Preprints
https://doi.org/10.48550/arXiv.2405.19546
https://doi.org/10.48550/arXiv.2405.19546
09 Jul 2024
 | 09 Jul 2024
Status: this preprint is open for discussion.

Convex optimization of initial perturbations toward quantitative weather control

Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura, and Shunji Kotsuki

Abstract. This study proposes introducing convex optimization to find initial perturbations of atmospheric models for realizing specified changes in subsequent forecasts. In the proposed method, we formulate and solve an inverse problem to find effective perturbations in atmospheric variables so that controlled variables satisfy specified changes at a specified time. The proposed method first constructs a sensitivity matrix of controlled variables, such as accumulated precipitation, to the initial atmospheric variables, such as temperature and humidity, through sensitivity analysis using numerical weather prediction (NWP) models. The sensitivity matrix is used to solve the inverse problem as convex optimization, in which a global optimal solution can be found computationally efficiently. The proposed method was validated through a benchmark warm bubble experiment using an NWP model. The experiments showed that identified perturbation successfully realized specified spatial distributions of accumulated precipitation. These results demonstrated the possibility of controlling the real atmosphere by solving inverse problems and adding small perturbations to atmospheric states.

Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura, and Shunji Kotsuki

Status: open (until 03 Sep 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura, and Shunji Kotsuki

Model code and software

SCALE-RM Team SCALE, RIKEN https://scale.riken.jp/

Weather-Control-by-InitCond Toshiyuki Ohtsuka https://github.com/ohtsukalab/Weather-Control-by-InitCond

Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura, and Shunji Kotsuki

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Short summary
We utilize weather forecasts in the reverse direction and determine how much we should change the temperature or humidity of the atmosphere at a certain time to change the future rainfall as desired. Even though a weather phenomenon is complicated, we can superimpose the effects of small changes in the atmosphere and find suitable small changes to realize desirable rainfall by solving an optimization problem. We examine this idea on a realistic weather simulator and show it is promising.