LAUSR

Radar echo extrapolation is a common approach to weather nowcasting, which has become a significant support to detect potential disastrous weather a few hours ahead. The dynamics pattern inside an echo intensity sequence is beneficial for echo prediction. However, existing extrapolation methods have limited ability to consider entire time-series echo context in an entire echo sequence from a given historical timestamp to a given future timestamp, thus leading to low long-term extrapolation accuracy. To solve this issue, we introduce spatiotemporal self-attention and propose a deep learning model named the nonlocal echo dynamics (NLED) network to capture the dependencies of the entire time domain. The NLED network has an encoder–decoder architecture for extrapolation. The encoder decomposes historical echoes into features of multiple spatial scales, which makes it better at learning echo dynamics from the global scale to the local scale. The decoder employs nonlocal blocks with sparse self-attention related to echo dynamics to learn correlations in the entire echo event, which is beneficial for predicting long-term echo distributions. Our model is evaluated on radar reflectivity datasets from Shanghai and Hong Kong. The experimental results indicate that the NLED model achieves a more accurate long-term forecast and alleviates the forgetting of stronger echo dynamics, thus validating the effectiveness of entire time-series modeling by the NLED network.