Nonlinear Dynamics and Chaos: Applications for Prediction of Weather and Climate

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dc.creatorPethkar, J. S.
dc.creatorSelvam, A. M.
dc.date2001-04-19
dc.date.accessioned2026-07-07T05:45:42Z
dc.date.available2026-07-07T05:45:42Z
dc.descriptionTurbulence, namely, irregular fluctuations in space and time characterize fluid flows in general and atmospheric flows in particular.The irregular,i.e., nonlinear space-time fluctuations on all scales contribute to the unpredictable nature of both short-term weather and long-term climate.It is of importance to quantify the total pattern of fluctuations for predictability studies. The power spectra of temporal fluctuations are broadband and exhibit inverse power law form with different slopes for different scale ranges. Inverse power-law form for power spectra implies scaling (self similarity) for the scale range over which the slope is constant. Atmospheric flows therefore exhibit multiple scaling or multifractal structure.Standard meteorological theory cannot explain satisfactorily the observed multifractal structure of atmospheric flows.Selfsimilar spatial pattern implies long-range spatial correlations. Atmospheric flows therefore exhibit long-range spatiotemporal correlations, namely,self-organized criticality,signifying order underlying apparent chaos. A recently developed non-deterministic cell dynamical system model for atmospheric flows predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics.The model predictions are in agreement with continuous periodogram spectral analysis of meteorological data sets.
dc.description4 pages
dc.identifierhttps://arxiv.org/abs/physics/0104056
dc.identifierhttp://arxiv.org/abs/physics/0104056
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/84087
dc.subjectGeneral Physics
dc.titleNonlinear Dynamics and Chaos: Applications for Prediction of Weather and Climate
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