Articles | Volume 3, issue 2
https://doi.org/10.5194/ar-3-589-2025
https://doi.org/10.5194/ar-3-589-2025
Research article
 | 
28 Nov 2025
Research article |  | 28 Nov 2025

Global fields of daily accumulation-mode particle number concentrations using in situ observations, reanalysis data, and machine learning

Aino Ovaska, Elio Rauth, Daniel Holmberg, Paulo Artaxo, John Backman, Benjamin Bergmans, Don Collins, Marco Aurélio Franco, Shahzad Gani, Roy M. Harrison, Rakesh K. Hooda, Tareq Hussein, Antti-Pekka Hyvärinen, Kerneels Jaars, Adam Kristensson, Markku Kulmala, Lauri Laakso, Ari Laaksonen, Nikolaos Mihalopoulos, Colin O'Dowd, Jakub Ondracek, Tuukka Petäjä, Kristina Plauškaitė, Mira Pöhlker, Ximeng Qi, Peter Tunved, Ville Vakkari, Alfred Wiedensohler, Kai Puolamäki, Tuomo Nieminen, Veli-Matti Kerminen, Victoria A. Sinclair, and Pauli Paasonen

Viewed

Total article views: 1,107 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
961 113 33 1,107 50 34 73
  • HTML: 961
  • PDF: 113
  • XML: 33
  • Total: 1,107
  • Supplement: 50
  • BibTeX: 34
  • EndNote: 73
Views and downloads (calculated since 01 Jul 2025)
Cumulative views and downloads (calculated since 01 Jul 2025)

Viewed (geographical distribution)

Total article views: 1,107 (including HTML, PDF, and XML) Thereof 1,093 with geography defined and 14 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Nov 2025
Download
Short summary
We trained machine learning models to estimate the number of aerosol particles large enough to form clouds and generated daily estimates for the entire globe. The models performed well in many continental regions but struggled in remote and marine areas. Still, this approach offers a way to quantify these particles in areas that lack direct measurements, helping us understand their influence on clouds and climate on a global scale.
Share
Altmetrics
Final-revised paper
Preprint