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Quantitative study of the relative effects of initial condition and model uncertainties on local predictability in a nonlinear dynamical system

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  • Li, Xuan
  • Ding, Ruiqiang
  • Li, Jianping

Abstract

The relative effects of initial condition and model uncertainties on local predictability are important issues in the atmospheric sciences. This study quantitatively compared the relative effects of these two types of uncertainty on local predictability using the Lorenz model. Local predictability limits were quantitatively estimated using the nonlinear local Lyapunov exponent (NLLE) method. Results show that the relative effects of initial conditions and model uncertainties on local predictability vary with the state. In addition, inverse spatial distributions of local predictability limits are induced by the two types of uncertainty. In the regime transition region, the local predictability limits of modeled states are more sensitive to initial condition uncertainty than to model uncertainty, resulting in lower local predictability limits being induced by initial condition uncertainties. Local predictability limits induced by initial condition uncertainties are 4 time units shorter than those induced by model uncertainties. In the “butterfly wing” regions, the local predictability limits of modeled states are more sensitive to model uncertainty than to initial condition uncertainty, resulting in lower local predictability limits due to model uncertainty. Local predictability limits induced by initial condition uncertainty are larger (0 to 4 time units) than those induced by model uncertainty. These differences in the regions that are sensitive to the two types of uncertainty mean that strategic reductions of uncertainty in sensitive areas may effectively improve forecast skill.

Suggested Citation

  • Li, Xuan & Ding, Ruiqiang & Li, Jianping, 2020. "Quantitative study of the relative effects of initial condition and model uncertainties on local predictability in a nonlinear dynamical system," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
  • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304914
    DOI: 10.1016/j.chaos.2020.110094
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    Cited by:

    1. Wang, Mingzhao & Fu, Zuntao, 2022. "A new method of nonlinear causality detection: Reservoir computing Granger causality," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    2. Fu, Chao & Zheng, Zhaoli & Zhu, Weidong & Lu, Kuan & Yang, Yongfeng, 2022. "Non-intrusive frequency response analysis of nonlinear systems with interval uncertainty: A comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).

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