Radar Echo Extrapolation

Radar echo extrapolation aims to predict future radar reflectivity patterns from past observations, primarily for improving short-term precipitation forecasting. Recent research focuses on deep learning models, particularly transformer-based architectures, designed to effectively capture the complex spatiotemporal dynamics of radar echoes while mitigating issues like cumulative error propagation and the accurate representation of sparse data. These advancements offer improved accuracy and efficiency compared to traditional methods, leading to more reliable and timely weather predictions, particularly for severe weather events. The ongoing challenge lies in enhancing the generalization and stability of these deep learning models for diverse weather conditions and radar datasets.