Identification and interpretation of nonnormality in atmospheric time series

Cristian Proistosescu, Andrew Rhines, Peter Huybers

Research output: Contribution to journalArticlepeer-review


Nonnormal characteristics of geophysical time series are important determinants of extreme events and may provide insight into the underlying dynamics of a system. The structure of nonnormality in winter temperature is examined through the use of linear filtering of radiosonde temperature time series. Filtering either low or high frequencies generally suppresses what is otherwise statistically significant nonnormal variability in temperature. The structure of nonnormality is partly attributable to geometric relations between filtering and the appearance of skewness, kurtosis, and higher order moments in time series data, and partly attributable to the presence of nonnormal temperature variations at the highest resolved frequencies in the presence of atmospheric memory. A nonnormal autoregressive model and a multiplicative noise model are both consistent with the observed frequency structure of nonnormality. These results suggest that the generating mechanism for nonnormal variations does not necessarily act at the frequencies at which greatest nonnormality is observed.

Original languageEnglish (US)
Pages (from-to)5425-5434
Number of pages10
JournalGeophysical Research Letters
Issue number10
StatePublished - 2016
Externally publishedYes


  • filtering
  • non-Gaussianity
  • nonnormality
  • spectral analysis
  • temperature distributions

ASJC Scopus subject areas

  • Geophysics
  • Earth and Planetary Sciences(all)


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