Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-995-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-995-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
03 Feb 2022
Development and technical paper |
| 03 Feb 2022
| 03 Feb 2022
Improvements in the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv2)
Xueming Zhu
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai, 519000, China
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Ziqing Zu
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Miaoyin Zhang
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Yunfei Zhang
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Hui Wang
CORRESPONDING AUTHOR
Institute of Marine Science and Technology, Shandong University,
Qingdao, Shandong, 266237, China
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Ang Li
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
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Cited
12 citations as recorded by crossref.
- MEC: A Mesoscale Events Classifier for Oceanographic Imagery G. Pieri et al. 10.3390/app13031565
- Numerical Weather Prediction of Sea Surface Temperature in South China Sea Using Attention-Based Context Fusion Network H. He et al. 10.3390/rs16203793
- Improvements of ocean fronts forecasting in the South China Sea by using ensemble optimal interpolation assimilation S. Ren et al. 10.1088/1742-6596/2486/1/012033
- Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application Z. Chen et al. 10.3389/fmars.2022.985048
- Controlling factor analysis of oceanic surface pCO2 in the South China Sea using a three-dimensional high-resolution biogeochemical model M. Zhang et al. 10.3389/fmars.2023.1155979
- Removing the systematic errors of the model in operational oceanography forecasting system using data assimilation method Z. Zu et al. 10.1088/1742-6596/2718/1/012027
- Spatiotemporal characteristics of water exchange between the Andaman Sea and the Bay of Bengal Y. Wang et al. 10.1007/s13131-024-2317-8
- Model validation and applications of wave and current forecasts from the Hong Kong Observatory's Operational Marine Forecasting System W. Kong et al. 10.1016/j.ocemod.2024.102393
- Impact of typhoons on the ecological environment of the Pearl River Estuary in the summer of 2021—a study of an algal bloom event X. Zhang et al. 10.3389/fmars.2024.1395804
- Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China P. Lu et al. 10.3390/foods12030542
- Oceanic and ecological response to native Typhoons Cempaka and Lupit (2021) along the northern South China Sea continental shelf: comparison and evaluation of global and regional Operational Oceanography Forecasting Systems S. Guo et al. 10.3389/fmars.2023.1175263
- Inversion of Initial Field Based on a Temperature Transport Adjoint S. Jiao et al. 10.3390/jmse9070760
11 citations as recorded by crossref.
- MEC: A Mesoscale Events Classifier for Oceanographic Imagery G. Pieri et al. 10.3390/app13031565
- Numerical Weather Prediction of Sea Surface Temperature in South China Sea Using Attention-Based Context Fusion Network H. He et al. 10.3390/rs16203793
- Improvements of ocean fronts forecasting in the South China Sea by using ensemble optimal interpolation assimilation S. Ren et al. 10.1088/1742-6596/2486/1/012033
- Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application Z. Chen et al. 10.3389/fmars.2022.985048
- Controlling factor analysis of oceanic surface pCO2 in the South China Sea using a three-dimensional high-resolution biogeochemical model M. Zhang et al. 10.3389/fmars.2023.1155979
- Removing the systematic errors of the model in operational oceanography forecasting system using data assimilation method Z. Zu et al. 10.1088/1742-6596/2718/1/012027
- Spatiotemporal characteristics of water exchange between the Andaman Sea and the Bay of Bengal Y. Wang et al. 10.1007/s13131-024-2317-8
- Model validation and applications of wave and current forecasts from the Hong Kong Observatory's Operational Marine Forecasting System W. Kong et al. 10.1016/j.ocemod.2024.102393
- Impact of typhoons on the ecological environment of the Pearl River Estuary in the summer of 2021—a study of an algal bloom event X. Zhang et al. 10.3389/fmars.2024.1395804
- Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China P. Lu et al. 10.3390/foods12030542
- Oceanic and ecological response to native Typhoons Cempaka and Lupit (2021) along the northern South China Sea continental shelf: comparison and evaluation of global and regional Operational Oceanography Forecasting Systems S. Guo et al. 10.3389/fmars.2023.1175263
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
SCSOFS has provided daily updated marine forecasting in the South China Sea for the next 5 d since 2013. Comprehensive updates have been conducted to the configurations of SCSOFS's physical model and data assimilation scheme in order to improve its forecasting skill. The three most sensitive updates are highlighted. Scientific comparison and accuracy assessment results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version SCSOFSv1.
SCSOFS has provided daily updated marine forecasting in the South China Sea for the next 5 d...
