dorsal/arxiv
View SchemaNonlinear Dynamics and Chaos: Applications for Prediction of Weather and Climate
| Authors | J. S. Pethkar, A. M. Selvam |
|---|---|
| Categories | |
| ArXiv ID | physics/0104056 |
| URL | https://arxiv.org/abs/physics/0104056 |
Abstract
Turbulence, 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.
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"abstract": "Turbulence, namely, irregular fluctuations in space and time characterize\nfluid flows in general and atmospheric flows in particular.The irregular,i.e.,\nnonlinear space-time fluctuations on all scales contribute to the unpredictable\nnature of both short-term weather and long-term climate.It is of importance to\nquantify the total pattern of fluctuations for predictability studies. The\npower spectra of temporal fluctuations are broadband and exhibit inverse power\nlaw form with different slopes for different scale ranges. Inverse power-law\nform for power spectra implies scaling (self similarity) for the scale range\nover which the slope is constant. Atmospheric flows therefore exhibit multiple\nscaling or multifractal structure.Standard meteorological theory cannot explain\nsatisfactorily the observed multifractal structure of atmospheric\nflows.Selfsimilar spatial pattern implies long-range spatial correlations.\nAtmospheric flows therefore exhibit long-range spatiotemporal correlations,\nnamely,self-organized criticality,signifying order underlying apparent chaos. A\nrecently developed non-deterministic cell dynamical system model for\natmospheric flows predicts the observed self-organized criticality as intrinsic\nto quantumlike mechanics governing flow dynamics.The model predictions are in\nagreement with continuous periodogram spectral analysis of meteorological data\nsets.",
"arxiv_id": "physics/0104056",
"authors": [
"J. S. Pethkar",
"A. M. Selvam"
],
"categories": [
"physics.gen-ph"
],
"title": "Nonlinear Dynamics and Chaos: Applications for Prediction of Weather and Climate",
"url": "https://arxiv.org/abs/physics/0104056"
},
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