Persistence in a Simple Model for the Earth's Atmosphere Temperature Fluctuations

Berrones, Arturo

doi:10.1142/s021947750500280x

Cited by 3 publications

(1 citation statement)

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“…Since the pioneering work of Hurst on time series with long-range dependence LRD is observed in the Nile Basin 41 , the LRD property of time series in geosciences has been 2 Mathematical Problems in Engineering widely observed; see, for example, [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59] . By LRD, one means that the covariance function C τ of time series x t decays so slowly such that…”

Section: Introductionmentioning

confidence: 99%

Hölder Scales of Sea Level

Chen

et al. 2012

Mathematical Problems in Engineering

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The statistics of sea level is essential in the field of geosciences, ranging from ocean dynamics to climates. The fractal properties of sea level, such as long-range dependence (LRD) or long memory,1/fnoise behavior, and self-similarity (SS), are known. However, the description of its multiscale behavior as well as local roughness with the Hölder exponenth(t)from a view of multifractional Brownian motion (mBm) is rarely reported, to the best of our knowledge. In this research, we will exhibit that there is the multiscale property of sea level based onh(t)s of sea level data recorded by the National Data Buoy Center (NDBC) at six stations in the Florida and Eastern Gulf of Mexico. The contributions of this paper are twofold as follows. (i) Hölder exponent of sea level may not change with time considerably at small time scale, for example, daily time scale, but it varies significantly at large time scale, such as at monthly time scale. (ii) The dispersion of the Hölder exponents of sea level may be different at different stations. This implies that the Hölder roughness of sea level may be spatial dependent.

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