Statistical Intercomparison of Similarity Metrics in Sea Level Pressure Pattern Classification

Abstract

In this study, we compare the accuracy of five representative similarity metrics in extracting sea level pressure (SLP) patterns for accurate weather chart classification: correlation coefficient, Euclidean distance (EUC), S1-score (S1), structural similarity (SSIM), and average hash. We use a large amount of teacher data to statistically evaluate the accuracy of each metric. The evaluation results reveal that S1 and SSIM have the highest accuracy in terms of both average and maximum scores. Their accuracy does not change even when non-ideal data are used as the teacher data. In addition, S1 and SSIM can reproduce the subjective resemblance between two maps better than EUC. However, EUC reproduces the central position of the signal in a sample case. This study can serve as a reference for identifying the most useful similarity metric for the classification of SLP patterns, especially when using non-ideal teacher data.