Identification and interpretation of nonnormality in atmospheric time series

Abstract

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.

Predictability and Non-Gaussian Characteristics of the North Atlantic Oscillation

“…A proper understanding and modeling of this characteristic feature is invaluable and has important consequences, particularly in climate research, as weather and climate risk assessment depends on extreme events, which are reflected in the non-Gaussian behavior of the large-scale atmospheric flow structure (Sura and Hannachi 2015;Proistosescu et al 2016;). …”

“…A characteristic feature of the large-scale atmospheric flow structure and the teleconnections is their non-Gaussianity (e.g., Proistosescu et al 2016;Sura and Hannachi 2015;Hannachi 2010), and various mechanisms that could explain the non-Gaussian statistics of atmospheric synoptic and low-frequency variability have been proposed in the literature (e.g., Sura and Hannachi 2015). A proper understanding and modeling of this characteristic feature is invaluable and has important consequences, particularly in climate research, as weather and climate risk assessment depends on extreme events, which are reflected in the non-Gaussian behavior of the large-scale atmospheric flow structure (Sura and Hannachi 2015;Proistosescu et al 2016;).…”

Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality

“…Precisely, let us consider the partition of the rotated vector z into a number of subvectors or candidate sources (e.g., scalars, dyads, or tuples) as z = [z (1) , z (2) , . .…”

“…, ) with ∑ =1 dim(z ( ) ) = . Thanks to the separation theorem [13] or Negentropy Lemma [15], rot decomposes as a sum of partial negentropies plus the nonnegative generalized mutual information (MI) (z (1) , z (2) , . .…”

“…A characteristic feature of the system is its non-Gaussianity. The nonnormality of climate is an important determinant of extreme events and may provide insights into understanding the system dynamics [1][2][3]. For example, it is generally accepted that understanding non-Gaussian statistics of weather and climate has important consequences in atmospheric research not least because weather/climate risk assessment depends on understanding the tail of the system probability density function (PDF).…”