Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 6
Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3715-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3715-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 6
Development and technical paper
 | 
22 Jun 2021
Development and technical paper |  | 22 Jun 2021

Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0

Matthew Ozon, Aku Seppänen, Jari P. Kaipio, and Kari E. J. Lehtinen

Viewed

Total article views: 1,658 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,123 497 38 1,658 33 18
  • HTML: 1,123
  • PDF: 497
  • XML: 38
  • Total: 1,658
  • BibTeX: 33
  • EndNote: 18
Views and downloads (calculated since 16 Oct 2020)
Cumulative views and downloads (calculated since 16 Oct 2020)

Viewed (geographical distribution)

Total article views: 1,658 (including HTML, PDF, and XML) Thereof 1,437 with geography defined and 221 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Apr 2024
Download
Short summary
Experimental research has provided large amounts of high-quality data on aerosol over the last 2 decades. However, inference of the process rates (e.g., the rates at which particles are generated) is still typically done by simple curve-fitting methods and does not assess the credibility of the estimation. The devised method takes advantage of the Bayesian framework to not only retrieve the state of the observed aerosol system but also to estimate the process rates (e.g., growth rate).
Read more