Data-driven Exploration of Tropical Cyclone's Controllability

TL;DR

This paper demonstrates a data-driven approach using Ensemble Kalman Control to identify small-scale perturbations that can mitigate tropical cyclones in simulations, highlighting potential for controlled weather modification.

Contribution

It introduces the application of EnKC to tropical cyclone simulation for the first time, showing how data-driven control can find perturbations to reduce cyclone intensity.

Findings

EnKC finds perturbations that mitigate TCs in simulation.

A reduction in surface water vapor near 250km from TC center suppresses cyclone activity.

The method shows potential for data-driven weather control strategies.

Abstract

Although the chaotic nature of the atmosphere may enable efficient control of tropical cyclones (TCs) via small-scale perturbations, few studies have proposed data-driven optimization methods to identify such perturbations. Here, we apply the recently proposed Ensemble Kalman Control (EnKC) to a TC simulation. We show that EnKC finds small-scale perturbations that mitigate TC. An EnKC-estimated reduction in surface water vapor, located approximately 250km from the TC center, suppresses convective activity and latent heat release in the eye wall, leading to a reduction of TC intensity. To advance the discovery of feasible TC mitigation strategies, we discuss the potential of this data-driven method for leveraging chaos, as well as its remaining challenges.