Articles | Volume 29, issue 1
https://doi.org/10.5194/npg-29-133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
NPG Letters
| 
28 Mar 2022
NPG Letters |
| 28 Mar 2022
| 28 Mar 2022
Control simulation experiment with Lorenz's butterfly attractor
Takemasa Miyoshi
CORRESPONDING AUTHOR
RIKEN Center for Computational Science, Kobe, 650-0047, Japan
RIKEN Cluster for Pioneering Research, Kobe, 650-0047, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), Kobe, 650-0047, Japan
Application Laboratory, Japan Agency for Marine-Earth Science and
Technology (JAMSTEC), Yokohama, 236-0001, Japan
Qiwen Sun
RIKEN Center for Computational Science, Kobe, 650-0047, Japan
Graduate School of Mathematics, Nagoya University, Nagoya, 464-8601,
Japan
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Cited
19 citations as recorded by crossref.
- Non-invasive chaos control based on 2-contraction stabilizability D. Angeli et al. https://doi.org/10.1016/j.automatica.2025.112778
- Is the brain uncontrollable, like the weather? N. Rust https://doi.org/10.53053/RFPZ2797
- Optimizing Local Maxima in Chaos-Based Features for Drone Fault Diagnosis T. Freitas et al. https://doi.org/10.1109/ACCESS.2026.3728730
- Targeted adaptive chaos control of regimes and eddy strength in two Lorenz models M. Liu et al. https://doi.org/10.1016/j.chaos.2026.118657
- Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model M. Ouyang et al. https://doi.org/10.5194/npg-30-183-2023
- Ensemble-based model predictive control using data assimilation techniques K. Kurosawa et al. https://doi.org/10.5194/npg-32-293-2025
- Ensemble Kalman Filter Meets Model Predictive Control in Chaotic Systems Y. Sawada https://doi.org/10.2151/sola.2024-053
- Chaos suppression through Chaos enhancement L. Li et al. https://doi.org/10.1007/s11071-024-10426-z
- Control simulation experiments of extreme events with the Lorenz-96 model Q. Sun et al. https://doi.org/10.5194/npg-30-117-2023
- Bottom–up approach for mitigating extreme events with limited intervention options: a case study with Lorenz 96 model T. Mitsui et al. https://doi.org/10.5194/npg-32-457-2025
- Pseudo-Lorenz attractors in echo state networks T. Kabayama et al. https://doi.org/10.1007/s13160-025-00740-3
- A duality principle for chaotic systems: from data assimilation to efficient control T. Miyoshi https://doi.org/10.1007/s11071-025-12021-2
- Convex Optimization of Initial Perturbations toward Quantitative Weather Control T. Ohtsuka et al. https://doi.org/10.2151/sola.2025-020
- Ensemble Kalman-guided model predictive path integral control for spatially localized suppression of extremes in chaotic geophysical flows H. Kuroki et al. https://doi.org/10.5194/npg-33-473-2026
- Leading the Lorenz 63 system toward the prescribed regime by model predictive control coupled with data assimilation F. Kawasaki & S. Kotsuki https://doi.org/10.5194/npg-31-319-2024
- Towards Analyzable Design Paradigms for Chaos-Based Cryptographic Primitives A. Abba et al. https://doi.org/10.3390/jcp5030064
- Weather Jiu-Jitsu: Prospects for atmospheric nudging to defuse the impact of catastrophic weather extremes Q. Huang et al. https://doi.org/10.1371/journal.pwat.0000562
- Evaluation of the effectiveness of an intervention strategy in a control simulation experiment through comparison with model predictive control R. Nagai et al. https://doi.org/10.5194/npg-32-281-2025
- Regime identification and control of extremes in the nonautonomous Lorenz model with chaos and intransitivity M. Liu et al. https://doi.org/10.1103/gcz8-3j9y
19 citations as recorded by crossref.
- Non-invasive chaos control based on 2-contraction stabilizability D. Angeli et al. https://doi.org/10.1016/j.automatica.2025.112778
- Is the brain uncontrollable, like the weather? N. Rust https://doi.org/10.53053/RFPZ2797
- Optimizing Local Maxima in Chaos-Based Features for Drone Fault Diagnosis T. Freitas et al. https://doi.org/10.1109/ACCESS.2026.3728730
- Targeted adaptive chaos control of regimes and eddy strength in two Lorenz models M. Liu et al. https://doi.org/10.1016/j.chaos.2026.118657
- Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model M. Ouyang et al. https://doi.org/10.5194/npg-30-183-2023
- Ensemble-based model predictive control using data assimilation techniques K. Kurosawa et al. https://doi.org/10.5194/npg-32-293-2025
- Ensemble Kalman Filter Meets Model Predictive Control in Chaotic Systems Y. Sawada https://doi.org/10.2151/sola.2024-053
- Chaos suppression through Chaos enhancement L. Li et al. https://doi.org/10.1007/s11071-024-10426-z
- Control simulation experiments of extreme events with the Lorenz-96 model Q. Sun et al. https://doi.org/10.5194/npg-30-117-2023
- Bottom–up approach for mitigating extreme events with limited intervention options: a case study with Lorenz 96 model T. Mitsui et al. https://doi.org/10.5194/npg-32-457-2025
- Pseudo-Lorenz attractors in echo state networks T. Kabayama et al. https://doi.org/10.1007/s13160-025-00740-3
- A duality principle for chaotic systems: from data assimilation to efficient control T. Miyoshi https://doi.org/10.1007/s11071-025-12021-2
- Convex Optimization of Initial Perturbations toward Quantitative Weather Control T. Ohtsuka et al. https://doi.org/10.2151/sola.2025-020
- Ensemble Kalman-guided model predictive path integral control for spatially localized suppression of extremes in chaotic geophysical flows H. Kuroki et al. https://doi.org/10.5194/npg-33-473-2026
- Leading the Lorenz 63 system toward the prescribed regime by model predictive control coupled with data assimilation F. Kawasaki & S. Kotsuki https://doi.org/10.5194/npg-31-319-2024
- Towards Analyzable Design Paradigms for Chaos-Based Cryptographic Primitives A. Abba et al. https://doi.org/10.3390/jcp5030064
- Weather Jiu-Jitsu: Prospects for atmospheric nudging to defuse the impact of catastrophic weather extremes Q. Huang et al. https://doi.org/10.1371/journal.pwat.0000562
- Evaluation of the effectiveness of an intervention strategy in a control simulation experiment through comparison with model predictive control R. Nagai et al. https://doi.org/10.5194/npg-32-281-2025
- Regime identification and control of extremes in the nonautonomous Lorenz model with chaos and intransitivity M. Liu et al. https://doi.org/10.1103/gcz8-3j9y
Saved (final revised paper)
Latest update: 11 Nov 2026
Short summary
The weather is chaotic and hard to predict, but the chaos implies an effective control where a small control signal grows rapidly to make a big difference. This study proposes a control simulation experiment where we apply a small signal to control
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.
The weather is chaotic and hard to predict, but the chaos implies an effective control where a...
