Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-509-2022
https://doi.org/10.5194/gmd-15-509-2022
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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Cited articles

Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. 
Amorim, R. C. D. and Hennig, C: Recovering the number of clusters in data sets with noise features using feature rescaling factors, Inform. Sciences, 324, 126–145, https://doi.org/10.1016/j.ins.2015.06.039, 2015. 
Aquila, V., Hendricks, J., Lauer, A., Riemer, N., Vogel, H., Baumgardner, D., Minikin, A., Petzold, A., Schwarz, J. P., Spackman, J. R., Weinzierl, B., Righi, M., and Dall'Amico, M.: MADE-in: a new aerosol microphysics submodel for global simulation of insoluble particles and their mixing state, Geosci. Model Dev., 4, 325–355, https://doi.org/10.5194/gmd-4-325-2011, 2011. 
Bauer, S. E., Wright, D. L., Koch, D., Lewis, E. R., McGraw, R., Chang, L.-S., Schwartz, S. E., and Ruedy, R.: MATRIX (Multiconfiguration Aerosol TRacker of mIXing state): an aerosol microphysical module for global atmospheric models, Atmos. Chem. Phys., 8, 6003–6035, https://doi.org/10.5194/acp-8-6003-2008, 2008. 
Beer, C. G.: Model simulation data used in “Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54” (Beer et al., Geosci. Model Dev., 2020), Zenodo [data set], https://doi.org/10.5281/zenodo.3941462, 2020. 
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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.