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.
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.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
25 Jan 2022
Methods for assessment of models |
| 25 Jan 2022
| 25 Jan 2022
An aerosol classification scheme for global simulations using the K-means machine learning method
Jingmin Li
CORRESPONDING AUTHOR
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Johannes Hendricks
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Mattia Righi
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Christof G. Beer
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
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22 citations as recorded by crossref.
- A global climatology of ice-nucleating particles under cirrus conditions derived from model simulations with MADE3 in EMAC C. Beer et al. https://doi.org/10.5194/acp-22-15887-2022
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- Distinct aerosol populations and their vertical gradients in central Amazonia revealed by optical properties and cluster analysis R. Valiati et al. https://doi.org/10.5194/acp-25-14923-2025
- Transport patterns of global aviation NOx and their short-term O3 radiative forcing – a machine learning approach J. Maruhashi et al. https://doi.org/10.5194/acp-22-14253-2022
- Improvements on Gaussian mixture model and its application in identifying aerosol types in two major cities in the Yangtze River Delta, China J. Wang et al. https://doi.org/10.1016/j.scitotenv.2024.172743
- Water vapor and energy sources of persistent extreme heavy rainfall related to typhoon over the Huaihe River basin J. Yu et al. https://doi.org/10.1016/j.ejrh.2026.104108
- Methodology to modify and adapt the standardised spectral power distributions for daylight to account for geographical, seasonal and diurnal variations for practical applications M. Knoop et al. https://doi.org/10.1177/14771535251322386
- Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations J. Li et al. https://doi.org/10.5194/acp-24-12727-2024
- Composition and source based aerosol classification using machine learning algorithms S. Annapurna et al. https://doi.org/10.1016/j.asr.2023.09.068
- Enhancing Fine Aerosol Simulations in the Remote Atmosphere with Machine Learning M. Lu & C. Gao https://doi.org/10.3390/atmos15111356
- Data clustering to optimise the representativity of observational data in air quality data assimilation: a case study with EURAD-IM (version 5.9.1 DA) A. Hermanns et al. https://doi.org/10.5194/gmd-18-9417-2025
- The evolution of aerosol types and direct radiative forcing in Beijing-Tianjin-Hebei, China: A long-term analysis with a modified Gaussian mixture model J. Wang et al. https://doi.org/10.1016/j.atmosres.2026.109072
- Classification of global aerosol types and its radiative effects using Aerosol Robotic Network (AERONET) data S. Mukhopadhyay et al. https://doi.org/10.1016/j.atmosenv.2025.121530
- A Novel Framework Using Unsupervised Machine Learning for Aerosol Classification: A Case Study of North Indian AERONET Sites V. Hole et al. https://doi.org/10.1007/s12524-026-02508-9
- Seasonal and interannual variability of atmospheric ammonia over Guatemala driven by land use, biomass burning, and meteorological circulation C. Saravia et al. https://doi.org/10.5194/acp-26-12111-2026
- Spatiotemporal Analysis of NH3 Emission Sources and Their Relation to Land Use Types in the Eastern German Lowlands C. Saravia & K. Trachte https://doi.org/10.3390/atmos16030346
- Aerosol classification by application of machine learning spectral clustering algorithm S. Ningombam et al. https://doi.org/10.1016/j.apr.2023.102026
- A new aerosol type identification algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS) instrument F. Wang et al. https://doi.org/10.1080/20964471.2026.2654984
- Season-Specific Spatial Organization of Global Aerosol Optical Depth from 25 Years of MODIS Observations Revealed by Nonnegative Matrix Factorization W. Hou et al. https://doi.org/10.3390/rs18193292
- Diurnal Variations in CO2 and CH4 Concentrations Based on Measurements in the Middle Urals A. Luzhetskaya et al. https://doi.org/10.1134/S0001433826700969
- Cloud detection from Himawari-8 spectral images using K-means++ clustering with the convolutional module K. Wang et al. https://doi.org/10.1080/01431161.2024.2305625
- A hybrid AI framework for aerosol type classification and 10-year forecasting using composition and source-based features A. Kukkar et al. https://doi.org/10.3389/fclim.2026.1801377
Saved (final revised paper)
Latest update: 04 Nov 2026
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.
The growing complexity of global aerosol models results in a large number of parameters that...
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