Articles | Volume 20, issue 21
Atmos. Chem. Phys., 20, 12853–12869, 2020
https://doi.org/10.5194/acp-20-12853-2020
Atmos. Chem. Phys., 20, 12853–12869, 2020
https://doi.org/10.5194/acp-20-12853-2020

Research article 05 Nov 2020

Research article | 05 Nov 2020

Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

Arshad Arjunan Nair and Fangqun Yu

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AR by Arshad Nair on behalf of the Authors (23 Aug 2020)  Author's response    Manuscript
ED: Publish as is (30 Sep 2020) by Gordon McFiggans
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Short summary
Small particles in the atmosphere can affect cloud formation and properties and thus Earth's energy budget. These cloud condensation nuclei (CCN) contribute the largest uncertainties in climate change modeling. To reduce these uncertainties, it is important to quantify CCN numbers accurately, measurements of which are sparse. We propose and evaluate a machine learning method to estimate CCN, in the absence of their direct measurements, using more common measurements of weather and air quality.
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