A Robust Control Framework for Precipitation Regulation under NWP Model Uncertainty

    Severe rainfall events can cause significant harm to individuals, damage infrastructure, and result in substantial economic losses. If precipitation regulation could be realized, it could help mitigate the risks of disasters. However, controlling precipitation remains a formidable challenge due to the highly complex and uncertain dynamics of weather systems. To address this, we propose a novel control framework for precipitation management based on a numerical weather prediction (NWP) model and applied the framework for a series of warm bubble experiments, where the direction and amplitude of regional wind serve as the input and precipitation as the output. This approach investigates the potential of modifying regional wind patterns to effectively influence and regulate precipitation intensity and distribution.

    The proposed framework integrates a Sampling-Based Model Predictive Control (SBMPC) module to generate ideal control inputs for precipitation reduction and a novel Control Barrier Function (CBF) module to refine these inputs when discrepancies between the model and real weather systems are detected. The SBMPC combines the strengths of model predictive control and random sampling techniques to efficiently solve high-dimensional and nonlinear optimization problems. Inspired by ensemble prediction methods in numerical weather forecasting, the developed SBMPC module uses sampled control inputs to simulate potential system responses with a numerical weather prediction model and selects the input, whose corresponding output most closely aligned with the desired one, as the nominal control input. However, the effectiveness of the SBMPC module depends heavily on the accuracy of the NWP model, making it vulnerable to discrepancies between the simulated and real weather systems.

    To mitigate this limitation, our control framework incorporates a CBF module to ensure safety by enforcing constraints, such as maintaining precipitation intensities within predefined boundaries to prevent extreme weather events. Unlike conventional CBF methods, which rely on precise system dynamics, the CBF controller developed in this work reduces dependency on detailed models, making it particularly effective for managing the complexity and uncertainty inherent in weather systems.    

    The feasibility of the proposed framework is validated through simulations using the SCALE Regional Model (SCALE-RM), which emulates real-world weather systems. Results demonstrate that the proposed control framework effectively regulates precipitation to a safe level and maintains computational efficiency, offering a robust and practical solution for managing precipitation.