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Ocean Science An interactive open-access journal of the European Geosciences Union
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In order to improve forecasting skills of South China Sea Operational Forecasting System operated in NMEFC of China, comprehensive updates have been conducted to the configurations of physical model and data assimilation scheme. Scientific inter-comparison and accuracy assessment has been performed by employing GODAE IV-TT Class 4 metrics. The results indicate that remarkable improvements have been achieved in the new version of SCSOFS.
Preprints
https://doi.org/10.5194/os-2020-104
https://doi.org/10.5194/os-2020-104

  24 Nov 2020

24 Nov 2020

Review status: this preprint is currently under review for the journal OS.

The improvements to the regional South China Sea Operational Oceanography Forecasting System

Xueming Zhu1,2, Ziqing Zu1, Shihe Ren1, Yunfei Zhang1, Miaoyin Zhang1, and Hui Wang3,1 Xueming Zhu et al.
  • 1National Marine Environmental Forecasting Center, Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
  • 2Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
  • 3Institute of Marine Science and Technology, Shandong University, Qingdao, Shandong, 266237, China

Abstract. South China Sea Operational Oceanography Forecasting System (SCSOFS) had been built up and operated in National Marine Environmental Forecasting Center of China to provide daily updated hydrodynamic forecasting in SCS for the future 5 days since 2013. This paper presents comprehensive updates had been conducted to the configurations of the physical model and data assimilation scheme in order to improve SCSOFS forecasting skills in recent years. It highlights three of the most sensitive updates, sea surface atmospheric forcing method, tracers advection discrete scheme, and modification of data assimilation scheme. Scientific inter-comparison and accuracy assessment among five versions during the whole upgrading processes are performed by employing Global Ocean Data Assimilation Experiment OceanView Inter-comparison and Validation Task Team Class4 metrics. The results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version SCSOFSv1. Domain averaged monthly mean root mean square errors decrease from 1.21 °C to 0.52 °C for sea surface temperature, from 21.6 cm to 8.5 cm for sea level anomaly, respectively.

Xueming Zhu et al.

 
Status: open (until 19 Jan 2021)
Status: open (until 19 Jan 2021)
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Xueming Zhu et al.

Xueming Zhu et al.

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Short summary
In order to improve forecasting skills of South China Sea Operational Forecasting System operated in NMEFC of China, comprehensive updates have been conducted to the configurations of physical model and data assimilation scheme. Scientific inter-comparison and accuracy assessment has been performed by employing GODAE IV-TT Class 4 metrics. The results indicate that remarkable improvements have been achieved in the new version of SCSOFS.
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