EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Power spectrum estimation for extreme events data

Norbert Marwan and Tobias Braun
Norbert Marwan and Tobias Braun
  • Potsdam Institute for Climate Impact Research, Complexity Science, Potsdam, Germany (

The estimation of power spectral density (PSD) of time series is an important task in many quantitative scientific disciplines. However, the estimation of PSD from discrete data, such as extreme event series is challenging. We present a novel approach for the estimation of a PSD of discrete data. Combining the edit distance metric with the Wiener-Khinchin theorem provides a simple yet powerful PSD analysis for discrete time series (e.g., extreme events). This method works directly with the event time series without interpolation. We demonstrate the method's potential on some prototypical examples and on event sequences of atmospheric rivers (AR), narrow filaments of extensive water vapor transport in the lower troposphere. Considering the spatial-temporal event series of ARs over Europe, we investigate the presence of a seasonal cycle as well as periodicities in the multi-annual range for specific regions, likely related to the North-Atlantic Oscillation (NAO).

How to cite: Marwan, N. and Braun, T.: Power spectrum estimation for extreme events data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7730,, 2023.

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