and the lowest mean absolute error of 32 m under the clear/slightly cloudy condition. The validation conducted in the coastal city of Qingdao further confirmed the superiority of the SOEM in resolving meteorological heterogeneity. The predictions of the SOEM aligned well with CDWL observations during Typhoon Sinlaku (2020), capturing dynamic disturbances in MABLH. Overall, the SOEM provides a precise approach for estimating convective boundary-layer height, supporting marine meteorology, onshore wind power, and coastal protection applications.

In this study, we devised a constraint method, called multi-model ensemble pattern (MEP), to estimate the refractive index structure parameter ($C_n^2$) profiles based on observational data and multiple existing models. We verified this approach against radiosonde data from field campaigns in China’s eastern and northern coastal areas. Multi-dimensional statistical evaluations for the $C_n^2$ profiles and integrated astronomical parameters have proved MEP’s relatively reliable performance in estimating optical turbulence in the atmosphere. The correlation coefficients of MEP and measurement overall $C_n^2$ in two areas are up to 0.65 and 0.76. A much higher correlation can be found for a single radiosonde profile. Meanwhile, the difference evaluation of integrated astronomical parameters also shows its relatively robust performance compared to a single model. The prowess of this reliable approach allows us to carry out regional investigation on optical turbulence features with routine meteorological data soon.

The refractive index structure constant $C_n^2$ near the ocean surface is an important parameter for studying atmospheric optical turbulence over the ocean. The measured refractive index structure constant and meteorological parameters, such as temperature and three-dimensional wind speed, near the sea surface on the South China Sea during the period from January to November 2020 were analyzed. On the basis of Monin–Obukhov similarity theory, the dimensionless temperature structure parameter function $f_T$ near the sea surface was established, and a new parameterized model of the near-sea surface was proposed. The new model improved the error of the widely used model proposed by Wyngaard in 1973 (W73) and better reproduced the daily variation in the measured $C_n^2$. Further analysis of the seasonal applicability of the new model indicated that the correlation coefficients between the estimated and measured $C_n^2$ in the spring, summer, autumn, and winter were 0.94, 0.94, 0.95, and 0.89, respectively, and the root mean square errors were 0.32, 0.41, 0.46, and 0.40 m^−2/3, respectively. Compared with the $C_n^2$ estimated by the W73 model, the correlation coefficient of $C_n^2$ estimated by the new model and measured by the micro-thermometer increased by 0.05–0.27 and the root mean square error decreased by 0.04–0.56. The improved $f_T$ demonstrated higher accuracy than the existing models, which can lay a foundation for estimating turbulence parameters in different sea areas.