Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 1
Articles | Volume 26, issue 1
https://doi.org/10.5194/acp-26-59-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-59-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 1
Research article
 | 
05 Jan 2026
Research article |  | 05 Jan 2026

Machine learning reveals strong grid-scale dependence in the satellite Nd–LWP relationship

Matthew W. Christensen, Andrew Geiss, Adam C. Varble, and Po-Lun Ma

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Short summary
We used satellite data and machine learning to better understand how tiny particles in the atmosphere affect clouds and their brightness. At higher spatial resolution, we discovered a new “M”-shaped pattern in the relationship between cloud water and droplet concentration unlike the inverted-V shape observed at coarsely gridded scales. Cloud water increases more with droplet concentration when rain is present. These findings support the development of next-generation atmospheric models.
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