A new version of regional ocean reanalysis for coastal waters of China and adjacent seas

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

doi:10.1007/s00376-012-2195-4

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…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

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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.

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Section: Background and Summarymentioning

confidence: 99%

A reanalysis dataset of the South China Sea

Zeng

Peng

et al. 2014

Sci Data

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“…This study employed a SCS reanalysis product as part of the China Ocean ReAnalysis version 1.0 (CORA in brief) dataset obtained from http://www.cmoc-china.cn/ (Han et al, 2011;Han et al, 2013b). 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).…”

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

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“…A one-month free model run using the above POMgcs from the CORA [19,[21][22][23][24] reanalysis datasets on March 16, 2005, as the initial conditions and forced by the above open boundary condition and meteorological forcing is applied to generate TRUTH run results from March 16 to April 16, 2005. Synthetic temperature and salinity observations are constructed by interpolating the TRUTH run results to some preset positions spatially and temporally. Instead of using highly inhomogeneous real observational positions which are not fit for making it clear to analyze the advantage of the different data assimilation schemes in this study domain, spatially and temporally homogeneous positions are employed.…”

Section: Experiments Setupmentioning

confidence: 99%

A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation

Yang

et al. 2017

Advances in Meteorology

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This study addresses how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA). It is well known that the oceanic mixing across the potential density surface is much weaker than that along the potential density surface. However, traditional ODA schemes allow the mixing across the potential density surface and thus may result in extra assimilation errors. Here, a new ODA scheme that uses potential density gradient information of the model background to rescale observational adjustment is designed to improve the quality of assimilation. The new scheme has been tested using a regional ocean model within a multiscale 3-dimensional variational framework. Results show that the new scheme effectively prevents the excessive unphysical projection of observational information in the direction across potential density surface and thus improves assimilation quality greatly. Forecast experiments also show that the new scheme significantly improves the model forecast skills through providing more dynamically consistent initial conditions

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A new version of regional ocean reanalysis for coastal waters of China and adjacent seas

Cited by 13 publications

References 26 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

A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation

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