Artificial Intelligence Weather Radar

Abstract The integration of artificial intelligence (AI) with weather radar systems marks a transformative advancement in remote sensing. This study introduces an AI‐powered radar system that utilizes Long Short‐Term Memory (LSTM) neural networks to predict the in‐phase ( I ) and quadrature ( Q ) components of radar signals, enabling faster, and more accurate radar observations By synthesizing extended time series from a subset of real‐time measurements, the AI radar enhances measurement accuracy and spatial resolution without requiring longer dwell times. The proposed technique reduces data collection time and storage demands while maintaining the statistical and spectral characteristics of radar signals. Applied to both simulated and measured radar data, the AI radar demonstrates promising results in improving signal prediction and radar observations across ground‐based, airborne, and spaceborne platforms. This innovation paves the way for more efficient radar technologies, with potential applications in weather monitoring, military systems, and resource‐constrained environments.