Articles | Volume 13, issue 12
Atmos. Meas. Tech., 13, 6945–6964, 2020
https://doi.org/10.5194/amt-13-6945-2020
Atmos. Meas. Tech., 13, 6945–6964, 2020
https://doi.org/10.5194/amt-13-6945-2020

Research article 21 Dec 2020

Research article | 21 Dec 2020

Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann–Kendall trend detection and the associated Sen's slope

Martine Collaud Coen et al.

Model code and software

mannkendall/ Matlab: First release M. Collaud Coen and F. Vogt https://doi.org/10.5281/zenodo.4134619

mannkendall/Python: First release F. P. A. Vogt https://doi.org/10.5281/zenodo.4134435

mannkendall/R: First release A. Bigi and F. P. A. Vogt, https://doi.org/10.5281/zenodo.4134633

Short summary
The Mann–Kendall trend test requires prewhitening in the presence of serially correlated data. The effects of five prewhitening methods and time granularity, autocorrelation, temporal segmentation and length of the time series on the statistical significance and the slope are studies for seven atmospheric datasets. Finally, a new algorithm using three prewhitening methods is proposed in order to optimize the power of the test, the amount of erroneous false positive trends and the slope estimate.