Abstract
Radar reflectivity plays a crucial role in detecting heavy rainfall and is an important tool for meteorological analysis. However, the coverage of a single radar is limited, leading to the use of satellite data as a complementary source. Consequently, how to bridge the gap between radar and satellite data has become a growing research focus. In this paper, we present MAFormer, a novel model for reconstructing radar reflectivity using satellite data within the Transformer framework. MAFormer consists of two modules: the Axial Local Attention Module and the Mixup Global Attention Module, which extract both local saliency and global similarity. Quantitative and qualitative experiments demonstrate the effectiveness of our proposed method. Specifically, the MAFormer model exhibits notable advancements when compared to state-of-the-art deep learning techniques. It demonstrates an improvement ranging from 0.01 to 0.05 in terms of the Heidke skill score, indicating its superior performance. Additionally, MAFormer effectively mitigates false alarm rates by approximately 0.016 to 0.04, which further highlights its enhanced accuracy and reliability.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
