Abstract. The distribution of extreme event return times and their correlations are analyzed in observed and simulated long-term memory (LTM) time series with 1/f power spectra. The analysis is based on tropical temperature and mixing ratio (specific humidity) time series from TOGA COARE with 1 min resolution and an approximate 1/f power spectrum. Extreme events are determined by Peak-Over-Threshold (POT) crossing. The Weibull distribution represents a reasonable fit to the return time distributions while the power-law predicted by the stretched exponential for 1/f deviates considerably. For a comparison and an analysis of the return time predictability, a very long simulated time series with an approximate 1/f spectrum is produced by a fra...
Statistical physics and dynamical systems theory are key tools to study high-impact geophysical even...
During recent decades, there has been a growing interest in research activities on change in geophys...
Summary. The analysis of extreme values within a stationary time series entails various assumptions ...
The distribution of extreme event return times and their correlations are analyzed in observed and s...
Effects of extreme value loss on long-term correlated time series are analyzed by means of detrended...
The present study aims at proving the existence of long memory (or long-range dependence) in the ear...
Serial correlation of extreme midlatitude cyclones observed at the storm track exits is explained by...
In the literature many papers state that long-memory time series models such as Fractional Gaussian ...
Time series in the Earth Sciences are often characterized as self-affine long-range persistent, wher...
Extreme events, which are usually characterized by generalized extreme value (GEV) models, can exhib...
ABSTRACT: This study aimed to analyze the time series behavior of the Southern Oscillation Index thr...
[1] The paper explores the hypothesis that the temporal global temperature response can be modeled a...
It will be discussed the statistics of the extreme values in time series characterized by finite-ter...
Abstract—We review recent studies of the statistics of return intervals (i) in long-term correlated ...
We study global mean surface temperature records since 1850 and their potential forcings. We find lo...
Statistical physics and dynamical systems theory are key tools to study high-impact geophysical even...
During recent decades, there has been a growing interest in research activities on change in geophys...
Summary. The analysis of extreme values within a stationary time series entails various assumptions ...
The distribution of extreme event return times and their correlations are analyzed in observed and s...
Effects of extreme value loss on long-term correlated time series are analyzed by means of detrended...
The present study aims at proving the existence of long memory (or long-range dependence) in the ear...
Serial correlation of extreme midlatitude cyclones observed at the storm track exits is explained by...
In the literature many papers state that long-memory time series models such as Fractional Gaussian ...
Time series in the Earth Sciences are often characterized as self-affine long-range persistent, wher...
Extreme events, which are usually characterized by generalized extreme value (GEV) models, can exhib...
ABSTRACT: This study aimed to analyze the time series behavior of the Southern Oscillation Index thr...
[1] The paper explores the hypothesis that the temporal global temperature response can be modeled a...
It will be discussed the statistics of the extreme values in time series characterized by finite-ter...
Abstract—We review recent studies of the statistics of return intervals (i) in long-term correlated ...
We study global mean surface temperature records since 1850 and their potential forcings. We find lo...
Statistical physics and dynamical systems theory are key tools to study high-impact geophysical even...
During recent decades, there has been a growing interest in research activities on change in geophys...
Summary. The analysis of extreme values within a stationary time series entails various assumptions ...