A Self-Supervised Precipitation Forecast Verification Based on Contrastive Learning

Abstract. Accurate precipitation forecast verification (PFV) is essential for improving forecasting models and supporting disaster management. However, current PFV methods remain limited, point-to-point methods are overly sensitive to minor errors, while spatial verification methods commonly require setting parameter and rule comprehensively, which constrains their availability. To tackle these issues, we are inspired by the success of deep learning in image verification through extracting high-level features, and thus propose a self-supervised contrastive learning-based PFV method (CLPFV). First, CLPFV uses precipitations augmentation (displacement, intensity, area size) to simulate actual forecast errors and construct positive and negative training sample pairs. Subsequently, with a novel loss function proportionally penalizing forecast errors, a backbone network is trained in CLPFV to extract high-level precipitation features. Finally, the cosine similarity of features is calculated as CLPFV’s verification score. Experiments demonstrate that CLPFV outperforms traditional (POD, FAR, TS) and spatial (FSS, SAL) verifications in different degrees of forecast errors and aligns better with expert assessments. In general, CLPFV offers an efficient deep learning solution for PFV tasks.