We propose ensemble-based model predictive control (EnMPC), a novel method that improves the control of complex systems like the atmosphere by integrating control theory with data assimilation. Unlike traditional methods, which are computationally expensive, EnMPC uses ensemble simulations to efficiently handle uncertainties and optimize solutions. This approach reduces computational cost while maintaining accuracy, making it a promising step toward real-world applications in dynamic system control.

Controlling chaotic systems is a key step toward weather control. The control simulation experiment (CSE) modifies weather systems using small perturbations, as shown in studies with the Lorenz-63 model. However, the effectiveness of CSE compared to other methods is unclear. This study evaluates CSE against model predictive control (MPC). Simulations reveal that MPC achieves higher success rates with less effort under certain conditions, linking control theory and atmospheric science.

A major challenge in weather control aimed at mitigating extreme weather events is identifying effective control inputs under limited computational resources. This study proposes a novel control framework called model predictive control with foreseeing horizon, designed to efficiently control chaotic dynamical systems. Using a 40-variable chaotic dynamical model, the proposed method successfully mitigated extreme events and reduced computational cost compared to the conventional approach.

A tropical cyclone is a circular air movement that emerges over warm waters of the tropical ocean and its movement is guided by complex interactions between the ocean and the atmosphere. To better understand this complexity, we adopted ideas and techniques from biology and bioinformatics, to have a fresh look at this matter. This led to the creation of "MeteoScape," a tool that calculates the probability of paths for tropical cyclones can take and visualize them in an understandable way.

Data assimilation (DA) has been used to reconstruct paleoclimate fields. DA integrates model simulations and climate proxies based on their error sizes. Consequently, error information is vital for DA to function optimally. This study estimated observation errors using "innovation statistics" and demonstrated DA with estimated errors outperformed previous studies.