Articles | Volume 29, issue 1
Nonlin. Processes Geophys., 29, 133–139, 2022
Nonlin. Processes Geophys., 29, 133–139, 2022
NPG Letters
28 Mar 2022
NPG Letters | 28 Mar 2022

Control simulation experiment with Lorenz's butterfly attractor

Takemasa Miyoshi and Qiwen Sun

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An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0
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Geosci. Model Dev., 15, 8395–8410,,, 2022
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A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
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Control Simulation Experiments of Extreme Events with the Lorenz-96 Model
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Nonlin. Processes Geophys. Discuss.,,, 2022
Preprint under review for NPG
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Guidance on how to improve vertical covariance localization based on a 1000-member ensemble
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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
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Cited articles

Atlas, R., Kalnay, E., Baker, W. E., Susskind, J., Reuter, D., and Halem, M.: Simulation studies of the impact of future observing systems on weather prediction, Preprints, Seventh Conf. on Numerical Weather Prediction, Montreal, QC, Canada, Amer. Meteor. Soc., 145–151, 1985. 
Boccaletti, S., Grebogi, C., Lai, Y.-C., Mancini, H., and Maza, D.: The control of chaos: theory and applications, Phys. Rep., 329, 103–197,, 2000.  
Evans, E., Bhatti, N., Kinney, J., Pann, L., Peňa, M., Yang, S. C., Kalnay, E., and Hansen, J.: RISE undergraduates find that regime changes in Lorenz's model are predictable, B. Am. Meteorol. Soc., 85, 521–524, 2004. 
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143–10162,, 1994. 
Evensen, G.: Advanced data assimilation for strongly nonlinear dynamics, Mon. Weather Rev., 125, 1342–1354, 1997. 
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 nature in 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.