A Performance Evaluation of Despiking Algorithms for Eddy Covariance Data

Spike detection for raw high-frequency eddy covariance time series is a challenging task because of the confounding effect caused by complex dynamics and the high level of noise affecting such data. To cope with these features, a new despiking procedure rooted on robust functionals is proposed. By processing simulated data, it is demonstrated that the proposed procedure performs better than the existing algorithms and can be therefore considered as a candidate for the implementation in data center environmental monitoring systems, where the availability of automatic procedures ensuring a high quality standard of released products constitutes an essential prerequisite.