The Lagged Effect of Anthropogenic Aerosol on East Asian Precipitation during the Summer Monsoon Season

Abstract

The authors investigated the lagged effect of anthropogenic aerosols (AAs) during the premonsoon season (April–May–June) on the East Asian precipitation during the postmonsoon season (July–August) using the aerosol optical depth (AOD) from a satellite dataset and reanalysis datasets. When the AOD is high in Eastern China during the premonsoon season, the amount of precipitation increases in the western North Pacific, including the Korean Peninsula and Japan, during the postmonsoon season. The amount of cloud in the western-to-central North Pacific in the premonsoon season increases during the high-AOD period. Subsequently, it cools the sea surface temperature until the postmonsoon season, which strengthens the North Pacific High. The strengthened North Pacific High in the postmonsoon season expands to the western North Pacific, which leads to the enhancement of the moisture flows from the ocean. This results in the increase in precipitation in the western North Pacific, including the Korean Peninsula and Japan, during the postmonsoon season.

Spatiotemporal Variations of Aerosols in China during the COVID-19 Pandemic Lockdown

“…In this study, Hubei Province in China was used as our experimental area, and Himawari-8 satellite images were used as experimental data to analyze the change in AOD during the initial outbreak of COVID-19 and its relationship with human activities. However, two difficulties must be overcome before data analysis: (1) the Himawari-8 AOD algorithm has great uncertainty, and the results of the optimization algorithm of Wang et al are experimental data [27,28]; (2) the temporal and spatial distribution of aerosol optical depth is influenced by many complex factors such as human activities, temperature, and precipitation [29][30][31][32], the AOD in satellite image acquisition is only one component in the overall change rule, so long-term experimental data are mixed with discontinuous and abnormal values, significantly challenging data analysis. Here, we use E3D-LSTM to predict and analyze the long time-series data, correct the obvious errors in the raster data and make up for disordered missing values.…”