scholarly journals Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6

2019 ◽  
Vol 12 (1) ◽  
pp. 195-213
Author(s):  
Dale Partridge ◽  
Tobias Friedrich ◽  
Brian S. Powell
Keyword(s):  
Initial Conditions ◽  
Hawaiian Island ◽  
The State ◽  
Ocean Modeling ◽  
Surface Velocity ◽  
Regional Ocean Modeling System ◽  
Forecast System ◽  
Strong Constraint ◽  
State Estimate ◽  
High Frequency Radar

Abstract. A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong-constraint 4-D variational data assimilation with the Regional Ocean Modeling System (ROMS v3.6). Observations were assimilated from a range of sources: satellite-derived sea surface temperature (SST), salinity (SSS), and height anomalies (SSHAs); depth profiles of temperature and salinity from Argo floats, autonomous Seagliders, and shipboard conductivity–temperature–depth (CTD); and surface velocity measurements from high-frequency radar (HFR). The performance of the state estimate is examined against a forecast showing an improved representation of the observations, especially the realization of HFR surface currents. EOFs of the increments made during the assimilation to the initial conditions and atmospheric forcing components are computed, revealing the variables that are influential in producing the state-estimate solution and the spatial structure the increments form.

scholarly journals Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6

2018 ◽  
Author(s):  
Dale Partridge ◽  
Brian S. Powell
Keyword(s):  
Initial Conditions ◽  
Hawaiian Island ◽  
The State ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System ◽  
Forecast System ◽  
Strong Constraint ◽  
State Estimate ◽  
High Frequency Radar ◽  
Free Running

Abstract. A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong constraint 4D-Variational data assimilation with the Regional Ocean Modeling System (ROMS). Observations were assimilated from a range of sources: satellite-derived sea surface temperature (SST), salinity (SSS), and height anomalies (SSHA); depth profiles of temperature and salinity from Argo floats, autonomous SeaGliders, shipboard conductivity-temperature-depth (CTDs); and surface HFR velocity measurements from high frequency radar (HFR). The performance of the state-estimate is examined against a free-running forecast showing an improved representation of the observations, especially the realization of HFR surface currents. EOFs of the increments made during the assimilation to the initial conditions and atmospheric forcing components are computed, revealing the variables that are influential in producing the state-estimate solution and the spatial structure the increments form.

Predictability of Mesoscale Variability in the East Australian Current Given Strong-Constraint Data Assimilation

2012 ◽  
Vol 42 (9) ◽  
pp. 1402-1420 ◽  
Author(s):  
Javier Zavala-Garay ◽  
J. L. Wilkin ◽  
H. G. Arango
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Empirical Relationship ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Initial Time ◽  
East Australian Current ◽  
Mesoscale Variability ◽  
Regional Ocean Modeling System ◽  
Strong Constraint

Abstract One of the many applications of data assimilation is the estimation of adequate initial conditions for model forecasts. In this work, the authors evaluate to what extent the incremental, strong-constraint, four-dimensional variational data assimilation (IS4DVAR) can improve prediction of mesoscale variability in the East Australian Current (EAC) using the Regional Ocean Modeling System (ROMS). The observations considered in the assimilation experiments are daily composites of satellite sea surface temperature (SST), 7-day average reanalysis of satellite altimeter sea level anomalies, and subsurface temperature profiles from high-resolution expendable bathythermograph (XBT). Considering all available observations for years 2001 and 2002, ROMS forecast initial conditions are generated every week by assimilating the available observations from the 7 days prior to the forecast initial time. It is shown that assimilation of surface information only [SST and sea surface height (SSH)] results in poor estimates of the true subsurface ocean state (as depicted by the XBTs) and therefore poor forecast skill of subsurface conditions. Including the XBTs in the assimilation experiments improves the ocean state estimation in the vicinity of the XBT transects. By introducing subsurface pseudo-observations (which are called synthetic CTD) based on an empirical relationship between satellite surface observations and subsurface variability, the authors find a significant improvement in ocean state estimates that leads to skillful forecasts for up to 2 weeks in the domain considered.

Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System (ROMS): Development and application for a baroclinic coastal upwelling system

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 ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Coastal Upwelling ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System ◽  
Strong Constraint ◽  
Upwelling System

scholarly journals Permanent Meanders in the California Current System

2008 ◽  
Vol 38 (8) ◽  
pp. 1690-1710 ◽  
Author(s):  
L. R. Centurioni ◽  
J. C. Ohlmann ◽  
P. P. Niiler
Keyword(s):  
Sea Level ◽  
Current System ◽  
California Current ◽  
Ocean Model ◽  
Ocean Modeling ◽  
Surface Velocity ◽  
Regional Ocean Modeling System ◽  
Near Surface ◽  
Geostrophic Velocity ◽  
California Current System

Abstract Surface Velocity Program (SVP) drifter data from 1987 through 2005; Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) sea level anomalies; and NCEP reanalysis winds are used to assemble a time-averaged map of the 15-m-deep geostrophic velocity field in the California Current System seaward of about 50 km from the coast. The wind data are used to compute the Ekman currents, which are then subtracted from the drifter velocity measurements. The resulting proxy for geostrophic velocity anomalies computed from drifters and from satellite sea level measurements are combined to form an unbiased mean geostrophic circulation map. The result shows a California Current System that flows southward with four permanent meanders that can extend seaward for more than 800 km. Bands of alternating eastward and westward zonal currents are connected to the meanders and extend several thousand kilometers into the Pacific Ocean. This observed time-mean circulation and its associated eddy energy are compared to those produced by various high-resolution OGCM solutions: Regional Ocean Modeling System (ROMS; 5 km), Parallel Ocean Program model (POP; 1/10°), Hybrid Coordinate Ocean Model (HYCOM; 1/12°), and Naval Research Laboratory (NRL) Layered Ocean Model (NLOM; 1/32°). Simulations in closest agreement with observations come from ROMS, which also produces four meanders, geostrophic time-mean currents, and geostrophic eddy energy consistent with the observed values. The time-mean ageostrophic velocity in ROMS is strongest within the cyclonic part of the meanders and is similar to the ageostrophic velocity produced by nonlinear interaction of Ekman currents with the near-surface vorticity field.

scholarly journals Rossby Wave Instability and Apparent Phase Speeds in Large Ocean Basins

2007 ◽  
Vol 37 (5) ◽  
pp. 1177-1191 ◽  
Author(s):  
P. E. Isachsen ◽  
J. H. LaCasce ◽  
J. Pedlosky
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Water System ◽  
Local Deformation ◽  
Equation Model ◽  
Ocean Modeling ◽  
High Latitudes ◽  
Regional Ocean Modeling System ◽  
Latitude Range ◽  
The Stability

Abstract The stability of baroclinic Rossby waves in large ocean basins is examined, and the quasigeostrophic (QG) results of LaCasce and Pedlosky are generalized. First, stability equations are derived for perturbations on large-scale waves, using the two-layer shallow-water system. These equations resemble the QG stability equations, except that they retain the variation of the internal deformation radius with latitude. The equations are solved numerically for different initial conditions through eigenmode calculations and time stepping. The fastest-growing eigenmodes are intensified at high latitudes, and the slower-growing modes are intensified at lower latitudes. All of the modes have meridional scales and growth times that are comparable to the deformation radius in the latitude range where the eigenmode is intensified. This is what one would expect if one had applied QG theory in latitude bands. The evolution of large-scale waves was then simulated using the Regional Ocean Modeling System primitive equation model. The results are consistent with the theoretical predictions, with deformation-scale perturbations growing at rates inversely proportional to the local deformation radius. The waves succumb to the perturbations at the mid- to high latitudes, but are able to cross the basin at low latitudes before doing so. Also, the barotropic waves produced by the instability propagate faster than the baroclinic long-wave speed, which may explain the discrepancy in speeds noted by Chelton and Schlax.

scholarly journals An Advanced Data Assimilation System for the Chesapeake Bay: Performance Evaluation

2012 ◽  
Vol 29 (10) ◽  
pp. 1542-1557 ◽  
Author(s):  
Matthew J. Hoffman ◽  
Takemasa Miyoshi ◽  
Thomas W. N. Haine ◽  
Kayo Ide ◽  
Christopher W. Brown ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Chesapeake Bay ◽  
Nonlinear Dynamical Systems ◽  
Initial Conditions ◽  
Real Data ◽  
Ocean Modeling ◽  
Forecast Errors ◽  
Regional Ocean Modeling System ◽  
Data Assimilation System ◽  
Assimilation System

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.

Evaluation of the Ocean Circulation for the Solomon Sea Using the Regional Ocean Modeling System (ROMS)

2019 ◽  
Author(s):  
A. Rute Bento ◽  
Henrique Coelho ◽  
Chunxue Yang
Keyword(s):  
Ocean Circulation ◽  
Initial Conditions ◽  
Ocean Model ◽  
Heat Fluxes ◽  
Ocean Modeling ◽  
Regional Ocean Modeling System ◽  
Circulation Patterns ◽  
Terrain Following ◽  
Adcp Measurements ◽  
Main Circulation

Abstract The Regional Ocean Modeling System (ROMS) is a free-surface, terrain-following, primitive equations ocean model and it was implemented to perform a high-resolution 10-year hindcast study of Solomon’s Sea circulation patterns. The model was executed with a resolution of 1/36°, initial conditions from HYCOM+NCODA Global 1/12° and was forced by CFSR/CFSV2 momentum, mass and heat fluxes. The model was validated by comparing the simulated temperatures, salinities and flow patterns with satellite data, Argo floats and Ship ADCP measurements. In general, the model captured the main circulation patterns and performed well for the Solomon Sea. The modelled Temperature and Salinity profiles were comparable with the observations, with some error variability in the thermocline layer, which agreed with previous studies.

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