Using NASA MERRA2 daily data, we investigated the origin, maintenance and variability of the Asian Tropopause Aerosol Layer (ATAL) in relation to variations of the Asia Monsoon Anticyclone (AMA) during the summer of 2008. During May-June, abundant quantities of carbon monoxide (CO), carbonaceous aerosols (CA) and dusts are found in the mid- and upper troposphere over India and China, arising from enhanced biomass burning emissions, as well as westerly transport from the Middle East deserts. During July-August, large quantities of dusts transported from the deserts are trapped and accumulate over the southern and eastern foothills of the Tibetan Plateau. Despite strong precipitation washout, ambient CO, CA and dust are lofted by orographically forced deep convection to great elevations, 12–16 km above sea level, via two key pathways over heavily polluted regions: a) the Himalayas-Gangetic Plain, and b) the Sichuan Basin. Upon entering the upper-troposphere-lower-stratosphere, the pollutants are capped by a stable layer near the tropopause, advected and dispersed by the anticyclonic circulation of AMA, forming the ATAL resembling a planetary-scale “double-stem chimney cloud”. The development and variability of the ATAL are strongly linked to the seasonal march and intraseasonal (20–30 days and higher frequency) oscillations of the Asian monsoon.

In this study, we have investigated the interannual variability and the decadal trend of carbon monoxide (CO), carbonaceous aerosols (CA) and mineral dust in the Asian Tropopause Aerosol Layer (ATAL) in relation to varying strengths of the South Asian summer monsoon (SASM) using MERRA-2 reanalysis data (2001–2015). Results show that during this period, the aforementioned ATAL constituents exhibit strong interannual variability and rising trends connected to the variations of the strength of SASM. During strong monsoon years, the Asian monsoon anticyclone (AMA) is more expansive and shifted northward compared to weak years. In spite of the effect of quenching of biomass burning emissions of CO and CA by increased precipitation, as well as the removal of CA and dust by increased washout from the surface to the mid-troposphere in monsoon regions, all three constituents are found to be more abundant in an elongated accumulation zone in the ATAL, on the southern flank of the expanded AMA. Enhanced transport to the ATAL by overshooting deep convection is found over preferred pathways in the Himalayan-Gangetic Plain (HGP) and the Sichuan Basin (SB). The long-term positive trends of ATAL CO and CA are robust, while the ATAL dust trend is weak due to its large interannual variability. The ATAL trends are associated with increasing strength of the AMA, with earlier and enhanced vertical transport of ATAL constituents by enhanced overshooting convection over the HGP and SB regions, outweighing the strong reduction of CA and dust from the surface to the mid-troposphere.

Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have rapidly emerged as a research hotspot in atmospheric science. This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model’s characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. Furthermore, the paper addresses critical challenges in the field, including dataset quality, algorithm optimization, and practical application barriers, proposing future research directions, such as the development of large-scale, accurately labeled datasets, innovative learning strategies, and enhanced model interpretability. These findings highlight the potential of deep learning in enhancing atmospheric visibility detection techniques, providing valuable insights into the literature and contributing to advances in the field of meteorological observation and public safety.