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24.1 The Regional Ocean Modelling System and the IS4DVAR data assimilation algorithm 24.1 The Regional Ocean Modelling System and the IS4DVAR data assimilation algorithm
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24.1.1 The Forward Model 24.1.1 The Forward Model
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24.1.2 The IS4DVAR driver 24.1.2 The IS4DVAR driver
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24.2 ROMS IS4DVAR in a quasi-geostrophic domain: the East Australia Current 24.2 ROMS IS4DVAR in a quasi-geostrophic domain: the East Australia Current
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24.2.1 The East Australia Current ROMS application 24.2.1 The East Australia Current ROMS application
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24.2.2 IS4DVAR data assimilation in the EAC 24.2.2 IS4DVAR data assimilation in the EAC
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24.2.3 Impct in nowcast/forecast skill 24.2.3 Impct in nowcast/forecast skill
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24.3 ROMS IS4DVAR in a complex coastal domain: the Middle Atlantic Bight 24.3 ROMS IS4DVAR in a complex coastal domain: the Middle Atlantic Bight
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24.3.1 The Middle Atlantic Bight ROMS application: ESPRESSO 24.3.1 The Middle Atlantic Bight ROMS application: ESPRESSO
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24.3.2 Preparing the model for assimilation 24.3.2 Preparing the model for assimilation
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Model bias correction Model bias correction
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Modelling of the background error covariances Modelling of the background error covariances
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24.3.3 Observation pre-processing 24.3.3 Observation pre-processing
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Along-track sea surface height anomalies Along-track sea surface height anomalies
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Sea surface temperature Sea surface temperature
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24.3.4 IS4DVAR data assimilation in the MAB 24.3.4 IS4DVAR data assimilation in the MAB
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References References
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24 Data assimilation in coastal oceanography: IS4DVAR in the Regional Ocean Modelling System (ROMS)
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Published:October 2014
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Abstract
This chapter presents examples of variational data assimilation in coastal oceanography using the Regional Ocean Modeling System (ROMS). Realizing that satellite data is the only source of information in real time in most parts of the world ocean, the Ocean Modeling Group at Rutgers University has developed methodologies to exploit the information content in remotely sensed observations. This chapter evaluates the extent to which incremental, strong constraint, four-dimensional variational data assimilation (IS4DVAR) can improve prediction of mesoscale variability using ROMS. Examples of two applications of IS4DVAR in two very different dynamical regimes are presented: the East Australia Current (EAC) and the Middle Atlantic Bight (MAB). The two main sources of satellite information, namely sea surface temperature (SST) and sea surface height anomaly (SSHA), are found to be complementary, and therefore both need to be assimilated in order to approximate the three-dimensional structure of the ocean.
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