the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Dec 2024
Evaluation of Effectiveness of Intervention Strategy in Control Simulation Experiment through Comparison with Model Predictive Control
Abstract. Climate change intensifies weather-related disasters, necessitating innovative mitigation strategies beyond conventional weather prediction methods. The Control Simulation Experiment (CSE) framework proposes altering weather systems through small perturbations, but its effectiveness relative to other control methods remains uncertain. This study evaluates CSE's efficacy against Model Predictive Control (MPC), a well-established method in control engineering. We develop an MPC algorithm tailored for the Lorenz-63 model, incorporating temporal deep unfolding to address challenges in controlling chaotic systems. Simulations reveal that MPC achieves higher success rates with less control effort under certain conditions, particularly with shorter prediction horizons. This work bridges control theory and atmospheric science, advancing the understanding of atmospheric controllability and informing future strategies to mitigate extreme weather events.
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RC1: 'Comment on npg-2024-26', Anonymous Referee #1, 15 Jan 2025
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The application of control theory, including optimal control theory, to solving problems of weather modification and climate engineering is not new, but at the same time, this problem remains underdeveloped. The application of control theory in meteorological and climate applications has two important aspects.
The first aspect, let's call it physical, is related to the fact that atmospheric processes possess enormous energy, while human technological energy capabilities are orders of magnitude smaller. Therefore, the identification of physically justified methods of affecting weather and climate is a topical issue. For example, this problem can be solved using the sensitivity theory of dynamical systems [1-3].
The second aspect concerns the mathematical theory of weather and climate control. Here too, rather complex problems arise related to the observability and controllability of dynamical systems, which the climate system and its components, including the atmosphere, are. An important and rather complex issue is also the problem of goal-setting, i.e., justification of objective functions (also known as cost functions ot performance indices).
Unfortunately, the key problems of weather and climate control listed above are not even identified by the authors of the reviewed article. I see the reason for this in that the authors seem to be little familiar with a number of works in which the problem of weather and climate control was considered with varying degrees of detail. The list of some articles includes [4-11].
As a model of the atmospheric system, the authors use the well-known E. Lorenz system, which under certain conditions exhibits chaotic behavior. However, it should be kept in mind that the sensitivity and controllability of chaotic dynamical systems have their distinctive features [12,13]. The Lorenz system, in its classical version, as well as in the coupled (fast-slow) version to mimic the atmosphere-ocean system [14,15], is widely used in many fields, including meteorology. This system is mainly used to study the phenomenon of deterministic chaos, as well as to test various new numerical algorithms including AI. However to study the possibilities of controlling meteorological and climatic processes, models must be much more realistic (see for example [7,9]).
The authors essentially solved the problem of studying the sensitivity of the Lorenz system to small controlled perturbations. Meanwhile they did not indicate the problems that may arise in this case [12,13]. The authors also did not at all illuminate the current state of the problem and the current achievements of other researchers, referring only to the work of Miyoshi and Sun. Despite the fact that the work is well written and structured, the authors need to substantially revise it taking into account the above mentioned comments.
References
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Citation: https://doi.org/10.5194/npg-2024-26-RC1
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