Articles | Volume 3, issue 2
https://doi.org/10.5194/ar-3-589-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ar-3-589-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Nov 2025
Research article |
| 28 Nov 2025
| 28 Nov 2025
Global fields of daily accumulation-mode particle number concentrations using in situ observations, reanalysis data, and machine learning
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Elio Rauth
Department of Computer Science, University of Helsinki, Helsinki, Finland
Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, Germany
Daniel Holmberg
Department of Computer Science, University of Helsinki, Helsinki, Finland
Paulo Artaxo
Institute of Physics, University of São Paulo, São Paulo, Brazil
John Backman
Finnish Meteorological Institute (FMI), Erik Palménin aukio 1, 00560, Helsinki, Finland
Benjamin Bergmans
Institut Scientifique de Service Public (ISSeP), Liege, Belgium
Don Collins
University of California, Riverside, USA
Marco Aurélio Franco
Institute of Astronomy, Geophysics and Atmospheric Sciences, University of São Paulo, São Paulo, Brazil
Shahzad Gani
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India
Roy M. Harrison
School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
Rakesh K. Hooda
Finnish Meteorological Institute (FMI), Erik Palménin aukio 1, 00560, Helsinki, Finland
Tareq Hussein
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
University of Jordan, School of Science, Department of Physics, Environmental and Atmospheric Research Laboratory (EARL), Amman, 11942, Jordan
Antti-Pekka Hyvärinen
Finnish Meteorological Institute (FMI), Erik Palménin aukio 1, 00560, Helsinki, Finland
Kerneels Jaars
Atmospheric Chemistry Research Group, Chemical Resource Beneficiation, North-West University, Potchefstroom, South Africa
Adam Kristensson
Department of Physics, Lund University, Lund, Sweden
Markku Kulmala
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences, Nanjing University, Nanjing, China
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Lauri Laakso
Finnish Meteorological Institute (FMI), Erik Palménin aukio 1, 00560, Helsinki, Finland
Atmospheric Chemistry Research Group, Chemical Resource Beneficiation, North-West University, Potchefstroom, South Africa
Ari Laaksonen
Finnish Meteorological Institute (FMI), Erik Palménin aukio 1, 00560, Helsinki, Finland
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
Nikolaos Mihalopoulos
Department of Chemistry, University of Crete, Heraklion, Greece
Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Palaia Penteli, 15236 Athens, Greece
Colin O'Dowd
School of Physics, National University of Ireland, Galway, Ireland
Jakub Ondracek
Research Group of Aerosol Chemistry and Physics, Institute of Chemical Process Fundamentals of the CAS, Prague, Czech Republic
Tuukka Petäjä
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Kristina Plauškaitė
Center for Physical Sciences and Technology (FTMC), Vilnius, Lithuania
Mira Pöhlker
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Ximeng Qi
Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences, Nanjing University, Nanjing, China
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Peter Tunved
Department of Environmental Science, Stockholm University, Stockholm, Sweden
Ville Vakkari
Finnish Meteorological Institute (FMI), Erik Palménin aukio 1, 00560, Helsinki, Finland
Atmospheric Chemistry Research Group, Chemical Resource Beneficiation, North-West University, Potchefstroom, South Africa
Alfred Wiedensohler
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Kai Puolamäki
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Department of Computer Science, University of Helsinki, Helsinki, Finland
Tuomo Nieminen
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Department of Physics, University of Helsinki, Helsinki, Finland
Veli-Matti Kerminen
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Victoria A. Sinclair
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Pauli Paasonen
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, Helsinki, Finland
Data sets
Daily Averaged Accumulation Mode Particle Number Concentrations (N100) from 35 Stations (2003-2019) A. Ovaska, E. Rauth, D. Holmberg, P. Artaxo, J. Backman, B. Bergmans, D. Collins, M. A. Franco, S. Gani, R. M. Harrison, R. K. Hooda, T. Hussein, A. Hyvärinen, K. Jaars, A. Kristensson, M. Kulmala, L. Laakso, A. Laaksonen, N. Mihalopoulos, C. O'Dowd, J. Ondracek, T. Petäjä, K. Plauškaitė, M. Pöhlker, X. Qi, P. Tunved, V. Vakkari, A. Wiedensohler, K. Puolamäki, T. Nieminen, V.-M. Kerminen, V. A. Sinclair, and P. Paasonen https://doi.org/10.5281/zenodo.15222674
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.
We trained machine learning models to estimate the number of aerosol particles large enough to...
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