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

2020 ◽  
Author(s):  
Xueming Zhu ◽  
Ziqing Zu ◽  
Shihe Ren ◽  
Yunfei Zhang ◽  
Miaoyin Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
South China Sea ◽  
South China ◽  
Sea Surface ◽  
Global Ocean ◽  
China Sea ◽  
Data Assimilation Scheme ◽  
Operational Oceanography ◽  
Forecasting System ◽  
Assimilation Scheme

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.

scholarly journals The improvements to the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv1)

2021 ◽  
Author(s):  
Xueming Zhu ◽  
Ziqing Zu ◽  
Shihe Ren ◽  
Miaoyin Zhang ◽  
Yunfei Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
South China Sea ◽  
South China ◽  
Sea Surface ◽  
China Sea ◽  
Data Assimilation Scheme ◽  
Operational Oceanography ◽  
Forecasting System ◽  
Mean Square Errors ◽  
Assimilation Scheme

Abstract. South China Sea Operational Oceanography Forecasting System (SCSOFS) had been constructed 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 recent comprehensive updates of the configurations of the physical model and data assimilation scheme in order to improve SCSOFS forecasting skills. It highlights three of the most sensitive updates, including sea surface atmospheric forcing method, tracers advection discrete scheme, and modification of data assimilation scheme. Inter-comparison and accuracy assessment among five versions during the whole upgrading processes are performed by employing OceanPredict 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 known as 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.

On the modification of operational oceanography forecasting system for South China Sea in National Marine Environmental Forecasting Center of China

2020 ◽  
Author(s):  
Ziqing Zu ◽  
Xueming Zhu ◽  
Hui Wang
Keyword(s):  
South China Sea ◽  
South China ◽  
Optimal Interpolation ◽  
Physical Parameters ◽  
Background Error ◽  
Pass Filter ◽  
China Sea ◽  
Operational Oceanography ◽  
Forecasting System ◽  
Marine Environmental

<p>Based on ROMS and Ensemble Optimal Interpolation (EnOI) method, the South China Sea operational Oceanography Forecasting System (SCSOFS) is implemented in National Marine Environmental Forecasting Center (NMEFC), to provide the forecast of the currents, temperature and salinity in South China Sea for the future 5 days. Recently, a systematic modification has been carried out to SCSOFS to improve its forecast skill.</p><p>For the data assimilation system, new methods have been implemented, such as using Increment Analysis Update (IAU) and First Guess at Appropriate Time (FGAT), using a high-pass filter to evaluate the background error, assimilating multi-source observations, using non-uniform localization radius. In addition, the respective contribution of each method will also be discussed.</p><p>An optimization system is implemented for evaluating the values of physical parameters in ROMS, to remove the long-term bias of simulation. Argo temperature profiles is assimilated in the first half of 2017, to obtain the optimal coefficients of horizontal/vertical viscosity/diffusion and linear bottom drag. An independent validation from July of 2017 to December of 2018 shows that the simulation is improved using the optimal values.</p>

The improvements to the numerical model of South China Sea Ocean Circulations

2020 ◽  
Author(s):  
Xueming Zhu ◽  
Hui Wang ◽  
Ziqing Zu
Keyword(s):  
Numerical Model ◽  
South China Sea ◽  
South China ◽  
Horizontal Resolution ◽  
Forecast Skill ◽  
Sea Surface ◽  
China Sea ◽  
Terrain Following ◽  
Direct Forcing ◽  
Forecasting System

<p>The South China Sea (SCS) ocean circulations numerical model has been build up based on ROMS with high horizontal resolution. It had been operated in NMEFC to provide daily updated the hydrodynamic forecasting in SCS for the future 5 days since 2013, and named as the SCS operational Oceanography Forecasting System (SCSOFS). Recently, a few systematic optimizations have been carried out to the configuration of the physical model to improve SCSOFS forecast skill. For example, the differential schemes of horizontal and vertical advection of tracers are changed from 4<sup>th</sup>-order centered to 4<sup>th</sup>-ordered Akima, the schemes of horizontal mixing of tracers are changed from along epineutral surfaces to along geopotential surfaces, in order to correct for the spurious diapycnal diffusion of the advection operator in terrain-following coordinates, which could cause anomaly temperature increasing about 1 centigrade in deep layer. The method of sea surface atmospheric forcing is changed from direct forcing to bulk formula, by introducing the negative feedback effects between ocean and atmosphere, in order to improve forecast skill of sea surface temperature.</p>

scholarly journals Purpleback Flying Squid Sthenoteuthis oualaniensis in the South China Sea: Growth, Resources and Association with the Environment

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Chunxu Zhao ◽  
Chunyan Shen ◽  
Andrew Bakun ◽  
Yunrong Yan ◽  
Bin Kang
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Biological Characteristics ◽  
Trophic Levels ◽  
Sea Surface ◽  
Height Anomaly ◽  
The South ◽  
China Sea ◽  
Sthenoteuthis Oualaniensis

The purpleback flying squid (Ommastrephidae: Sthenoteuthis oualaniensis) is an important species at higher trophic levels of the regional marine ecosystem in the South China Sea (SCS), where it is considered to show the potential for fishery development. Accordingly, under increasing climatic and environmental changes, understanding the nature and importance of various factors that determine the spatial and temporal distribution and abundance of S. oualaniensis in the SCS is of great scientific and socio-economic interest. Using generalized additive model (GAM) methods, we analyzed the relationship between available environmental factors and catch per unit effort (CPUE) data of S. oualaniensis. The body size of S. oualaniensis in the SCS was relatively small (<19.4 cm), with a shorter lifespan than individuals in other seas. The biological characteristics indicate that S. oualaniensis in the SCS showed a positive allometric growth, and could be suitably described by the logistic growth equation. In our study, the sea areas with higher CPUE were mainly distributed at 10°–11° N, with a 27–28 °C sea surface temperature (SST) range, a sea surface height anomaly (SSHA) of −0.05–0.05 m, and chlorophyll-a concentration (Chl-a) higher than 0.18 μg/L. The SST was the most important factor in the GAM analysis and the best fitting GAM model explained 67.9% of the variance. Understanding the biological characteristics and habitat status of S. oualaniensis in the SCS will benefit the management of this resource.

Application of deep learning technique to the sea surface height prediction in the South China Sea

2021 ◽  
Vol 40 (7) ◽  
pp. 68-76
Author(s):  
Tao Song ◽  
Ningsheng Han ◽  
Yuhang Zhu ◽  
Zhongwei Li ◽  
Yineng Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
South China Sea ◽  
South China ◽  
Sea Surface Height ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
China Sea ◽  
Learning Technique ◽  
Height Prediction
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