regClimateChem: An R Package for Data Driven Variable Selection Applied to Atmospheric Carbon Monoxide

Carbon monoxide (CO) is a major pollutant, impacting air quality and contributing to the greenhouse effect. Buchholz et al. [1] used a multiple linear regression model to link CO anomalies in the atmosphere to variability in the climate. Measurements of total column CO were retrieved from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard the Terra satellite. Anomalies generated by these measurements are used as the response variable in this model. Climate mode indices represent regional variability in the climate, and these indices at various time lags are used as the predictor variables. Buchholz et al. [1] performed this analysis in MATLAB using serial algorithms in non-functionalized scripts. Simonson et al. [2] refactored the MATLAB codebase, improving both code interpretability and generality and adding parallelization options. In this technical note, we present further updates to the codebase used in the atmospheric CO analysis from Buchholz et al. [1]. These updates stem from four primary objectives. First, transfer the codebase into an R package that is available to researchers without a MATLAB license. Second, allow for a single climate index to appear in the model multiple times with different lag values. Third, implement an exhaustive variable selection algorithm. Finally, improve upon the non-exhaustive variable selection algorithm implemented in the MATLAB code. These updates are discussed, and the accuracy and timing of the new variable selection algorithms are compared.

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Author Daniels, William
Hammerling, Dorit
Buchholz, Rebecca
Publisher UCAR/NCAR - Library
Publication Date 2020-05-15T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2023-08-18T18:06:38.822632
Metadata Record Identifier edu.ucar.opensky::technotes:583
Metadata Language eng; USA
Suggested Citation Daniels, William, Hammerling, Dorit, Buchholz, Rebecca. (2020). regClimateChem: An R Package for Data Driven Variable Selection Applied to Atmospheric Carbon Monoxide. UCAR/NCAR - Library. Accessed 29 September 2023.

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