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

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

doi:https://doi.org/10.5194/amt-16-1167-2023

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

Supplement

https://doi.org/10.5194/amt-16-1167-2023-supplement

Data sets

Data from TO2021 campaign J. N. Crowley, P. Dewald, C. M. Nussbaumer, A. Ringsdorf, A. Edtbauer, J. Schuladen, H. Fischer, J. Williams, L. Röder, and Z. Hamryszczak https://keeper.mpdl.mpg.de/d/f12c1d71d4734a89a6ef/

Model code and software

lenroed/smc-boxmodel: Initial Release lenroed https://doi.org/10.5281/zenodo.7677275

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.