Graphical Abstract

Sato, T., and H. Kusaka, 2021: Statistical intercomparison of similarity metrics in sea level pressure pattern classification. J. Meteor. Soc. Japan, 99,
Early Online ReleaseGraphical Abstract NEW GA


Plain Language Summary: We compare the accuracy of five representative similarity metrics in extracting sea level pressure (SLP) patterns for accurate weather chart classification. 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.