  It is a grand challenge to find a feasible weather modification method to
mitigate the impact of extreme weather events such as tropical cyclones.
Previous works have proposed potentially effective actuators and assessed their
capabilities to achieve weather modification objectives through numerical
simulations. However, few studies have explored efficient mathematical and
computational methods to inversely determine optimal actuators from specific
modification goals. Here I demonstrate the utility of the ensemble Kalman
filter (EnKF)-based control method, referred to as ensemble Kalman control
(EnKC). The series of numerical experiments with the Lorenz 96 model indicates
that EnKC efficiently identifies local, small, and intermittent control
perturbations that can mitigate extreme events. The existing techniques of
EnKF, such as background error covariance localization and observation error
covariance inflation, can improve the sparsity and efficiency of the control.
This work paves the way toward the real-world applications of EnKC to explore
the controllability of extreme atmospheric events.
