Observing system experiments over the Atlantic Ocean with the REMO ocean data assimilation system (RODAS) into HYCOM

Tanajura, Clemente Augusto Souza; Mignac, Davi; Santana, Ana Lúcia Almeida; Costa, Fernanda do N.; Lima, Leonardo; Belyaev, Konstantin; Zhu, Jiang

doi:10.1007/s10236-019-01309-8

Cited by 14 publications

(14 citation statements)

References 56 publications

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“…T-SIS employs a version of the well-known Optimal Interpolation scheme, favoring computational efficiency, and was designed particularly to be implemented with HYCOM. At the same time, REMO was able to create the REMO Ocean Data Assimilation System (RODAS) based on the Ensemble Optimal Interpolation scheme -EnOI (Melo et al, 2013;Tanajura et al, 2014;Mignac et al, 2015;Carvalho et al, 2019;Tanajura et al, 2020), which has been implemented in the Navy operational forecasting system. A detailed description of RODAS is presented by Tanajura et al (2020).…”

Section: The Oceanographic Modeling and Observation Network (Remo)mentioning

confidence: 99%

“…At the same time, REMO was able to create the REMO Ocean Data Assimilation System (RODAS) based on the Ensemble Optimal Interpolation scheme -EnOI (Melo et al, 2013;Tanajura et al, 2014;Mignac et al, 2015;Carvalho et al, 2019;Tanajura et al, 2020), which has been implemented in the Navy operational forecasting system. A detailed description of RODAS is presented by Tanajura et al (2020). Both schemes can assimilate vertical profiles of temperature (T) and salinity (S) from the ARGO system, XBTs, CTDs, gridded or along-track sea level anomaly data, and sea surface temperature fields.…”

Section: The Oceanographic Modeling and Observation Network (Remo)mentioning

confidence: 99%

“…As discussed by Tanajura et al (2020) on the description of the EnOI, the observational error covariance matrix depends on the observation type, and in general includes information about the instrumentation error (EI), the representativeness error (ER) due to unresolved scales, and an error associated with the relative "age" of each observation (EA). The latter is necessary in order to consider the relative importance between observations made a few days before the analysis time and observations at or closer to the analysis time.…”

Section: The Oceanographic Modeling and Observation Network (Remo)mentioning

confidence: 99%

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Coastal Ocean Observing and Modeling Systems in Brazil: Initiatives and Future Perspectives

et al. 2021

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Coastal ocean observing and modeling systems (coastal observatories), connected with regional and global ocean systems, improve the quality of information and forecasts for effective management of safe and sustainable maritime activities. The public availability of systematic and long-term information of the ocean is an engine for the Blue Economy, boosting economic growth, employment, and innovation. An overview of some Brazilian initiatives is presented in this paper, involving universities, private companies, federal and state agencies, covering institutions from south to north of Brazil. Although these initiatives focus mainly on ocean physics, integrated efforts can extend the scope to include biogeochemistry and marine biodiversity, helping to address interdisciplinary problems. Existing initiatives can be connected, and new ones fostered, to fill in the gaps of temporal and spatial coverage of ocean monitoring in the vast oceanic area under Brazilian jurisdiction (nationally referred to as the Blue Amazon, in reference to the similar richness of the Amazon Forest). The alignment among national and regional initiatives, as well as with international programs, can be promoted if coordinated by a national-level organization, maximizing the return of public investment and socioeconomic benefits. In light of international examples, possible future institutional arrangements are discussed, leveraging from existing national public policies and international cooperation that Brazil is taking part. The United Nations Decade of Ocean Science for Sustainable Development is a timely opportunity to encourage an institutional arrangement to support and articulate an integrated network of coastal observatories in Brazil.

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Section: The Oceanographic Modeling and Observation Network (Remo)mentioning

confidence: 99%

Section: The Oceanographic Modeling and Observation Network (Remo)mentioning

confidence: 99%

Section: The Oceanographic Modeling and Observation Network (Remo)mentioning

confidence: 99%

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Coastal Ocean Observing and Modeling Systems in Brazil: Initiatives and Future Perspectives

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“…The lower‐resolution configuration covers the region from 78°S to 55°N, and from 100°W to 20°E. This larger‐scale grid was employed in a recent Atlantic study made by Tanajura et al (2020) and Mignac et al (2015). The HYCOM 1/12 presents 601 by 733 grid points in the zonal and meridional directions, respectively.…”

Section: Model Configuration and Data Assimilationmentioning

confidence: 99%

A New Approach for Estimating Salinity in the Southwest Atlantic and Its Application in a Data Assimilation Evaluation Experiment

et al. 2020

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A new method for generating synthetic salinity (SS) profiles in the Southwest Atlantic was developed and applied in data assimilation experiments. This method was based on the smallest integrated values of root mean square deviation (RMSD)—with respect to observations—to infer salinity through climatological data and by regressive methods on temperature (T) using a five‐order polynomial function (P5). In the 14 delimited subregions, the averaged RMSD of P5 was 45% smaller than interpolated climatological data. However, climatological salinity presented better results in the first top layers while P5 presented smaller errors in higher depths. Therefore, by joining the best that P5 and climatology may offer, a new hybrid approach was used to generate SS based on T from XBT profiles. The SS would allow more T profiles to be employed in the Oceanographic Modeling and Observation Network (REMO) data assimilation system, called RODAS, into the Hybrid Coordinate Ocean Model (HYCOM). The use of SS estimates has potential to improve model outputs, in which the presence of the T‐S pair is quite necessary. Three integrations were performed: one run without assimilation (FREE), one assimilating sea surface temperature, Argo profilers, and sea level anomaly (RODAS) and one similar to RODAS, but with added XBTs with SS (RODAS_XBT). The inclusion of XBT data in the HYCOM + RODAS system improved the position and magnitude of the Brazil Current (BC). It was shown that SS is feasible for producing ocean reanalysis and initial conditions for ocean forecast systems requiring very low computational cost.

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“…Assimilation of sea level was found to be the most important factor for representing the CSTE, though assimilation of temperature and salinity were also important for the thermohaline field and eddy motions. Tanajura et al (2020) presented and evaluated an operational data assimilation and forecast system for Brazil using HYCOM. The data assimilation is based on a multivariate ensemble optimal interpolation scheme, using temperature and salinity profiles from Argo, as well as satellite SST data and along-track sea level altimeter data.…”

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The 10th International Workshop on Modeling the Ocean (IWMO 2018) in Santos, Brazil, June 25–28, 2018

et al. 2020

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Observing system experiments over the Atlantic Ocean with the REMO ocean data assimilation system (RODAS) into HYCOM

Cited by 14 publications

References 56 publications

Coastal Ocean Observing and Modeling Systems in Brazil: Initiatives and Future Perspectives

Coastal Ocean Observing and Modeling Systems in Brazil: Initiatives and Future Perspectives

A New Approach for Estimating Salinity in the Southwest Atlantic and Its Application in a Data Assimilation Evaluation Experiment

The 10th International Workshop on Modeling the Ocean (IWMO 2018) in Santos, Brazil, June 25–28, 2018

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