A cost-effective data assimilation system for improving ROMS simulations in the Southwest Atlantic
6 affiliations
- 1Oceanographic Modeling and Observation Network (REMO), Salvador, Bahia, Brazil.
- 2Graduate Program in Geophysics, Federal University of Bahia (UFBA), Salvador, Bahia, Brazil.
- 3Department of Earth and Environmental Physics, Physics Institute, Federal University of Bahia (UFBA), Salvador, Bahia, Brazil.
- 4Department of Oceanic Engineering Technology, Petrobras Research and Development Center (CENPES), Rio de Janeiro, Rio de Janeiro, Brazil.
- 5Federal University of Rio de Janeiro (UFRJ), Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Rio de Janeiro, Rio de Janeiro, Brazil.
- 6CMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, Italy.
Abstract
This study presents for the first time the integration of the low-cost REMO Ocean Data Assimilation System (RODAS) into the Regional Ocean Modeling System (ROMS) for assimilating Sea Surface Temperature (SST) observations. RODAS is an Ensemble Optimal Interpolation (EnOI)-based data assimilation system originally developed for use with the HYCOM model. Here, it is adapted to ROMS and applied to the Southwest Atlantic, with a focus on evaluating its impact on short-term ocean hindcasts. We conducted 4-years simulations with and without SST assimilation, referred to as RODAS_SST and Free, respectively. The RODAS_SST experiment showed a 70% reduction in SST root mean square deviation (RMSD) relative to the Free run, and a 55% reduction compared to 72-hour hindcasts generated from previous analysis cycles. When compared with the GLORYS reanalysis, SST RMSD was reduced by 64%. Ocean heat content estimates also improved, with reductions in mean absolute error of 15% during summer and 25% in winter. While assimilation improved surface and subsurface thermal structure in general, comparisons with Argo profiles revealed occasional degradations near the thermocline, highlighting the need for better multivariate covariance representation in the EnOI scheme. The successful implementation of RODAS in ROMS provides a cost-effective tool for enhancing model initial conditions and regional ocean simulations. Future developments will include the assimilation of Argo temperature and salinity profiles, sea level anomaly (SLA) data, and observations from the SWOT satellite. The code will be openly released, aiming to broaden access to ocean data assimilation by enabling robust performance even on limited computational resources.
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BibTeX
@article{dorfschafer2026costeffective,
title = {{A cost-effective data assimilation system for improving ROMS simulations in the Southwest Atlantic}},
author = {Geoff S Dorfschäfer and Clemente A S Tanajura and Thiago P De Paula and Filipe B Costa and Janini Pereira},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0358351},
url = {https://doi.org/10.1371/journal.pone.0358351},
note = {CC BY},
}
RIS
TY - JOUR
TI - A cost-effective data assimilation system for improving ROMS simulations in the Southwest Atlantic
AU - Geoff S Dorfschäfer
AU - Clemente A S Tanajura
AU - Thiago P De Paula
AU - Filipe B Costa
AU - Janini Pereira
PY - 2026
DA - 2026/10/20
JO - PLOS One
DO - 10.1371/journal.pone.0358351
UR - https://doi.org/10.1371/journal.pone.0358351
AB - This study presents for the first time the integration of the low-cost REMO Ocean Data Assimilation System (RODAS) into the Regional Ocean Modeling System (ROMS) for assimilating Sea Surface Temperature (SST) observations. RODAS is an Ensemble Optimal Interpolation (EnOI)-based data assimilation system originally developed for use with the HYCOM model. Here, it is adapted to ROMS and applied to the Southwest Atlantic, with a focus on evaluating its impact on short-term ocean hindcasts. We conducted 4-years simulations with and without SST assimilation, referred to as RODAS_SST and Free, respectively. The RODAS_SST experiment showed a 70% reduction in SST root mean square deviation (RMSD) relative to the Free run, and a 55% reduction compared to 72-hour hindcasts generated from previous analysis cycles. When compared with the GLORYS reanalysis, SST RMSD was reduced by 64%. Ocean heat content estimates also improved, with reductions in mean absolute error of 15% during summer and 25% in winter. While assimilation improved surface and subsurface thermal structure in general, comparisons with Argo profiles revealed occasional degradations near the thermocline, highlighting the need for better multivariate covariance representation in the EnOI scheme. The successful implementation of RODAS in ROMS provides a cost-effective tool for enhancing model initial conditions and regional ocean simulations. Future developments will include the assimilation of Argo temperature and salinity profiles, sea level anomaly (SLA) data, and observations from the SWOT satellite. The code will be openly released, aiming to broaden access to ocean data assimilation by enabling robust performance even on limited computational resources.
SN - 1932-6203
ER -