A global ocean reanalysis product in the China Ocean Reanalysis (CORA) project

Han, Guijun; Fu, Hongli; Zhang, Xuefeng; Li, Wei; Wu, Xinrong; Wang, Xidong; Zhang, Lianxin

doi:10.1007/s00376-013-2198-9

Cited by 20 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many ocean reanalysis projects have been carried out worldwide, such as SODA 5 , 6 (Simple Ocean Data Assimilation), ECCO 7 (Estimating the Circulation and Climate of the Ocean), NCEP-GODAS 8 (Global Ocean Data Assimilation System), HYCOM 9 (Hybrid Coordinate Ocean Model) and CORA (China Ocean Reanalysis). Particularly, the CORA, consisting of a global version 10 and a regional version 11 , 12 , is focused on coastal waters of China and its adjacent seas.…”

Section: Background and Summarymentioning

confidence: 99%

A reanalysis dataset of the South China Sea

Zeng

Peng

et al. 2014

Sci Data

View full text Add to dashboard Cite

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992–2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability.

show abstract

Section: Background and Summarymentioning

confidence: 99%

A reanalysis dataset of the South China Sea

Zeng

Peng

et al. 2014

Sci Data

View full text Add to dashboard Cite

show abstract

“…Based on the parallelized Princeton Ocean Model with a generalized coordinate system (POMgcs), satellite remote sensing sea surface temperature (SST), altimetry sea surface height anomaly (SSHA), and temperature/salinity profiles are assimilated using a sequential threedimensional variational (3D-Var) scheme implemented within a multigrid framework (Li et al, 2010;Li et al, 2008). Such a data assimilation process combines various oceanic observations with a dynamical ocean model to provide a reconstruction of the ocean state (Han et al, 2013a;Han et al, 2011;Han et al, 2013b), and with it regarded them as true fields in this study.…”

Section: Description Of Datamentioning

confidence: 99%

Ocean Reanalysis Data‐Driven Deep Learning Forecast for Sea Surface Multivariate in the South China Sea

Shao

Hou

et al. 2021

Earth and Space Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…shows the final level. It can be seen that the background error covariance matrix does not appear in (1), which has been represented implicitly by the grid levels. Compared to the traditional scheme of 3DVAR, the multigrid 3D-Var scheme has higher forecast accuracy and lower root-mean square errors.…”

Section: Data Assimilation Schemementioning

confidence: 99%

“…An ocean reanalysis system of the global ocean has been established recently by National Marine Data and Information Service (NMDIS) of China for the purpose of understanding monthly, annual, and interannual changes of sea surface height (SSH), as well as three-dimensional (3D) temperature, salinity, and currents. MITgcm (MIT general circulation model) serves as the ocean dynamical model in the reanalysis system [1], which is a state-of-the-art ocean model and is also employed in the Estimating the Circulation and Climate of the Ocean (ECCO) reanalysis project. The ocean data assimilation scheme used is a sequential 3D variational (3D-Var) analysis scheme designed to assimilate temperature and salinity using a multigrid framework [2].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Impact of Argo Data on Ocean Reanalysis in the Pacific Region

Zhang

Sun

Liu

et al. 2017

Advances in Meteorology

Self Cite

View full text Add to dashboard Cite

Observing System Simulation Experiments (OSSEs) have been conducted to evaluate the effect of Argo data assimilation on ocean reanalysis in the Pacific region. The "truth" is obtained from a 5-year model integration from 2003 to 2007 based on the MIT general circulation model with the truly varying atmospheric forcing. The "observations" are the projections of the truth onto the observational network including ocean station data, CTD, and various BTs and Argo, by adding white noise to simulate observational errors. The data assimilation method employed is a sequential three-dimensional variational (3D-Var) scheme within a multigrid framework. Results show the interannual variability of temperature, salinity, and current fields can be reconstructed fairly well. The spread of temperature anomalies in the tropical Pacific region is also able to be reflected accurately when Argo data is assimilated, which may provide a reliable initial field for the forecast of temperature and currents for the subsurface in the tropical Pacific region. The adjustment of salinity by using T-S relationship is vital in the tropical Pacific region. However, the adjustment of salinity is almost meaningless in the northwest Pacific if Argo data is included during the reanalysis.

show abstract

A global ocean reanalysis product in the China Ocean Reanalysis (CORA) project

Cited by 20 publications

References 27 publications

A reanalysis dataset of the South China Sea

A reanalysis dataset of the South China Sea

Ocean Reanalysis Data‐Driven Deep Learning Forecast for Sea Surface Multivariate in the South China Sea

Evaluation of the Impact of Argo Data on Ocean Reanalysis in the Pacific Region

Contact Info

Product

Resources

About