This repository contains data and scripts needed to reproduce the figures of Emad and Siebicke (2022)
True eddy accumulation – Part 2: Theory and experiment of the short-time eddy accumulation method
Accepted for final publication in Atmospheric Measurement Techniques
The publication version is archived on zenodo
Abstract
A new variant of the eddy accumulation method for measuring atmospheric exchange is derived and a prototype sampler is evaluated. The new method, termed short-time eddy accumulation (STEA), overcomes the requirement of fixed accumulation intervals in the true eddy accumulation method (TEA) and enables the sampling system to run in a continuous flow-through mode. STEA enables adaptive time-varying accumulation intervals which improves the system’s dynamic range and brings many advantages to flux measurement and calculation. The STEA method was successfully implemented and deployed to measure CO2 fluxes over an agricultural field in Braunschweig, Germany. The measured fluxes matched very well against a conventional eddy covariance system (slope of 1.04, R^2 of 0.86). We provide a detailed description of the setup and operation of the STEA system in the continuous flow-through mode, devise an empirical correction for the effect of buffer volumes, and describe the important considerations for the successful operation of the STEA method. The STEA method reduces the bias and uncertainty in the measured fluxes compared to conventional TEA and creates new ways to design eddy accumulation systems with finer control over sampling and accumulation. The results encourage the application of STEA for measuring fluxes of more challenging atmospheric constituents such as reactive species. This paper is Part 2 of a two-part series on true eddy accumulation.
Data are provided under the directory data in two different formats:
rds: native R serialization format, convenient and recommended for loading the data into R.csv: for intolerability, a copy of the data is provided in csv format.
Metadata for all files is stored under data/metadata.
The metadata contains information about variables description and units.
To reproduce the figures in the paper, you will need to run the individual
scripts associated with each figure.
The scripts are found under scripts directory.
The name of each script indicates the name of the figure it produces.
Simply run the scripts in order to generate the figures.
The resulting figures will be saved under figures directory.
Additionally, you can clone the repository and run create-all-figures.sh to
reproduce all the figures.
The font Carrois Gothic is required for the figures.
R packages in scripts/01-deps.R are needed.
Below is a full R session info.
sessionInfo()
> sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Manjaro Linux
Matrix products: default
BLAS/LAPACK: /opt/intel/oneapi/mkl/2022.1.0/lib/intel64/libmkl_gf_lp64.so.2
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.0 timechange_0.1.1 data.table_1.14.6 patchwork_1.1.2
[5] latex2exp_0.9.6 lmodel2_1.7-3 ggplot2_3.3.6 colorout_1.2-2
loaded via a namespace (and not attached):
[1] pillar_1.8.0 compiler_4.2.2 R.methodsS3_1.8.2 R.utils_2.12.2
[5] tools_4.2.2 lifecycle_1.0.1 tibble_3.1.8 gtable_0.3.0
[9] pkgconfig_2.0.3 rlang_1.0.4 DBI_1.1.3 cli_3.3.0
[13] withr_2.5.0 dplyr_1.0.10 stringr_1.4.0 generics_0.1.3
[17] vctrs_0.4.1 grid_4.2.2 tidyselect_1.1.2 glue_1.6.2
[21] R6_2.5.1 fansi_1.0.3 purrr_0.3.5 farver_2.1.1
[25] magrittr_2.0.3 scales_1.2.0 assertthat_0.2.1 colorspace_2.0-3
[29] utf8_1.2.2 stringi_1.7.8 munsell_0.5.0 R.oo_1.25.0