Statistical properties of aerosols and meteorological factors in Southwest China

Abstract

The spatial and temporal variations of aerosols and their relationships with meteorological conditions in Southwest China were studied to understand the aerosol-meteorology interactions. The spatial distribution of aerosols shows a strong "basin" effect with the highest values of Aerosol Optical Thickness at 0.55 μm (AOT, also denoted τ 0.55 ) (>0.70), fine-mode AOT (τ 0.55-fine ) (0.40~0.67), and coarse-mode AOT (τ 0.55-coarse ) (0.40~0.54), all observed in the Sichuan Basin. The temporal variation of seasonal mean τ 0.55 shows dissimilar cyclic variations, which is due to the contrary annual cycle trends of seasonal mean τ 0.55-coarse (first decrease and then increase) and τ 0.55-fine (first increase and then decrease). Both the results obtained from self-organizing map (SOM) model and principal component analysis (PCA) could imply the reasons for different features of the spatial and temporal variations of τ 0.55 , τ 0.55-fine , and τ 0.55-coarse , and also confirm that their distributions are influenced by complex interactions among different meteorological factors. The seasonal mean τ 0.55 during the selected period is positively correlated with air pressure (PRS) and relative humidity (RHU) but negatively correlated with sunshine duration (SSD). The seasonal mean τ 0.55-fine is positively correlated with PRS, RHU, average temperature (TEM), and precipitation (PRE). However, the seasonal mean τ 0.55-coarse is only negatively correlated with PRE and evaporation (EVP). In particular, the τ 0.55-fine increases (decreases) and τ 0.55-coarse decreases (increases) with increasing (decreasing) TEM and PRE. This discrepant trend of τ 0.55-fine and τ 0.55-coarse might account for the weak relationships between τ 0.55 and these two meteorological factors.

A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches

“…Tai et al (2010), Shen et al (2018) all revealed that meteorological variables like temperature, relative humidity (RH), and wind speed explain much of the variations in PM 2.5 concentrations (> 50 %). Xiang et al (2019) and Gui et al (2019) found a negative association between planetary boundary layer height (PBLH) and PM 2.5 , and Kang et al (2014) found that fine-mode aerosols and air pressure were significantly correlated. In this study, to investigate the correlation between meteorological variables and the FMF, we implemented the generalized additive model (GAM).…”

Precipitation deficits increase high diurnal temperature range extremes

“…The observed daily precipitation and maximum and minimum temperature data used in this study was collected from the SURF_CLI_CHN_MUL_DAY_V3.0 dataset, which was downloaded from the China Meteorological Data Sharing Service System ( http://cdc.nmic.cn/home.do ). This dataset contains daily measurements of eight meteorological factors (air pressure, temperature, precipitation, evaporation, etc 25 ) from 824 stations from January 1951 to July 2014. According to the dataset information ( http://cdc.nmic.cn/datasets.do?…”