TEDR: A spatiotemporal attention radar extrapolation network constrained by optical flow and distribution correction

Abstract

In recent years, deep learning has been widely applied to meteorological radar extrapolation due to the shortcomings of traditional optical flow methods in predicting the genesis and dissipation of radar echoes. However, it still faces challenges in addressing issues of clarity and overall intensity attenuation caused by uncertainty. This study implemented a dual-path spatiotemporal attention network that integrates optical flow techniques by employing intra-frame static attention and inter-frame dynamic attention, which could simulate motion fields and the overall intensity distribution of radar echoes separately. Our approach effectively resolve the issues of systematic intensity attenuation and clarity degradation introduced by deep learning methods. Through the comparisons of key metrics such as MSE, SSIM, CSI20, CSI30, and CSI40, the results demonstrated significant improvements over traditional approaches, particularly in CSI30 and CSI40, where the metrics improved by more than 35 %.