2016
DOI: 10.3390/atmos7120157
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Sensitive versus Rough Dependence under Initial Conditions in Atmospheric Flow Regimes

Abstract: Abstract:In this work, we will identify the existence of "rough dependence on initial conditions" in atmospheric phenomena, a concept which is a problem for weather analysis and forecasting. Typically, two initially similar atmospheric states will diverge slowly over time such that forecasting the weather using the Navier-Stokes equations is useless after some characteristic time scale. With rough dependence, two initial states diverge very quickly, implying forecasting may be impossible. Using previous resear… Show more

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Cited by 7 publications
(15 citation statements)
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“…Also, the study by [18] describes blocking as a quasistationary atmospheric state with quasi-barotropic structure, and in a quasi-barotropic flow the sum of the positive Lyapunov exponents is related to IRE. A Lyapunov exponent is the measure of the divergence or convergence of system trajectories that are initially close, and can be approximated by integrating enstrophy over a finite region known as IRE, which is vorticity squared [10,29]. The work of [29] used this technique to determine the stability or predictability within a planetry flow regime.…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…Also, the study by [18] describes blocking as a quasistationary atmospheric state with quasi-barotropic structure, and in a quasi-barotropic flow the sum of the positive Lyapunov exponents is related to IRE. A Lyapunov exponent is the measure of the divergence or convergence of system trajectories that are initially close, and can be approximated by integrating enstrophy over a finite region known as IRE, which is vorticity squared [10,29]. The work of [29] used this technique to determine the stability or predictability within a planetry flow regime.…”
Section: Methodsmentioning
confidence: 99%
“…Since numerical models are reliable out to about seven days or so, but limited to 10-14 days at a maximum (e.g., [8,9]) the failure of these models in operational forecasting is a problem for anticipating blocking. Model predictions are subject to fail for various reasons, several being: parameterization errors, lack of data, measurement errors and errors in initial conditions (e.g., [10] and references therein).…”
Section: Introductionmentioning
confidence: 99%
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“…Enstrophy in a geophysical fluid relates to the dissipation tendency, and this quantity has use in studying turbulent flows (e.g., [14][15][16][17]). Many studies have shown that IRE is related to the sum of the positive Lyapunov exponents as discussed first by [2] and later by the work cited above and by [12] flow stability.…”
Section: Introductionmentioning
confidence: 99%
“…Limitations for dynamic weather are due also to the lack of data, knowledge of the physical processes, and measurement error (e.g., [9,10]). Error in the initial and/or boundary conditions can render model forecasts useless in as quickly as a few days [8], or alternatively two forecasts with slight difference in the initial conditions could evolve in radically different ways over the course of time (e.g., [11,12]). This problem is referred to 2 Advances in Meteorology as sensitivity to the initial conditions (SDOIC).…”
Section: Introductionmentioning
confidence: 99%