Precipitation nowcasting is a crucial task with applications across various industries and services. Recent advancements in deep learning have enabled the use of radar imagery for precipitation nowcasting. This work proposes a fusion model that leverages both radar and satellite imagery to enhance precipitation nowcasting accuracy. Specifically, we employ a latent diffusion model called LDCast for local radar images and a large pretrained model called ClimaX for global satellite data. These models serve as baseline and foundation models, extracting temporal features. These features are then integrated within a U-Net architecture for final forecasting. We construct a local radar dataset, the Nha-Be Radar Dataset (NRD-1), and conduct experiments using NRD-1 as local data and ERA5 as global data. Despite being trained on a relatively small dataset, our model outperforms the baseline LDCast model. We further compare our model to a state-of-the-art precipitation nowcasting method to demonstrate its effectiveness.