To improve the accuracy of atmospheric visibility (V) prediction based on machine learning in different pollution scenarios, a new atmospheric visibility prediction method based on the stacking fusion model (VSFM) is established in this paper. The new method uses the stacking strategy to fuse two base learners—eXtreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM)—to optimize prediction accuracy. Furthermore, seasonal feature importance evaluations and feature selection were utilized to optimize prediction accuracy in different seasons with different pollution sources. The new VSFM was applied to 1-year environmental and meteorological data measured in Qingdao, China. Compared to other traditional non-stacking models, the new VSFM improved precision during different seasons, especially in extremely low-visibility scenarios (V< 2 km). The TS score of the VSFM was significantly better than that of other models. For extremely low-visibility scenarios, the VSFM had a threat score (TS) of 0.5, while the best performance of other models was less than 0.27. The new method is promising for atmospheric visibility prediction under complex urban pollution conditions. The research results can also improve our understanding of the factors that influence urban visibility.

Cloud occurrences, vertical structures, and along-track horizontal scales over East Asia are studied using four years (2007–2010) of CloudSat 2B-CLDCLASS-LIDAR data. The CloudSat 2B-CLDCLASS-LIDAR data employs combined CloudSat radar and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to provide by far the most accurate detections of cloud boundaries and their vertical structures. The mean cloud occurrence frequency over East Asia is 66.3%, which is 13.8% and 21.6% higher than that from the Cloud–Aerosol LIdar with Orthogonal Polarization (CALIOP) level 2 5-km cloud layer product and the CloudSat 2B-GEOPROF product, respectively. Cloud-top heights over East Asia show three local peaks at approximately 1.5 km, 10 km, and 15 km above ground level (AGL), indicating different mid-altitude cloud formation mechanisms from those over the tropics. Significant fractions of low-level cloud, mid-level cloud, and high-level cloud have thicknesses smaller than 0.5 km, indicating that models with vertical resolutions lower than 0.5 km have difficulties resolving those clouds. The average cloud along-track horizontal scale over East Asia is 82.0 km. Probability distribution functions (PDFs) of cloud along-track horizontal scales suggest that approximately 81.2% of the clouds over East Asia cannot be resolved by climate models with a grid resolution of 1°. The results from this study can be used to improve cloud parameterizations in climate models and validate model simulations of clouds over East Asia.

The high elevation, complex topography, and unique atmospheric circulations of the Tibetan Plateau (TP) make its optical turbulence characteristics different from those in low-elevation regions. In this study, the characteristics of the atmospheric refractive index structure constant ($C_n^2$) profiles in the Lhasa area at different strength states of the Asian summer monsoon anticyclone (ASMA) are analyzed based on precious in situ sounding data measured over the Lhasa in August 2018. $C_n^2$ in the upper troposphere–lower stratosphere fluctuates significantly within a few days during the ASMA, particularly in the upper troposphere. The effect of the ASMA on $C_n^2$ varies among the upper troposphere, tropopause, and lower stratosphere. The stronger and closer the ASMA is to Lhasa, the more pronounced is the “upper highs and lower lows” pressure field structure, which is beneficial for decreasing the potential temperature lapse rate. The decrease in static stability is an important condition for developing optical turbulence, elevating the tropopause height, and reducing the tropopause temperature. However, if strong high-pressure activity occurs at the lower pressure layer, such as at 500 hPa, an “upper highs and lower highs” pressure field structure forms over the Lhasa, increasing the potential temperature lapse rate and suppressing the convective intensity. Being almost unaffected by low-level atmospheric high-pressure activities, the ASMA, as the main influencing factor, mainly inhibits $C_n^2$ in the tropopause and lower stratosphere. The variations of turbulence intensity in UTLS caused by ASMA activities also have a great influence on astronomical parameters, which will have certain guiding significance for astronomical site testing and observations.

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.