15 citations as recorded by crossref.

1. A global climatology of ice-nucleating particles under cirrus conditions derived from model simulations with MADE3 in EMAC C. Beer et al.
2. Integrating Traditional and Artificial Intelligence Methods in Dust Aerosol Research: A Comprehensive Review T. Sha et al.
3. Distinct aerosol populations and their vertical gradients in central Amazonia revealed by optical properties and cluster analysis R. Valiati et al.
4. Transport patterns of global aviation NOx and their short-term O3 radiative forcing – a machine learning approach J. Maruhashi et al.
5. 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.
6. 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.
7. 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.
8. Composition and source based aerosol classification using machine learning algorithms S. Annapurna et al.
9. Enhancing Fine Aerosol Simulations in the Remote Atmosphere with Machine Learning M. Lu & C. Gao
10. 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.
11. Classification of global aerosol types and its radiative effects using Aerosol Robotic Network (AERONET) data S. Mukhopadhyay et al.
12. Spatiotemporal Analysis of NH3 Emission Sources and Their Relation to Land Use Types in the Eastern German Lowlands C. Saravia & K. Trachte
13. Aerosol classification by application of machine learning spectral clustering algorithm S. Ningombam et al.
14. A new aerosol type identification algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS) instrument F. Wang et al.
15. Cloud detection from Himawari-8 spectral images using K-means++ clustering with the convolutional module K. Wang et al.