Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 3
Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1821-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1821-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 3
Development and technical paper
 | 
01 Apr 2021
Development and technical paper |  | 01 Apr 2021

Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0

Dana L. McGuffin, Yuanlong Huang, Richard C. Flagan, Tuukka Petäjä, B. Erik Ydstie, and Peter J. Adams

Viewed

Total article views: 2,352 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,656 631 65 2,352 271 93 133
  • HTML: 1,656
  • PDF: 631
  • XML: 65
  • Total: 2,352
  • Supplement: 271
  • BibTeX: 93
  • EndNote: 133
Views and downloads (calculated since 02 Sep 2020)
Cumulative views and downloads (calculated since 02 Sep 2020)

Viewed (geographical distribution)

Total article views: 2,352 (including HTML, PDF, and XML) Thereof 2,143 with geography defined and 209 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Oct 2025
Download
Short summary
Atmospheric particle formation, emissions, and growth process rates are significant sources of uncertainty in predicting climate change. We aim to reduce that uncertainty by using measurements from several ground-based sites across Europe. We developed an estimation technique to adapt the governing process rates so model–measurement bias decays. The estimation framework developed has potential to improve model predictions while providing insight into the underlying atmospheric particle physics.
Read more
Share