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- W4322006974 abstract "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)." @default.
- W4322006974 created "2023-02-26" @default.
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- W4322006974 date "2023-05-15" @default.
- W4322006974 modified "2023-09-29" @default.
- W4322006974 title "Power spectrum estimation for extreme events data" @default.
- W4322006974 doi "https://doi.org/10.5194/egusphere-egu23-7730" @default.
- W4322006974 hasPublicationYear "2023" @default.
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