Reply on RC2

Abstract. Ocean fronts are critical interfaces between different water masses, profoundly influencing atmosphere–ocean interactions, weather systems, marine ecosystems, and climate regulation. Accurate and long-term observations of ocean fronts are essential for advancing studies in meteorology, oceanography, and climate science. However, no publicly available, long-term ocean front dataset currently exists, and existing detection methods often rely on time-consuming manual labeling or traditional algorithms with limited accuracy in complex frontal regions. In this study, we release the first publicly available 30-year ocean front dataset (1993–2023) for the Northwest Pacific, generated by applying a deep learning framework (Mask R-CNN) to daily sea surface temperature (SST) fields, with manually annotated samples for model training. The dataset provides pixel-level frontal boundaries along with associated attributes, including position, intensity, and width, stored in NetCDF-4 format at 1/12° spatial and daily temporal resolution. Accuracy evaluation shows a mean average precision (mAP) exceeding 0.90, with smaller errors in front width and intensity compared with traditional gradient-based methods, while capturing more small-scale features. The dataset offers three main contributions: (1) Filling the critical gap of a standardized, long-term ocean front product; (2) Serving as a ready-to-use training resource for deep learning models, greatly reducing the need for manual labeling; and (3) Providing benchmark samples for validation and intercomparison of other ocean front detection products. This dataset supports robust investigations of seasonal-to-interannual frontal variability and provides a valuable foundation for applications in meteorology, ecosystem management and climate change research.