Hybrid Weighting Loss for Precipitation Nowcasting from Radar Images

Abstract

Precipitation nowcasting is gaining increasing attention in the signal processing community. Existing deep learning-based studies focus on designing an effective model architecture, neglecting the influence of the severe imbalanced distribution of rainfall data that can compromise the predictive accuracy on heavy rainfall intensities. To address the uneven distribution of precipitation nowcasting data, we propose a novel data reweighting strategy, termed Hybrid Weighting, which hybrids reweighting and non-weighting strategies together, boosting the precipitation nowcasting performance. Experimental results on two natural radar echo benchmark datasets demonstrate the superior performance of our proposed approach for precipitation nowcasting over existing loss functions on high rainfall intensities, without degenerating on low rainfall intensities compared with state-of-art reweighting methods.