Abstract
Aerosol type is a critical piece of information in both aerosol forcing estimation and passive satellite remote sensing. However, the major aerosol types in China and their variability is still less understood. This work uses direct sun measurements and inversion derived parameters from 47 sites within the Aerosol Robotic Network (AERONET) in China, with more than 39,000 records obtained between April 1998 and January 2017, to identify dominant aerosol types using two independent methods, namely, K means and Self Organizing Map (SOM). In total, we define four aerosol types, namely, desert dust, scattering mixed, absorbing mixed and scattering fine, based on their optical and microphysical characteristics. Seasonally, dust aerosols mainly occur in the spring and over North and Northwest China; scattering mixed are more common in the spring and summer, whereas absorbing aerosols mostly occur in the autumn and winter during heating period, and scattering fine aerosols have their highest occurrence frequency in summer over East China. Based on their spatial and temporal distribution, we also generate seasonal aerosol type maps that can be used for passive satellite retrieval. Compared with the global models used in most satellite retrieval algorithms, the unique feature of East Asian aerosols is the curved single scattering albedo spectrum, which could be related to the mixing of black carbon with dust or organic aerosols.
Highlights
In recent decades, with its rapid economic development, China has been suffering from severe air pollution and has become the focus of global aerosol study
Tao et al [14] compared MODIS aerosol retrievals over China with ground observation and showed that Dark Target (DT) retrievals tended to overestimate the aerosol loading while Deep Blue (DB) retrievals exhibited obvious underestimation in northwestern and southern China
We def6inoef 1a6 distance threshold as the longest distance from the centroid of each type, i.e., if the shortest distance between a data point and a centroid is still greater than the distance threshold of this type, this data igsivneont cinlatshseifipeadreanntdheisisthoufsTdabislcea3rdinedp.aIrnaltlhelistowtahye,Kwme ecalansssirfeiesdult3s9.,2W4e9 ndoattiacerethcoartdths ebryesduisltcsarodf itnhge otwnloyt4e7chrneciqourdes.are almost identical, which increases the credibility of the clustering results
Summary
With its rapid economic development, China has been suffering from severe air pollution and has become the focus of global aerosol study. It is important to understand the major types of aerosols in China and their spatial-temporal variability, in order to better estimate their climate and environmental effects. Many satellite aerosol products, such as those from MODIS and VIIRS, still have large uncertainties in China. Li et al [13] evaluated the VIIRS AOD product over mainland China and found it had an overall high bias of 0.13 compared with ground measurements. Tao et al [14] compared MODIS aerosol retrievals over China with ground observation and showed that Dark Target (DT) retrievals tended to overestimate the aerosol loading while Deep Blue (DB) retrievals exhibited obvious underestimation in northwestern and southern China. Zhu et al [16] found VIIRS AOD overestimated more over the North China Plain region, and AOD bias in Beijing was the largest. One of the reasons for these uncertainties is that assumption of aerosol type is not accurate
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