Short-Horizon Sparse Model Predictive Control for Precipitation Reduction Using Numerical Weather Prediction

Jul 16, 2026

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Extreme climate-driven weather risks require quantitative weather control for disaster mitigation. This study presents a precipitation control framework that couples a realistic Numerical Weather Prediction (NWP) model with model predictive control (MPC). At each MPC time instant, a finite-difference sensitivity matrix from the NWP model yields a local linear prediction of how atmospheric perturbations affect future precipitation. A sparse convex optimization computes the control input, while blocked sensitivities reduce cost and an end-of-event sparse optimization targets accumulated precipitation over a short horizon.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

Abstract

Introduction

Motivation and background

Existing weather-control approaches

Challenges with realistic NWP-based control

Proposed framework and method

MPC with NWP-based local linear model

Blocked sensitivity and computational efficiency

Short-horizon terminal precipitation objective

Experimental evaluation

arXiv :2607 .04746v1 [ ee ss . SY] 6 Jul 2026 Received: Added at production Revised: Added at production Accepted: Added at production DOI: xxx/xxxx SPECIAL ISSUE ARTICLE Short-Horizon Sparse Model Predictive Control for Precipitation Reduction Using Numerical Weather Prediction Yuta Tanikawa Yuga Tomita Toshiyuki Ohtsuka 1Graduate School of Informatics, Kyoto University, Kyoto, Japan Correspondence Department of Informatics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan. Email: [ohtsuka@i.kyoto-u.ac.jp](ohtsuka@i.kyoto-u.ac.jp) Funding Information This research was supported by the JST Moonshot R&D Program Grant Number JPMJMS2389-1-1 Abstract Extreme weather events exacerbated by climate change demand the development of quantitative weather control technologies for disaster mitigation. This study proposes a precipitation control framework integrating a realistic Numerical Weather Prediction (NWP) model with model predictive control (MPC) . At each control instant in MPC, a finite-difference sensitivity matrix is constructed from the NWP model and used asa local linear model of how perturbations to the atmospheric state affect future precipitation. A sparse convex optimization problem is then solved to compute the control input, which is implemented as a perturbation to the atmospheric state. To reduce computational cost in sensitivity analysis, multiple grid points in the NWP model are treated collectively as a single block, and a uniform perturbation is applied to all points within each block. Moreover, a tailored convex optimization problem is introduced to effectively control the accumulated precipitation at the end of a weather event, using a prediction horizon much shorter than the entire event duration while promoting spatially sparse atmospheric perturbations. To evaluate the proposed MPC method, four control methods are compared: (i) initial-only open-loop optimal control (IO-OL),(ii) full-horizon open-loop optimal control (FH-OL),(iii) shrinking-horizon optimal control (SHOC) with a fixed terminal time, and (iv) single-move MPC with a fixed prediction-horizon length. Numerical experiments on a warm bubble benchmark demonstrate that MPC achieves precipitation reduction comparable to SHOC while reducing the total computational time relative to FH-OL and SHOC. Moreover, despite using a linear prediction model, MPC successfully achieves a challenging level of precipitation reduction, even when openloop optimal control methods, namely, IO-OL and FH-OL, fail because of nonlinear atmospheric evolution. These findings suggest that MPC is a promising control framework for NWP-based precipitation reduction in complex weather events. K E Y W O R D S weather control, model predictive control, convex optimization, numerical weather prediction model 1 INTRODUCTION Extreme windstorms and torrential rainfall events pose increasing risks to human lives, infrastructure, and economic activity. In Japan, particular attention has been paid to typhoons and linear precipitation zones (LPZs), whose spatial localization and rapid development often lead to severe hydrometeorological disasters. In response to these risks, the Japanese Moonshot Research and Development Program Goal 8 promotes research toward weather control technologies that can change the intensity, timing, and location of typhoons and torrential rains for disaster mitigation. 1,2 This national research direction has stimulated renewed interest in the mathematical, computational, and physical foundations of weather control. Weather modification itself has a long history, particularly in the context of cloud seeding for precipitation enhancement.3 More recently, numerical weather prediction (NWP) models have been used to examine physically motivated intervention mechanisms for mitigating heavy rainfall. For example, Hiraga et al.4 investigated idealized cloud-seeding experiments for a mesoscale convective syst

FAQs

How does the proposed MPC framework use the Numerical Weather Prediction (NWP) model?

At each control instant, it constructs a finite-difference sensitivity matrix from the NWP model to form a local linear model linking atmospheric perturbations to future precipitation.

What optimization method computes the control input for precipitation reduction?

A sparse convex optimization problem is solved to determine the control input, which is applied as a perturbation to the atmospheric state.

How is computational cost reduced during sensitivity analysis?

Multiple NWP grid points are grouped into blocks, and a uniform perturbation is applied across each block to reduce the sensitivity-analysis complexity.

Jul 16, 2026

Upload by Aria