Ensemble-Approximated Model Predictive Control for Meteorological Applications

As the effects of global warming intensify, extreme weather events such as typhoons and heavy rains are increasingly causing severe hydrological disasters. Addressing these challenges requires not only the development of infrastructure and the utilization of forecast information for disaster prevention but also exploring ways to adjust the intensity, timing, and scale of such weather events to potentially prevent or reduce their impact. Control theory represents a new research area that bridges meteorological models with control technologies, promising diverse contributions and novel developments from various scientific fields. The current study focuses on model predictive control (MPC), a technique established in engineering for nonlinear dynamic control, and advances its application in the realm of meteorology. Importantly, we incorporate ensemble approximations derived from the concept of ensemble data assimilation to enhance the effectiveness of MPC. This method has the potential to enable small interventions to modify these systems, especially under extreme conditions such as heavy precipitation or typhoons. It determines the optimal amount of correction and input values by numerically considering the time evolution of model meteorological systems. Significantly, in MPC, determining optimal control inputs necessitates the minimization of a cost function, a process remarkably similar to the variational methods in data assimilation. This similarity highlights the potential for synergies between these two approaches, bridging the strengths of each to enhance weather prediction and management. In this presentation, we will delve into both the concepts of data assimilation and MPC, explaining their similarities and how they can be effectively combined. We will also discuss the application of these concepts in control theory, including the results obtained from implementing these theories in toy models and numerical weather prediction models.