An individual motion driven CNN-Based AI method for precipitation forecasting Using RADAR Image Sequence

Precipitation forecasting, especially with high spatial resolution and accurate intensity estimation, remains a critical challenge in the field of Artificial Intelligence (AI). Existing AI-based forecasting models often struggle with key limitations, including mismatched precipitation motion patterns, blurred precipitation field generation, and inaccurate intensity predictions. These issues largely arise from conventional models simulating average motion and neglecting individual motion—which refers to the unique speed, trajectory, and direction of a single precipitation event. To address these limitations, we propose an Individual Motion Driven AI (IMD-AI) method based on a Convolutional Neural Network (CNN). This approach incorporates motion alignment and pattern grouping techniques to correct mismatches in individual motion estimation, thereby enabling more accurate and intact regional precipitation forecasting. Our CNN architecture is designed to extract spatial features from RADAR image sequences and map them directly to real-world parameters such as precipitation intensity, humidity, wind speed, and atmospheric pressure. Furthermore, to enhance precision and sharpness, we integrate strategies like patch embedding, schedule sampling, and adversarial training under the SPA framework. These additions mitigate the tendency of AI models to filter out high-frequency details, improving the model’s ability to preserve fine-scale patterns in precipitation fields. The final system is deployed through a web-based application, allowing users to upload RADAR images and instantly receive multiple weather parameter predictions with high reliability and accuracy.