An Integrated Multi‐Component Error Verification Method for Numerical Weather Prediction
Errors in weather forecasts remain a critical challenge for high‐resolution numerical weather prediction (NWP) and artificial intelligence forecast models. Traditional verification approaches, such as Threat Score (TS) and Equitable Threat Score (ETS), suffer from “double penalty” issues due to spatial‐temporal mismatches and lack of spatial distribution fidelity. In this study, we introduce an integrated multi‐component error verification approach (IMEV) that decomposes forecast errors into three physically interpretable components: accumulation error (systematic intensity discrepancy), area error (areal coverage and spatial extent mismatch), and pattern error (distributional dissimilarity and organization difference). Pattern error is evaluated by the similarity of intensity distribution features between the forecast and observation fields. These intensity distribution features are represented using probability density histograms of meteorological elements. Pattern error is quantified using Jensen‐Shannon divergence of probability density distributions, avoiding complex feature matching while capturing spatial pattern discrepancies. Two representative precipitation cases are analyzed to validate this method. Results demonstrate that the proposed method aligns closely with subjective analysis, outperforming traditional metrics like TS and ETS in capturing spatial distribution discrepancies. By prioritizing dominant error dimensions (accumulation, area, and pattern), the method downplays fine‐scale spatial details while preserving essential distributional characteristics through probability density analysis. Applying the IMEV algorithm within small‐sized sliding windows can, to a certain extent, allow for partial sensitivity to spatial displacements. This avoids computationally intensive feature matching, enabling efficient error quantification.