An evaluation of sea surface height assimilation using along-track and gridded products based on the Regional Ocean Modeling System (ROMS) and the four-dimensional variational data assimilation

2018 ◽  
Vol 37 (9) ◽  
pp. 50-58 ◽  
Author(s):  
Chaojie Zhou ◽  
Xiaohua Ding ◽  
Jie Zhang ◽  
Jungang Yang ◽  
Qiang Ma
Keyword(s):  
Data Assimilation ◽  
Sea Surface Height ◽  
Variational Data Assimilation ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Regional Ocean Modeling System ◽  
Along Track

scholarly journals A STUDY OF THE IMPACT OF ALTIMETRY DATA ASSIMILATION ON SHORT-TERM PREDICTABILITY OF THE HYCOM OCEAN MODEL IN REGIONS OF THE TROPICAL AND SOUTH ATLANTIC OCEAN

2013 ◽  
Vol 31 (2) ◽  
pp. 271 ◽  
Author(s):  
Leonardo Nascimento Lima ◽  
Clemente Augusto Souza Tanajura
Keyword(s):  
Data Assimilation ◽  
Sea Surface Height ◽  
Ocean Model ◽  
South Atlantic ◽  
Sea Surface ◽  
Optimal Interpolation ◽  
Altimetry Data ◽  
Hybrid Coordinate Ocean Model ◽  
Along Track ◽  
The Impact

ABSTRACT. In this study, assimilation of Jason-1 and Jason-2 along-track sea level anomaly (SLA) data was conducted in a region of the tropical and South Atlantic (7◦N-36◦S, 20◦W up to the Brazilian coast) using an optimal interpolation method and the HYCOM (Hybrid Coordinate Ocean Model). Four 24 h-forecast experiments were performed daily from January 1 until March 31, 2011 considering different SLA assimilation data windows (1 day and 2 days) and different coefficients in the parameterization of the SLA covariance matrix model. The model horizontal resolution was 1/12◦ and the number of vertical layers was 21. The SLA analyses added to the mean sea surface height were projected to the subsurface with the Cooper & Haines (1996) scheme. The results showed that the experiment with 2-day window of along-track data and with specific parameterizations of the model SLA covariance error for sub-regions of the METAREA V was the most accurate. It completely reconstructed the model sea surface height and important improvements in the circulation were produced. For instance, there was a substantial improvement in the representation of the Brazil Current and North Brazil Undercurrent. However, since no assimilation of vertical profiles of temperature and salinity and of sea surface temperature was performed, the methodology employed here should be considered only as a step towards a high quality analysis for operational forecasting systems.   Keywords: data assimilation, optimal interpolation, Cooper & Haines scheme, altimetry data.   RESUMO. Neste estudo, a assimilação de dados de anomalia da altura da superfície do mar (AASM) ao longo da trilha dos satélites Jason-1 e Jason-2 foi conduzida em uma região do Atlântico tropical e Sul (7◦N-36◦S, 20◦W até a costa do Brasil) com o método de interpolação ótima e o modelo oceânico HYCOM (Hybrid Coordinate Ocean Model). Foram realizados quatro experimentos de previsão de 24 h entre 1 de janeiro e 31 de março de 2011, considerando diferentes janelas de assimilação de AASM (1 dia e 2 dias) e diferentes coeficientes na parametrização da matriz de covariância dos erros de AASM do modelo. A resolução horizontal empregada no HYCOM foi 1/12◦ para 21 camadas verticais. As correções de altura da superfície do mar devido à assimilação de AASM foram projetadas abaixo da camada de mistura através da técnica de Cooper & Haines (1996). Os resultados mostraram que o experimento com assimilação de dados ao longo da trilha dos satélites com a janela de 2 dias e com parametrizações da matriz de covariância específicas para sub-regiões da METAREA V foi o mais acurado. Ele reconstruiu completamente a altura da superfície do mar e também proporcionou melhorias na circulação oceânica reproduzida pelo modelo. Por exemplo, houve substancial melhoria da representação nos campos da Corrente do Brasil e Subcorrente Norte do Brasil. Entretanto, tendo em vista que não foi realizada a assimilação de perfis verticais de temperatura e de salinidade e da temperatura da superfície do mar, a metodologia apresentada deve ser considerada apenas como um passo na conquista de uma análise oceânica e de um sistema previsor de qualidade para fins operacionais.   Palavras-chave: assimilação de dados, interpolação ótima, técnica de Cooper & Haines, dados de altimetria.

The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems

2011 ◽  
Vol 91 (1) ◽  
pp. 74-94 ◽  
Author(s):  
Andrew M. Moore ◽  
Hernan G. Arango ◽  
Gregoire Broquet ◽  
Chris Edwards ◽  
Milena Veneziani ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System

A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System

2008 ◽  
Vol 25 (11) ◽  
pp. 2074-2090 ◽  
Author(s):  
Zhijin Li ◽  
Yi Chao ◽  
James C. McWilliams ◽  
Kayo Ide
Keyword(s):  
Data Assimilation ◽  
Bottom Topography ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Ocean Modeling ◽  
Small Scale ◽  
Regional Ocean Modeling System ◽  
Dynamic Constraints ◽  
The Cost ◽  
Assimilation Scheme

Abstract A three-dimensional variational data assimilation (3DVAR) scheme has been developed within the framework of the Regional Ocean Modeling System (ROMS). This ROMS3DVAR enables the capability of predicting meso- to small-scale variations with temporal scales from hours to days in coastal oceans. To cope with particular difficulties that result from complex coastlines and bottom topography, unbalanced flows, and sparse observations, ROMS3DVAR includes novel strategies. These strategies include the implementation of three-dimensional anisotropic and inhomogeneous error correlations based on a Kronecker product, application of particular weak dynamic constraints, and implementation of efficient and reliable algorithms for minimizing the cost function. The formulation of ROMS3DVAR is presented here, and its implementation off the West Coast is currently under way.

scholarly journals Study Of Albacore Tuna (Thunnus alalunga) Abundance Using Regional Ocean Modeling System (ROMS) Data In Indian Ocean

2018 ◽  
Vol 2 (2) ◽  
pp. 76
Author(s):  
Dian Novianto ◽  
Takahiro Osawa ◽  
I Wayan Nuarsa
Keyword(s):  
Current Velocity ◽  
Sea Surface Height ◽  
Environmental Variable ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Regional Ocean Modeling System ◽  
Catch Data ◽  
Albacore Tuna ◽  
Thunnus Alalunga ◽  
The Relationship

First, we analysed alabcore catch data based on time, positions, and layer alabcore caught and ROMS result data monthly climatology data for temperature, salinity. current velocity, and sea surface height for 2005–2008. Then, we analyzed the relationship between catch data and ROMS data by combining the statistical method of regression trogh origin (RTO)  and geographic information system (GIS). Three model RTO were generated with the abundance of albacore tuna as a response variable, and  temperature, salinity. current velocity, and sea surface height as predictor variables. All of the predictors of  temperature, salinity. current velocity, and sea surface height were highly significant (P < 0.001) to the number of albacore tuna. Values of temperature, salinity. current velocity, and sea surface height in albacore tuna preferences ranged from 220 to 230 C, 34.79 to 34.84 Psu, 0.01 to 0.03 m/s and 0.66 to 0.70 m, respectively. Validation of the predicted number ofalbacore tuna with the observed value was significant (P < 0.05, r2 = 0.60). sea surface height was the most important environmental variable to the number of albacore tuna caught, followed by temperature, salinity and current velocity.

The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems

2011 ◽  
Vol 91 (1) ◽  
pp. 34-49 ◽  
Author(s):  
Andrew M. Moore ◽  
Hernan G. Arango ◽  
Gregoire Broquet ◽  
Brian S. Powell ◽  
Anthony T. Weaver ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System

Predictability of Mesoscale Variability in the East Australian Current Given Strong-Constraint Data Assimilation

2012 ◽  
Vol 42 (9) ◽  
pp. 1402-1420 ◽  
Author(s):  
Javier Zavala-Garay ◽  
J. L. Wilkin ◽  
H. G. Arango
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Empirical Relationship ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Initial Time ◽  
East Australian Current ◽  
Mesoscale Variability ◽  
Regional Ocean Modeling System ◽  
Strong Constraint

Abstract One of the many applications of data assimilation is the estimation of adequate initial conditions for model forecasts. In this work, the authors evaluate to what extent the incremental, strong-constraint, four-dimensional variational data assimilation (IS4DVAR) can improve prediction of mesoscale variability in the East Australian Current (EAC) using the Regional Ocean Modeling System (ROMS). The observations considered in the assimilation experiments are daily composites of satellite sea surface temperature (SST), 7-day average reanalysis of satellite altimeter sea level anomalies, and subsurface temperature profiles from high-resolution expendable bathythermograph (XBT). Considering all available observations for years 2001 and 2002, ROMS forecast initial conditions are generated every week by assimilating the available observations from the 7 days prior to the forecast initial time. It is shown that assimilation of surface information only [SST and sea surface height (SSH)] results in poor estimates of the true subsurface ocean state (as depicted by the XBTs) and therefore poor forecast skill of subsurface conditions. Including the XBTs in the assimilation experiments improves the ocean state estimation in the vicinity of the XBT transects. By introducing subsurface pseudo-observations (which are called synthetic CTD) based on an empirical relationship between satellite surface observations and subsurface variability, the authors find a significant improvement in ocean state estimates that leads to skillful forecasts for up to 2 weeks in the domain considered.

The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems

2011 ◽  
Vol 91 (1) ◽  
pp. 50-73 ◽  
Author(s):  
Andrew M. Moore ◽  
Hernan G. Arango ◽  
Gregoire Broquet ◽  
Chris Edwards ◽  
Milena Veneziani ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System
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