Quest for an efficient mathematical and computational method to explore optimal extreme weather modification

TL;DR

This paper introduces an ensemble Kalman filter-based control method to efficiently identify optimal actuators for mitigating extreme weather events, demonstrating promising results in a Lorenz 96 model simulation.

Contribution

It presents a novel application of ensemble Kalman control (EnKC) for inverse determination of optimal weather modification actuators, enhancing efficiency and sparsity.

Findings

EnKC effectively identifies local control perturbations to mitigate extreme events.

Techniques like covariance localization and inflation improve control sparsity and efficiency.

Numerical experiments show potential for real-world atmospheric event control.

Abstract

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.