Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-509-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-509-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
Methods for assessment of models
 | 
25 Jan 2022
Methods for assessment of models |  | 25 Jan 2022

An aerosol classification scheme for global simulations using the K-means machine learning method

Jingmin Li, Johannes Hendricks, Mattia Righi, and Christof G. Beer

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Latest update: 11 Dec 2024
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Short summary
The growing complexity of global aerosol models results in a large number of parameters that describe the aerosol number, size, and composition. This makes the analysis, evaluation, and interpretation of the model results a challenge. To overcome this difficulty, we apply a machine learning classification method to identify clusters of specific aerosol types in global aerosol simulations. Our results demonstrate the spatial distributions and characteristics of these identified aerosol clusters.
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