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

R Code and Supporting Documentation for Atmospheric Trace Gas and Meteorological Time-Series Imputation This repository contains the R scripts and supporting documentation associated with the manuscript “Imputation of atmospheric trace gases and meteorological time series under realistic missing conditions.” The study presents and evaluates a gap-length-adaptive hybrid framework for imputing missing observations in atmospheric and meteorological time series. The framework combines statistical and machine-learning approaches to address missing-data patterns of varying duration, with applications to meteorological variables, atmospheric CO₂ concentrations, and radon activity concentrations (RAC). Repository Contents The repository provides: R scripts: Code for meteorological and CO₂/radon imputation, model training, performance evaluation, and visualisation. README.md: Overview of the repository structure, workflow, and data requirements. Reproducibility Guide: Instructions for preparing input data and executing the analysis workflows. Software Requirements: Required R packages and computational environment information. Data Dictionary: Definitions, units, sources, and roles of variables used in the analysis. Data Availability and Restrictions This repository contains source code and documentation only. No observational, reanalysis, or derived datasets are included. The study uses meteorological observations from the Faculty of Mathematics, Physics and Informatics (FMFI), Comenius University in Bratislava, ERA5 atmospheric reanalysis data, and atmospheric CO₂ and radon activity concentration measurements. ERA5 reanalysis data are publicly accessible through the Copernicus Climate Data Store: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels The FMFI meteorological observations and original CO₂ and radon activity concentration measurements are subject to data-access restrictions and are not redistributed in this repository. Requests for access may be directed to the corresponding author, subject to approval by the relevant data owners and applicable institutional requirements. Reproducibility The supplied R scripts document the computational workflows used for missing-data simulation, imputation, model comparison, and visualisation. The accompanying documentation specifies the expected input variables, software dependencies, and execution procedures. The scripts can be inspected and adapted independently; however, full reproduction of the manuscript results requires authorised access to the original observational datasets and preparation of the corresponding input data structures. Associated Manuscript Sultani, M. A., Bulko, M., Müllerová, M., Masarik, J., and Eckertová, T. Imputation of atmospheric trace gases and meteorological time series under realistic missing conditions. Contact Mohammad Alem SultaniDepartment of Nuclear Physics and BiophysicsFaculty of Mathematics, Physics and InformaticsComenius University in Bratislava, SlovakiaEmail: [e-mail]