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The REMO Ocean Data Assimilation System into HYCOM (RODAS_H): General Description and Preliminary Results |
Clemente Augusto Souza TANAJURA1,2,3, Alex Novaes SANTANA2, Davi MIGNAC2,4, Leonardo Nascimento LIMA2, Konstantin BELYAEV2,5, XIE Ji-Ping6 |
1Physics Institute, Federal University of Bahia (UFBA), Salvador, 40170-280, Brazil
2Oceanographic Modeling and Observation Network (REMO), Center for Research in Geophysics and Geology, Federal University of Bahia (UFBA), Salvador, 40170-280, Brazil
3Ocean Sciences Department, University of California, Santa Cruz (UCSC), USA
4Graduate Program in Geophysics, Federal University of Bahia (UFBA), Salvador, Brazil
5Shirshov Institute of Oceanology, Russian Academy of Sciences, (SHIRSHOV/RAS) Moscow, Russia
6Institute of Atmospheric Physics(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China |
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Abstract The first version of the Brazilian Oceanographic Modeling and Observation Network (REMO) ocean data assimilation system into the Hybrid Coordinate Ocean Model (HYCOM) (RODAS_H) has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation (EnOI) scheme and considers the high frequency variability of the model error co-variance matrix. The EnOI can assimilate sea surface temperature (SST), satellite along-track and gridded sea level anomalies (SLA), and vertical profiles of temperature (T) and salinity (S) from Argo. The first observing system experiment was carried out over the Atlantic Ocean (78°S–50°N, 100°W–20°E) with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integrations were performed, including the control run without assimilation. In the other four, different observations were assimilated: SST only (A_SST); Argo T-S profiles only (A_Argo); along-track SLA only (A_SLA); and all data employed in the previous runs (A_All). The A_SST, A_Argo, and A_SLA runs were very effective in improving the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to observations. Overall, the A_All run produced a good analysis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%.
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Received: 11 February 2014
Revised: 11 March 2014
Accepted: 10 March 2014
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Corresponding Author:
Clemente Augusto Souza TANAJURA
E-mail: cast@ufba.br.
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