Stochastic modeling of daily temperature fluctuations

Andrea Király, I. Jánosi

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

Classical spectral, Hurst, and detrended fluctuation analysis have been revealed asymptotic power-law correlations for daily average temperature data. For short-time intervals, however, strong correlations characterize the dynamics that permits a satisfactory description of temperature changes as a low order linear autoregressive process (dominating the texts on climate research). Here we propose a unifying stochastic model reproducing correlations for all time scales. The concept is an extension of a first-order autoregressive model with power-law correlated noise. The inclusion of a nonlinear “atmospheric response function” conveys the observed skew for the amplitude distribution of temperature fluctuations. While stochastic models cannot help to understand the physics behind atmospheric processes, they are capable to extract useful features promoting to benchmark physical models, an example is shown. Possible applications for other systems of strong short-range and asymptotic power-law correlations are discussed.

Original languageEnglish
Number of pages1
JournalPhysical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Volume65
Issue number5
DOIs
Publication statusPublished - Jan 1 2002

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Stochastic Modeling
Fluctuations
Asymptotic Power
Power Law
Stochastic Model
autoregressive processes
atmospheric physics
Correlated Noise
temperature
Linear Process
Autoregressive Process
Response Function
Autoregressive Model
Physical Model
Climate
Skew
climate
Time Scales
Inclusion
Physics

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

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