Erratum to “4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)” [Ocean Modelling 23 (2008) 130–145]

2008 ◽  
Vol 25 (3-4) ◽  
pp. 172
2008 ◽  
Vol 23 (3-4) ◽  
pp. 130-145 ◽  
Author(s):  
B.S. Powell ◽  
H.G. Arango ◽  
A.M. Moore ◽  
E. Di Lorenzo ◽  
R.F. Milliff ◽  
...  

2007 ◽  
Vol 16 (3-4) ◽  
pp. 160-187 ◽  
Author(s):  
Emanuele Di Lorenzo ◽  
Andrew M. Moore ◽  
Hernan G. Arango ◽  
Bruce D. Cornuelle ◽  
Arthur J. Miller ◽  
...  

2021 ◽  
pp. 101889
Author(s):  
Thiago Pires de Paula ◽  
Jose Antonio Moreira Lima ◽  
Clemente Augusto Souza Tanajura ◽  
Marcelo Andrioni ◽  
Renato Parkinson Martins ◽  
...  

2020 ◽  
Vol 101 (8) ◽  
pp. E1340-E1356 ◽  
Author(s):  
P. A. Francis ◽  
A. K. Jithin ◽  
J. B. Effy ◽  
A. Chatterjee ◽  
K. Chakraborty ◽  
...  

Abstract A good understanding of the general circulation features of the oceans, particularly of the coastal waters, and ability to predict the key oceanographic parameters with good accuracy and sufficient lead time are necessary for the safe conduct of maritime activities such as fishing, shipping, and offshore industries. Considering these requirements and buoyed by the advancements in the field of ocean modeling, data assimilation, and ocean observation networks along with the availability of the high-performance computational facility in India, Indian National Centre for Ocean Information Services has set up a “High-Resolution Operational Ocean Forecast and Reanalysis System” (HOOFS) with an aim to provide accurate ocean analysis and forecasts for the public, researchers, and other types of users like navigators and the Indian Coast Guard. Major components of HOOFS are (i) a suite of numerical ocean models configured for the Indian Ocean and the coastal waters using the Regional Ocean Modeling System (ROMS) for forecasting physical and biogeochemical state of the ocean and (ii) the data assimilation based on local ensemble transform Kalman filter that assimilates in situ and satellite observations in ROMS. Apart from the routine forecasts of key oceanographic parameters, a few important applications such as (i) Potential Fishing Zone forecasting system and (ii) Search and Rescue Aid Tool are also developed as part of the HOOFS project. The architecture of HOOFS, an account of the quality of ocean analysis and forecasts produced by it and important applications developed based on HOOFS are briefly discussed in this article.


2012 ◽  
Vol 29 (10) ◽  
pp. 1542-1557 ◽  
Author(s):  
Matthew J. Hoffman ◽  
Takemasa Miyoshi ◽  
Thomas W. N. Haine ◽  
Kayo Ide ◽  
Christopher W. Brown ◽  
...  

Abstract An advanced data assimilation system, the local ensemble transform Kalman filter (LETKF), has been interfaced with a Regional Ocean Modeling System (ROMS) implementation on the Chesapeake Bay (ChesROMS) as a first step toward a reanalysis and improved forecast system for the Chesapeake Bay. The LETKF is among the most advanced data assimilation methods and is very effective for large, nonlinear dynamical systems with sparse data coverage. Errors in the Chesapeake Bay system are due more to errors in forcing than errors in initial conditions. To account for forcing errors, a forcing ensemble is used to drive the ensemble states for the year 2003. In the observing system simulation experiments (OSSEs) using the ChesROMS-LETKF system presented here, the filter converges quickly and greatly reduces the analysis and subsequent forecast errors in the temperature, salinity, and current fields in the presence of errors in wind forcing. Most of the improvement in temperature and currents comes from satellite sea surface temperature (SST), while in situ salinity profiles provide improvement to salinity. Corrections permeate through all vertical levels and some correction to stratification is seen in the analysis. In the upper Bay where the nature-run summer stratification is −0.2 salinity units per meter, stratification is improved from −0.01 per meter in the unassimilated model to −0.16 per meter in the assimilation. Improvements are seen in other parts of the Bay as well. The results from the OSSEs are promising for assimilating real data in the future.


2014 ◽  
Vol 11 (3) ◽  
pp. 1357-1390
Author(s):  
A. K. Sperrevik ◽  
K. H. Christensen ◽  
J. Röhrs

Abstract. Assimilation of High Frequency (HF) radar current observations and CTD hydrography is performed with the 4D-Var analysis scheme implemented in the Regional Ocean Modeling System (ROMS). We consider both an idealized case, with a baroclinic slope current in a periodic channel, and a realistic case for the coast of Vesterålen in Northern Norway. In the realistic case the results of the data assimilation are compared with independent data from acoustic profilers and surface drifters. Best results are obtained when background error correlation scales are small (10 km or less) and when the data assimilation window is short, i.e. about one day. Furthermore, we find that the impact of assimilating HF radar currents is generally larger than the impact of CTD hydrography, which implies that the amount of hydrographic data is insufficient to constrain the solution. Combining the HF radar currents with a few hydrographic profiles gives significantly better results, which demonstrates the importance of complementing surface observations with observations of the vertical structure of the ocean.


2011 ◽  
Vol 91 (1) ◽  
pp. 74-94 ◽  
Author(s):  
Andrew M. Moore ◽  
Hernan G. Arango ◽  
Gregoire Broquet ◽  
Chris Edwards ◽  
Milena Veneziani ◽  
...  

2008 ◽  
Vol 25 (11) ◽  
pp. 2074-2090 ◽  
Author(s):  
Zhijin Li ◽  
Yi Chao ◽  
James C. McWilliams ◽  
Kayo Ide

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.


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