Ensemble Kalman–Guided Model Predictive Path Integral Control for Spatially Localized Suppression of Extremes in Chaotic Geophysical Flows

Abstract. The possibility of influencing extreme weather phenomena has been discussed for decades; however, it remains far from operational practice, and there is still no established framework for designing small, spatially localized perturbations that can reliably steer chaotic geophysical flows. In this study, we propose a hybrid control method, termed ensemble-Kalmanguided model predictive path integral control (EKG-MPPI), which combines ensemble Kalman control (EnKC) with model predictive path integral (MPPI) control. Within a control simulation experiment framework, an ensemble Kalman filter is first used for state estimation, after which EnKC computes a candidate perturbation by treating the control objective as a pseudo-observation. An adaptive thresholding procedure then enforces spatial sparsity, so that the EnKC perturbation identifies candidate actuator locations and their nominal amplitudes. This information is embedded into the mean and covariance of Gaussian proposal distributions for MPPI, which subsequently refines the perturbation through sampling-based optimization with nonlinear rollouts, without linearizing the dynamics or computing gradients. Numerical experiments with the Lorenz–96 model and the surface quasi-geostrophic (SQG) model demonstrate that EKG-MPPI can suppress extremes in state variables and regional wind speed more effectively than EnKC alone, while using comparable or smaller control inputs. These results highlight EKG-MPPI as a promising building block for simulation-based assessment of localized intervention strategies in geophysical flows.