Supporting data and R scripts for: Imputation of atmospheric trace gases and meteorological time series under realistic missing conditions

Description This repository contains the supporting datasets, R scripts, and documentation associated with the manuscript entitled “Imputation of atmospheric trace gases and meteorological time series under realistic missing conditions" The study investigates missing-data patterns in atmospheric trace gas and meteorological time series and evaluates conventional imputation methods alongside a gap-length-adaptive hybrid approach. The repository is organised into the following components: 1. Data The meteorological datasets include measured observations from the FMFI monitoring station and corresponding ERA5-derived variables. Additional datasets provide comparisons between measured values and estimates obtained using direct ERA5 substitution, Kalman filtering, random forest, and the proposed hybrid imputation approach for temperature and wind speed. 2. R scripts The R scripts are organised into two workflows: Meteorological imputation: scripts for model training, imputation, evaluation, and visualisation of meteorological time series. CO₂ and radon imputation: scripts for model training, imputation, evaluation, and visualisation of atmospheric CO₂ and radon activity concentration (RAC) time series. 3. Documentation The documentation includes a data dictionary, software requirements, and a reproducibility guide describing the datasets, variables, required input files, and procedures for running the analysis. Data availability and restrictions The original CO₂ and radon activity concentration (RAC) observations are subject to data-access restrictions and are therefore not included in this public repository. Access to these observations may be requested from the corresponding author, subject to approval by the relevant data owners and institutional requirements. Consequently, complete reproduction of the CO₂ and RAC analyses requires authorised access to the original observations. The repository is intended to support transparency, methodological understanding, and reproducibility of the published research, within the applicable data-access conditions.