{"title":"Leading the Lorenz 63 system toward the prescribed regime by model predictive control coupled with data assimilation","authors":"Fumitoshi Kawasaki, S. Kotsuki","doi":"10.5194/npg-31-319-2024","DOIUrl":null,"url":null,"abstract":"Abstract. Recently, concerns have been growing about the intensification and increase in extreme weather events, including torrential rainfall and typhoons. For mitigating the damage caused by weather-induced disasters, recent studies have started developing weather control technologies to lead the weather to a desirable direction with feasible manipulations. This study proposes introducing the model predictive control (MPC), an advanced control method explored in control engineering, into the framework of the control simulation experiment (CSE). In contrast to previous CSE studies, the proposed method explicitly considers physical constraints, such as the maximum allowable manipulations, within the cost function of the MPC. As the first step toward applying the MPC to real weather control, this study performed a series of MPC experiments with the Lorenz 63 model. Our results showed that the Lorenz 63 system can be led to the positive regime with control inputs determined by the MPC. Furthermore, the MPC significantly reduced necessary forecast length compared to earlier CSE studies. It was beneficial to select a member that showed a larger regime shift for the initial state when dealing with uncertainty in initial states.\n","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nonlinear Processes in Geophysics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/npg-31-319-2024","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 3
Abstract
Abstract. Recently, concerns have been growing about the intensification and increase in extreme weather events, including torrential rainfall and typhoons. For mitigating the damage caused by weather-induced disasters, recent studies have started developing weather control technologies to lead the weather to a desirable direction with feasible manipulations. This study proposes introducing the model predictive control (MPC), an advanced control method explored in control engineering, into the framework of the control simulation experiment (CSE). In contrast to previous CSE studies, the proposed method explicitly considers physical constraints, such as the maximum allowable manipulations, within the cost function of the MPC. As the first step toward applying the MPC to real weather control, this study performed a series of MPC experiments with the Lorenz 63 model. Our results showed that the Lorenz 63 system can be led to the positive regime with control inputs determined by the MPC. Furthermore, the MPC significantly reduced necessary forecast length compared to earlier CSE studies. It was beneficial to select a member that showed a larger regime shift for the initial state when dealing with uncertainty in initial states.
期刊介绍:
Nonlinear Processes in Geophysics (NPG) is an international, inter-/trans-disciplinary, non-profit journal devoted to breaking the deadlocks often faced by standard approaches in Earth and space sciences. It therefore solicits disruptive and innovative concepts and methodologies, as well as original applications of these to address the ubiquitous complexity in geoscience systems, and in interacting social and biological systems. Such systems are nonlinear, with responses strongly non-proportional to perturbations, and show an associated extreme variability across scales.