Developing Global Aerosol Models based on the Analysis of 30-Year Ground Measurements by AERONET (AEROEX models) and Implication on Satellite based Aerosol Retrievals

TL;DR

This study develops refined global aerosol models from 30 years of AERONET data, enhancing satellite aerosol retrieval accuracy and providing detailed regional and seasonal aerosol type classifications.

Contribution

It introduces the AERONET-Extended (AEROEX) models based on extensive cluster analysis, improving aerosol classification and satellite retrievals over previous shorter-term models.

Findings

AERONET-Extended models outperform previous models in satellite aerosol retrievals.

Regional and seasonal aerosol variations are clearly characterized.

Global aerosol maps at 1x1 degree resolution are generated for climate studies.

Abstract

The AErosol RObotic NETwork (AERONET), established in 1993 with limited global sites, has grown to over 900 locations, providing three decades of continuous aerosol data. While earlier studies based on shorter time periods (10-12 years) and fewer sites (approximately 250) made significant contributions to aerosol research, the vast AERONET dataset (1993-2023) calls for a comprehensive reevaluation to refine global aerosol models and improve satellite retrievals. This is particularly important in light of major environmental changes such as industrialization, land use shifts, and natural events like wildfires and dust storms. In this study, a set of fine and coarse aerosol models called AERONET-Extended (AEROEX) models are developed based on cluster analysis of 30-years AERONET data, analyzing over 202,000 samples using Gaussian Mixture Models to classify aerosol types by season and region. Aerosols are categorized into spherical, spheroidal, and mixed types using particle linear depolarization ratio and fine mode fraction. Four fine-mode aerosol models were derived based on differences in scattering and absorption properties, revealing regional/seasonal variations, particularly in North America, Europe and Asia. Additionally, two coarse-mode aerosol models were identified, separated by their absorbing properties in dust-prone and polluted regions. We performed simulation analysis showing that the new models significantly improve satellite-based aerosol optical depth retrievals compared to widely used dark target aerosol models. A global aerosol model map, generated at 1x1 degree resolution for each season using Random Forest and expert refinement, provides valuable insights for climate and atmospheric studies, improving satellite-based aerosol retrievals at global scale.