Abstract
A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles.
Highlights
Received: 30 December 2020Atmospheric aerosols are known to have both direct and indirect effects on Earth’s climate since aerosols scatter and absorb solar radiation and affect cloud microphysical properties [1]
When aerosols were classified into seven types (PD, dust dominant mixed (DDM), pollution dominated mixture (PDM), strongly absorbing (SA), Moderately absorbing (MA), Weakly absorbing (WA), and NA), the overall accuracy (OA) of the random forest (RF)-based model was 59%
It turns out our aerosol classifiers have difficulty distinguishing the seven aerosol types with suitable classification performance, especially between the pollution-related aerosols (MA, WA, NA, and PDM)
Summary
Atmospheric aerosols are known to have both direct and indirect effects on Earth’s climate since aerosols scatter and absorb solar radiation and affect cloud microphysical properties [1]. The diverse and significant effects of aerosols on climate play a critical role in radiative forcing, which has high uncertainty [2]. To estimate aerosol radiative forcing, aerosol type has been identified as the important parameters because aerosol properties, such as radiation absorptivity and particle size, differ among aerosol types [3,4]. The aerosol type is an input parameter of satellite aerosol retrieval algorithms and affects their accuracy [5,6]. Several satellite aerosol-type classification algorithms have been developed based on the threshold approach. Higurashi and Nakajima [3] developed a four-channel algorithm (4CA) to detect aerosol types using four-channel data from Sea-viewing Wide Field of
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