Detection of SST Fronts from a High-Resolution Model and Its Preliminary Results in the South China Sea
Shihe Ren, Xueming Zhu, Marie Drévillon, Hui Wang, Yunfei Zhang, Ziqing Zu, Ang Li
Abstract
Abstract A frontal detection algorithm is developed with the capability of detecting significant frontal segments of sea surface temperature (SST) in the high-resolution South China Sea Operational Forecasting System (SCSOFS). To effectively obtain frontal information, a gradient-based Canny edge detection algorithm is improved with postprocessing designed for high-resolution numerical models, aiming at extracting primary ocean fronts while ensuring the balance of frontal continuity and positioning accuracy. Metrics of frontal probability and strength are used to measure the robustness of the results in terms of mean state and seasonal variability of frontal activities in the South China Sea (SCS). Most fronts are found in the nearshore and form a strip shape extending from the Taiwan Strait to the coast of Vietnam. The SCSOFS is found to reproduce strong seasonal signals dominating the variability of the frontal strength and occurrence probability in the SCS. We implement the algorithm on the daily averaged SST derived from two other SST analyses for intercomparison in the SCS.