Progress Report

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Control Theory of Weather-Society Coupling Systems for Supporting Social Decision-Making[A-2] Uncertainty quantification for meteorological control

«A-1A-2A-3B-1B-2»

Progress until FY2025

1. Outline of the project

Background: Research on weather control should rely on computer simulations until the safety of new intervention methods is thoroughly assessed. However, meteorological simulations inherently have uncertainties in their various modules, making it challenging to fully trust the simulation results of weather control.

Objective: We will pinpoint all potential sources of uncertainties in meteorological simulations and minimize them using observation data. Then, we will quantify the residual uncertainties contributing to the appropriate assessment of weather control techniques.

Method: We will conduct many simulations with various settings and analyze them alongside observational data using machine learning. This enables us to quantify the uncertainties of meteorological models, which have not been identified by their developers (data-driven approach). We will also comprehend the mechanisms contributing to these uncertainties (process-driven approach) (Fig. 1).

Fig.1
Fig. 1. Overview of this R&D Item. We are going to quantify the uncertainty from the arbitrariness of the selection of model structures and parameters.

2. Outcome so far

① [Data-driven approach]

Using our developed method, we successfully improve the skill of predicting severe rainfall and tropical cyclones and accurately quantify their uncertainties. We applied this method to improve operational weather forecast in Vietnam and toward impact-based forecasting in which real disaster risk is explicitly estimated. In addition, we developed Ensemble Kalman Control, which effectively used the uncertainties in simulation and realized meteorological control, and explored the controllability of tropical cyclones (Fig. 2).

② [Process-driven approach]

Cooperating with the other R&D Items, we are analyzing various large ensemble simulation data (Fig. 3). Focusing on precipitation processes, we are developing the method to interpret the uncertainties estimated by the data-driven method.

Fig.
Fig. 2. Example of a typhoon control experiment using Ensemble Kalman Control. The intervention reduces water vapor over small areas (blue) surrounding regions of strong convection (black). Compared with the no-intervention case (black line), the intervention case (blue line) successfully weakens the typhoon.
Fig.
Fig. 3. (upper left) 2-D histograms in which horizontal and vertical axes show simulated radar reflectivity and height, respectively. (upper right) same as the upper left figure but for simulated Doppler velocity. (bottom) same as the upper figures but for in-situ observations. We found that the model has a non-negligible bias of the precipitation processes despite its high accuracy of disastrous weather events.

3. Future plans

The data-driven algorithms for uncertainty quantification have been developed. Through various applied studies we aim to identify, quantify, and minimize all forms of uncertainty inherent in weather simulations.
We have developed a new mathematical control method that effectively leverages uncertainty in such weather simulations and confirmed its effectiveness in typhoon simulations involving systems with extremely high degrees of freedom. In future work, we will explore the controllability of various weather phenomena, not limited to typhoons, based on more realistic engineering approaches.

«A-1A-2A-3B-1B-2»