Precipitation nowcasting is critical for areas such as agriculture, water resource management, urban drainage systems, transport and disaster preparedness. In recent years, methods such as convolutional recurrent neural networks (ConvRNN) in deep learning techniques have been used to solve this task. Despite the effective improvement in forecasting quality, there are still problems with blurred and distorted prediction images, as well as difficulties in effectively forecasting high echo regions. To solve the above problems, this article presents a spatio-temporal long–short-term memory network model in view of multi-scale context fusion and attention mechanisms. This method fully extracts the short-term context information of different scales of radar image through the multi-scale context fusion module. The attention module broadens the time perception domain of the prediction unit so that the model perceives more historical time dynamics. Using the Hong Kong region weather radar data as a sample, the results of the experimental comparative analysis show that the spatio-temporal long and short-term memory network in view of multi-scale context fusion and attention mechanism achieves better prediction performance. Our model is effective in improving both image quality and meteorological assessment metrics with higher accuracy and more details.

Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of existing methods to extract and characterize complex spatiotemporal features from radar echo images remains insufficient, resulting in suboptimal forecasting accuracy. This paper proposes a novel extrapolation algorithm based on a dual-branch encoder–decoder and spatiotemporal Gated Recurrent Unit. In this model, the dual-branch encoder–decoder structure independently encodes radar echo images in the temporal and spatial domains, thereby avoiding interference between spatiotemporal information. Additionally, we introduce a Multi-Scale Channel Attention Module (MSCAM) to learn global and local feature information from each encoder layer, thereby enhancing focus on radar image details. Furthermore, we propose a Spatiotemporal Attention Gated Recurrent Unit (STAGRU) that integrates attention mechanisms to handle temporal evolution and spatial relationships within radar data, enabling the extraction of spatiotemporal information from a broader receptive field. Experimental results demonstrate the model’s ability to accurately predict morphological changes and motion trajectories of radar images on real radar datasets, exhibiting superior performance compared to existing models in terms of various evaluation metrics. This study effectively improves the accuracy of precipitation forecasting in radar echo images, provides technical support for the short-range forecasting of precipitation, and has good application prospects.