On time lag detection between time series sampled by eddy covariance systems

This work introduces a new procedure for time lag detection between high-frequency time series sampled by means of the eddy covariance technique. The proposed methodology is based on the assessment of the cross-correlation function between variables subject to (i) a preliminarily filtering procedure based on pre-whitening, to avoid the risk of spurious correlations, and (ii) to a resampling technique based on block-bootstrapping, to enhance the accuracy of time lag detection between variables with correlation of low order of magnitude. We expect that the proposed procedure will become useful for the centralized data processing pipelines of research infrastructures (e.g. ICOS-RI) where the use of completely data-driven procedures constitutes an essential prerequisite.