Assimilation of sea-level anomalies and Argo data into HYCOM and its impact on the 24 hour forecasts in the western tropical and South Atlantic

Costa, Fernanda do N.; Tanajura, Clemente Augusto Souza

doi:10.1080/1755876x.2015.1014646

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…The SLA observations are derived by subtracting 20 year (1993–2012) mean sea surface height (

η_{MSSH}^{Obs}

) from instantaneous sea surface height (

η_{SSH}^{Obs}

) observations (AVISO, 2015; Mertz et al, 2017). Since SLA represents the variable part of the

η_{SSH}^{Obs}

, one can only update

η_{SSH}^{model}

based on the altimeter SLA, by simply adding the

η_{MSSH}^{model}

to the SLA measurements (e.g., Costa & Tanajura, 2015; Vidard et al, 2009; Zuo et al, 2019). This has the disadvantage of not reducing SSH biases, if any, in the climatological

η_{MSSH}^{model}

.…”

Section: Description Of the Assimilation System And Experimentsmentioning

confidence: 99%

“…The SLA observations are derived by subtracting 20 year (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) Costa & Tanajura, 2015;Vidard et al, 2009;Zuo et al, 2019). This has the disadvantage of not reducing SSH biases, if any, in the climatological η model MSSH .…”

Section: Assimilated Observationsmentioning

confidence: 99%

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Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

et al. 2020

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The Ensemble Adjustment Kalman Filter of the Data Assimilation Research Testbed is implemented to assimilate observations of satellite sea surface temperature, altimeter sea surface height, and in situ ocean temperature and salinity profiles into an eddy‐resolving 4 km Massachusetts Institute of Technology general circulation model of the Red Sea. We investigate the impact of three different ensemble generation strategies: (1) Iexp—uses ensemble of ocean states to initialize the model on 1 January 2011 and inflates filter error covariance by 10%; (2) IAexp—adds ensemble of atmospheric forcing to Iexp; and (3) IAPexp—adds perturbed model physics to IAexp. The assimilation experiments are run for 1 year, starting from the same initial ensemble and assimilating data every 3 days. Results demonstrate that the Iexp mainly improved the model outputs with respect to assimilation‐free Massachusetts Institute of Technology general circulation model run in the first few months, before showing signs of dynamical imbalances in the ocean estimates, particularly in the data‐sparse subsurface layers. The IAexp yielded substantial improvements throughout the assimilation period with almost no signs of imbalances, including the subsurface layers. It further well preserved the model mesoscale features resulting in an improved forecast for eddies, both in terms of intensity and location. Perturbing model physics in IAPexp slightly improved the forecast statistics and also the placement of basin‐scale eddies. Increasing hydrographic coverage further improved the results of IAPexp compared to IAexp in the subsurface layers. Switching off multiplicative inflation in IAexp and IAPexp leads to further improvements, especially in the subsurface layers.

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“…The SLA observations are derived by subtracting 20 year (1993–2012) mean sea surface height (

η_{MSSH}^{Obs}

) from instantaneous sea surface height (

η_{SSH}^{Obs}

) observations (AVISO, 2015; Mertz et al, 2017). Since SLA represents the variable part of the

η_{SSH}^{Obs}

, one can only update

η_{SSH}^{model}

based on the altimeter SLA, by simply adding the

η_{MSSH}^{model}

to the SLA measurements (e.g., Costa & Tanajura, 2015; Vidard et al, 2009; Zuo et al, 2019). This has the disadvantage of not reducing SSH biases, if any, in the climatological

η_{MSSH}^{model}

.…”

Section: Description Of the Assimilation System And Experimentsmentioning

confidence: 99%

Section: Assimilated Observationsmentioning

confidence: 99%

Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Since SLA represents the variable part of the , one can only update based on the altimeter SLA, by simply adding the to the SLA measurements (e.g. Vidard et al, 2009;Costa and Tanajura, 2015;Zuo et al, 2019). This has the disadvantage of not reducing SSH biases, if any, in the climatological .…”

Section: Assimilated Observationsmentioning

confidence: 99%

“…The SLA observations are derived by subtracting 20 year (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) Vidard et al, 2009;Costa and Tanajura, 2015;Zuo et al, 2019). This has the disadvantage of not reducing SSH biases, if any, in the climatological 𝜂 𝑀𝑆𝑆𝐻 𝑚𝑜𝑑𝑒𝑙 .…”

Section: Assimilated Observationsmentioning

confidence: 99%

Impact of Atmospheric and Model Physics Perturbations On a High-Resolution Ensemble Data Assimilation System of the Red Sea

Sanikommu

Toye

Zhan

et al. 2020

Preprint

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The Ensemble Adjustment Kalman Filter (EAKF) of the Data Assimilation Research Testbed (DART) is implemented to assimilate observations of satellite sea surface temperature, altimeter sea surface height and in situ ocean temperature and salinity profiles into an eddyresolving 4 km-Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. We investigate the impact of three different ensemble generation strategies (1) Iexpuses ensemble of ocean states to initialize the model on 1 st January, 2011 and inflates filter error covariance by 10%, (2) IAexpadds ensemble of atmospheric forcing to Iexp, and(3) IAPexpadds perturbed model physics to IAexp. The assimilation experiments are run for one year, starting from the same initial ensemble and assimilating data every three days.Results demonstrate that the Iexp mainly improved the model outputs with respect to assimilation-free MITgcm run in the first few months, before showing signs of dynamical imbalances in the ocean estimates, particularly in the data-sparse subsurface layers. The IAexp yielded substantial improvements throughout the assimilation period with almost no signs of imbalances, including the subsurface layers. It further well preserved the model mesoscale features resulting in an improved forecasts for eddies, both in terms of intensity and location.Perturbing model physics in IAPexp slightly improved the forecast statistics and also the placement of basin-scale eddies. Increasing hydrographic coverage further improved the results of IAPexp compared to IAexp in the subsurface layers. Switching off multiplicative inflation in IAexp and IAPexp leads to further improvements, especially in the subsurface layers.

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“…SODA and OFES reanalyzes are built on the Modular Ocean Model ocean developed at the Geophysical Fluid Dynamics Laboratory/the National Oceanic and Atmospheric Administration (GFDL/NOAA) [4][5][6][7]. As the US Navy's current operational ocean model, HYCOM is developed from the Miami Isopycnic Coordinate Ocean Model (MICOM) [1,8,9].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Several Ocean Reanalyzes about North Equatorial Current at 160° E

Wang

Mao

Chen

2020

JMSE

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Three ocean reanalyzes including Simple Ocean Data Assimilation (SODA), Hybrid Coordinate Ocean Model, and the Navy Coupled Ocean Data Assimilation (HYCOM+NCODA) analysis, and the Ocean General Circulation Model for the Earth Simulator (OFES) are assessed about their ability of depicting the structure of North Equatorial Current (NEC) at 160° E. We found that these products could reflect the structure of NEC relatively well at the whole section, but not at the single point through comparing their results with mooring and cruising measurement, and the OFES is the best choice to study mesoscale processes versus the other two reanalyzes through comparing their results with satellite measurement.

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Assimilation of sea-level anomalies and Argo data into HYCOM and its impact on the 24 hour forecasts in the western tropical and South Atlantic

Cited by 8 publications

References 32 publications

Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

Impact of Atmospheric and Model Physics Perturbations On a High-Resolution Ensemble Data Assimilation System of the Red Sea

Assessment of Several Ocean Reanalyzes about North Equatorial Current at 160° E

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