Comparative Analysis of Black-Box Optimization Methods for Weather Intervention Design

TL;DR

This paper compares black-box optimization methods for designing weather interventions to mitigate climate change impacts, demonstrating Bayesian optimization's superior performance in reducing rainfall effectively.

Contribution

It introduces a black-box optimization framework for weather intervention design and provides a comparative analysis of four methods, highlighting Bayesian optimization's effectiveness.

Findings

Bayesian optimization outperforms other methods in rainfall reduction.

High-dimensional search spaces favor Bayesian optimization.

The approach enables efficient weather intervention planning without gradient information.

Abstract

As climate change increases the threat of weather-related disasters, research on weather control is gaining importance. The objective of weather control is to mitigate disaster risks by administering interventions with optimal timing, location, and intensity. However, the optimization process is highly challenging due to the vast scale and complexity of weather phenomena, which introduces two major challenges. First, obtaining accurate gradient information for optimization is difficult. In addition, numerical weather prediction (NWP) models demand enormous computational resources, necessitating parameter optimization with minimal function evaluations. To address these challenges, this study proposes a method for designing weather interventions based on black-box optimization, which enables efficient exploration without requiring gradient information. The proposed method is evaluated in two distinct control scenarios: one-shot initial value intervention and sequential intervention based on model predictive control. Furthermore, a comparative analysis is conducted among four representative black-box optimization methods in terms of total rainfall reduction. Experimental results show that Bayesian optimization achieves higher control effectiveness than the others, particularly in high-dimensional search spaces. These findings suggest that Bayesian optimization is a highly effective approach for weather intervention computation.