Design of an Entropy-Based Meteorological Monitoring and Early Warning Model for Weather Modification Operations

AbstractWeather modification operations require fast judgment from dispersed stations, heterogeneous sensors,and unstable communication links. To support operational early warning, this paper proposes an IoT-basedmeteorological monitoring model that combines data quality control, information entropy, an ExtremeWeather Index (EWI), and dynamic communication scheduling. Multivariate observations are normalized andconverted into entropy sequences to describe the coordinated change of temperature, humidity, pressure,wind, and precipitation. The EWI integrates abrupt entropy change, high-value aggregation, and trend slope,while communication resources are adjusted according to risk level and link quality. Hardware-in-the-loopsimulation shows that the proposed method improves F1 score, reduces missed alarms, and keeps responsetime suitable for short-term weather modification command. The model provides an interpretable anddeployable framework for meteorological monitoring and early warning.