Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 5
Articles | Volume 16, issue 5
https://doi.org/10.5194/amt-16-1167-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-1167-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 5
Research article
 | 
07 Mar 2023
Research article |  | 07 Mar 2023

Data quality enhancement for field experiments in atmospheric chemistry via sequential Monte Carlo filters

Lenard L. Röder, Patrick Dewald, Clara M. Nussbaumer, Jan Schuladen, John N. Crowley, Jos Lelieveld, and Horst Fischer

Viewed

Total article views: 3,213 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,257 818 138 3,213 426 176 261
  • HTML: 2,257
  • PDF: 818
  • XML: 138
  • Total: 3,213
  • Supplement: 426
  • BibTeX: 176
  • EndNote: 261
Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads (calculated since 14 Nov 2022)

Viewed (geographical distribution)

Total article views: 3,213 (including HTML, PDF, and XML) Thereof 3,092 with geography defined and 121 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 17 Jun 2026
Download
Short summary
Field experiments in atmospheric chemistry provide insights into chemical interactions of our atmosphere. However, high data coverage and accuracy are needed to enable further analysis. In this study, we explore a statistical method that combines knowledge about the chemical reactions with information from measurements to increase the quality of field experiment datasets. We test the algorithm for several applications and discuss limitations that depend on the specific variable and the dynamics.
Read more
Share