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The REMO Ocean Data Assimilation system into HYCOM(RODAS_H):General Description and Preliminary Results

Atmospheric and Oceanic Science Letters 2014年第5期7卷 464-470页

作者： Clemente Augusto Souza TANAJURA Alex Novaes SANTANA Davi MIGNAC Leonardo Nascimento LIMA Konstantin BELYAEV XIE Ji-Ping Physics Institute Federal University of Bahia (UFBA) Salvador 40170-280 Brazil Oceanographic Modeling and Observation Network (REMO) Center for Research in Geophysics and Geology Federal University of Bahia (UFBA) Salvador 40170-280 Brazil Ocean Sciences Department University of California Santa Cruz (UCSC) USA Graduate Program in Geophysics Federal University of Bahia (UFBA) Salvador Brazil Shirshov Institute of Oceanology Russian Academy of Sciences (SHIRSHOV/RAS) Moscow Russia Institute of Atmospheric Physics(IAP) Chinese Academy of Sciences (CAS)

The first version of the Brazilian Oceano- graphic Modeling and Observation Network （REMO） ocean data assimilation system into the Hybrid Coordi- nate 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 fre- quency variability of the model error co-variance matrix. The EnOl 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 integra- tions 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 （AArgo）; 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 improv- ing 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 ob- servations. Overall, the A_All run produced a good analy- sis 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%.

关键词： ocean data assimilation ensemble optimalinterpolation observing system experiment HYCOM Atlantic Ocean

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