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

In this study, we explore the applications and limitations of sequential Monte Carlo (SMC) filters to field experiments in atmospheric chemistry. 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, O-3, NO, NO2 and j(NO2) 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.

Automated summary

In this study, the applications and limitations of sequential Monte Carlo (SMC) filters in atmospheric chemistry field experiments are explored. The proposed algorithm is simple, fast, versatile, and provides a complete probability distribution. It effectively reduces uncertainty in measured variables by combining measurements with known system dynamics. The extension of the algorithm with an activity variable enhances its robustness and provides quantitative measure of dominant processes.