Editorial Type:
Article
Article Type:
Research Article

Detection of SST Fronts from a High-Resolution Model and Its Preliminary Results in the South China Sea

Shihe Ren Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, China

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Xueming Zhu Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, China

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Marie Drevillon Mercator Océan, Ramonville Saint Agne, France

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Hui Wang Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, China
Institute of Marine Science and Technology, Shandong University, Qingdao, Shandong, China

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Yunfei Zhang Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, China

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Ziqing Zu Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, China

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Ang Li Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, China

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Online Publication:
18 Feb 2021
Print Publication:
01 Feb 2021
DOI:
https://doi.org/10.1175/JTECH-D-20-0118.1
Page(s):
387–403
Restricted access

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.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xueming Zhu, zhuxm@nmefc.cn

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.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xueming Zhu, zhuxm@nmefc.cn
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