A global daily mesoscale front dataset from satellite observations: in situ validation and cross-dataset comparison

Abstract. Ocean fronts have garnered significant attention from researchers across various scientific disciplines due to their profound ecological and climatic impacts. The development of front detection algorithms has enabled the automatic extraction of frontal information from satellite observations, providing valuable tools for understanding the biophysical interactions within marine ecosystems. However, the lack of comprehensive validation and comparison of cross-satellite products against in situ observations, along with limited accessibility to frontal datasets, must be addressed to enable the broader application of front detection algorithms. This study promoted the improved histogram-based front detection algorithm to global oceans with additional enhancements, generating the first publicly available, high-resolution, daily global mesoscale front dataset spanning from 1982 to 2023 (Xing et al., 2024a, https://doi.org/10.5281/zenodo.14373832). Global validation using in situ underway observations shows that most in situ and satellite-detected fronts can be matched with each other, with high temporal and spatial consistency, demonstrating the dataset's acceptable performance in detecting fronts. Cross-dataset comparisons reveal that multi-satellite blended products offer the best front detection performance, followed by observation-assimilated ocean model products, while single-satellite and purely simulated products show the lowest performance. In addition, in situ observations show a strong spatial resemblance to global frontal frequency, providing independent validation of the satellite-based global frontal occurrence map. These results enhance confidence in the application of satellite-based front detection, and our global front dataset and detection algorithm may be valuable for both regional and global studies in marine ecology, fisheries, ocean dynamics, and climate change.