Publication

Journal	Journal of Open Source Software
Volume (Issue)	6(62)
Page(s)	2575 - 2575
Title of proceedings	Journal of Open Source Software
DOI	10.21105/joss

URL	http://doi.org/10.21105/joss
Type of Open Access	Publisher (Gold Open Access)

In ecosystem research, the eddy covariance (EC) method is widely used to quantify the biosphere-atmosphere exchange of greenhouse gases (GHGs) and energy (Aubinet et al., 2012; Baldocchi et al., 1988). The raw ecosystem flux (i.e., net exchange) is calculated by the covariance between the turbulent vertical wind component measured by a sonic anemometer and the entity of interest, e.g., CO2, measured by a gas analyzer. Due to the application of two different instruments, wind and gas are not recorded at exactly the same time, resulting in a time lag between the two time series. For the calculation of ecosystem fluxes this time delay has to be quantified and corrected for, otherwise fluxes are systematically biased. Time lags for each averaging interval can be estimated by finding the maximum absolute covariance between the two turbulent time series at different time steps in a pre-defined time window of physically possible time-lags (e.g., McMillen, 1988; Moncrieff et al., 1997). Lag detection works well when processing fluxes for compounds with high signal-to-noise ratio (SNR), which is typically the case for e.g. CO2. In contrast, for compounds with low SNR (e.g., N2O, CH4) the cross-covariance function with the turbulent wind component yields noisier results and calculated fluxes are biased towards larger absolute flux values (Langford et al., 2015), which in turn renders the accurate calculation of yearly ecosystem GHG budgets more difficult and results may be inaccurate. One method to adequately calculate fluxes for compounds with low SNR is to first calculate the time lag for a reference compound with high SNR (e.g., CO2) and then to apply the same time lag to the target compound of interest (e.g., N2O), with both compounds being recorded by the same analyzer (Nemitz et al., 2018). DYCO uses this method by facilitating the dynamic lag-detection between the turbulent wind data and a reference compound and the subsequent application of found reference time lags to one or more target compounds.