14 Nov 2022
14 Nov 2022

Data Quality Enhancement for Atmospheric Chemistry Field Experiments via Sequential Monte Carlo Filters

Lenard L. Röder1, Patrick Dewald1, Clara M. Nussbaumer1, Jan Schuladen1, John N. Crowley1, Jos Lelieveld1,2, and Horst Fischer1 Lenard L. Röder et al.
  • 1Max Planck Institute for Chemistry, Department of Atmospheric Chemistry, Mainz, Germany
  • 2Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, Cyprus

Abstract. In this study we explore the applications and limitations of Sequential Monte Carlo filters (SMC) to atmospheric chemistry field experiments. The proposed algorithm is simple, fast, versatile and returns a complete probability distribution. It combines information from measurements with known system dynamics to decrease the uncertainty of measured variables. The method shows high potential to increase data coverage, precision and even possibilities to infer unmeasured variables. We extend the original SMC algorithm with an activity variable that gates the proposed reactions. This extension makes the algorithm more robust when dynamical processes not considered in the calculation dominate and the information provided via measurements is limited. The activity variable also provides a quantitative measure of the dominant processes. Free parameters of the algorithm and their effect on the SMC result are analyzed. The algorithm reacts very sensitively to the estimated speed of stochastic variation. We provide a scheme to choose this value appropriately. In a simulation study O3, NO, NO2 and jNO2 are tested for interpolation and de-noising using measurement data of a field campaign. Generally, the SMC method performs well under most conditions, with some dependence on the particular variable being analyzed.

Lenard L. Röder et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1080', Anonymous Referee #2, 05 Dec 2022
    • AC1: 'Reply on RC1', Lenard Röder, 19 Jan 2023
  • RC2: 'Comment on egusphere-2022-1080', Anonymous Referee #1, 27 Dec 2022
    • AC2: 'Reply on RC2', Lenard Röder, 19 Jan 2023

Lenard L. Röder et al.

Data sets

Data from TO2021 campaign Crowley, J. N., Dewald, P., Nussbaumer, C. M., Ringsdorf, A., Edtbauer, A., Schuladen, J., Fischer, H., Williams, J., Röder, L., and Hamryszczak, Z.

Model code and software

Sequential Monte Carlo Filters for Chemical Box-Models Lenard L. Röder

Lenard L. Röder et al.


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Short summary
Field experiments in atmospheric chemistry provide insights into chemical interactions of our atmosphere. However, high data coverage and accuracy is 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 data sets. We test the algorithm for several applications and discuss limitations that depend on the specific variable and the dynamics.