Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6945-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-6945-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Dec 2020
Research article |
| 21 Dec 2020
| 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
CORRESPONDING AUTHOR
Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne,
Switzerland
Elisabeth Andrews
Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, CO, USA
NOAA/Earth Systems Research Laboratory Boulder, CO, USA
Alessandro Bigi
Università di Modena e Reggio Emilia, Department of Engineering
“Enzo Ferrari”, Modena, Italy
Giovanni Martucci
Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne,
Switzerland
Gonzague Romanens
Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne,
Switzerland
Frédéric P. A. Vogt
Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne,
Switzerland
Laurent Vuilleumier
Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne,
Switzerland
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Latest update: 14 Nov 2024
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.
The Mann–Kendall trend test requires prewhitening in the presence of serially correlated data....
