Near Real-time, 4-Dimensional Variational Data Assimilative Modeling of the California Current System (beta version)
Our squential assimilation procedure currently runs over 1 week cycles. Each Monday, a data assimilation run is performed for the previous week (Monday-Sunday), producing a 7-day ocean state-estimate in which the model initial conditions have been modified. The following data sets are assimilated: AVISO Sea Level Anomalies
, OSTIA Sea Surface Temperature
, subtidally averaged tide-guage data
, and subsurface hydrography from a glider run along CalCOFI
line 67 by the Monterey Bay Aquarium Research Institute (MBARI
). In the days following one assimilation cycle and before the next cycle has run, a non-data assimilative run of the nonlinear model is temporarily used for an estimate. These fields are nearly identical to the prior (background) solution of the next assimilation cycle.
present snapshots of surface ocean properties as produced by a numerical model, the Regional Ocean Modeling System
). The properties shown are (1) sea surface height (SSH), which varies about 1/2 meter within this domain; (2) sea surface temperature (SST), which usually shows a strong gradient between the warm subtropical waters to the southwest and cold subpolar waters to the north or cold upwelled waters along the coast; and (3) sea surface salinity (SSS), which shows a typical CCS value of about 33 psu (practical salinity units which is close to g/kg), with modest variation north to south owing to a relative increase in precipitation to the north and evaporation to the south. Superposed on SST and SSS are surface velocity vectors which show the direction and relative intensity of the instantaneous circulation. Although only surface features are presented above, the model resolves the full 3-dimensional structure of the ocean extending from the surface to the ocean bottom as deep as 5000 m beneath the surface.
The California Current System (CCS)
refers to the multiple oceanographic features of the northeast Pacific Ocean circulation off the U.S. west coast. It includes the broad equatorward surface motion that represents the easternmost portion of the North Pacific subtropical gyre spanning the breadth of the ocean basin, a narrow subsurface poleward flow (the California Undercurrent) often found between about 100 and 300 m depth along the continental slope, and occasionally a narrow surface nearshore countercurrent, seasonally varying in intensity and often strongest in fall. Superposed on these slowly varying motions are intense mesoscale eddies of tens to 100 km in scale that vary position on weekly to monthly time-scales, as well as smaller submesoscale and smaller motions that fluctuate on still shorter time-scales.
The ocean circulation is modeled using the Regional Ocean Modeling System
Our domain extends from midway down the Baja Peninsula to Vancouver Island at 1/10 degree (roughly 10 km) resolution, with 42 terrain-following levels resolving vertical structure in ocean properties. The model is forced at the surface by atmospheric fields produced by the Coupled Ocean Atmosphere Mesoscale Prediction System
) which is run in near-real-time by the Naval Research Laboratory
. For historical runs, we apply oceanographic fields at the (open) lateral boundaries as estimated by larger, basin-scale data assimilative models, such as the Simple Ocean Data Assimilation
) or Estimating the Circulation and Climate of the Ocean
) projects. For the near-real-time system, we use climatological boundary conditions from the 2005 version of the World Ocean Atlas
. The model does not include freshwater forcing by rivers, and it neglects tidal motion. Ocean model fields are stored as daily snapshots at midnight GMT.
Despite tremendous advances in ocean circulation models
over the last several decades, many sources of error exist and are in fact unavoidable when trying to accurately represent or predict the complex evolution of the true ocean state (i.e., the full physical structure of temperature, salinity and velocity fields). For example, the initial ocean state from which a simulation evolves is never known with 100% accuracy. In addition, ocean models themselves involve approximations of the true fluid dynamics for many reasons, such as limited resolution or imperfect representation of unresolved processes like small-scale turbulence. Finally, forcing fields (such as wind stress or heat flux) and conditions applied at the lateral (open) boundaries of the regional ocean model are themselves model solutions also with their own uncertainties.
The solution shown on this page applies the Incremental, Strong-Constraint 4-D Variational Assimilation (IS4DVAR) method. This approach finds changes to the initial ocean state during an assimilation cycle that minimize a cost function representing the sum of squared model-data differences and squared deviations of a background model ocean state. This method can also expand the control-space to include changes in the surface forcing and lateral boundary conditions, though such changes are not included in the shown solution. This approach is referred to as incremental as it determines increments to the background ocean state that are assumed to be small. The phrase strong-constraint means that errors in ocean dynamics are neglected (i.e., model dynamics are applied as a strong constraint).
Many people are responsible for various aspects of ROMS and the overall data assimilation code. Major contributors to UCSC data assimilation projects are listed below.
Andrew M. Moore
Christopher A. Edwards
We are also indebted to Brian Powell (UH) and John Wilkin (Rutgers) who have written and kindly shared outstanding computer scripts, and to James Doyle (NRL) for the COAMPS atmospheric fields.
More information on the CCS model, data assimilation system and related studies in the following:
- Veneziani, M., C. A. Edwards, J. D. Doyle, and D. Foley (2009), A central California coastal ocean modeling study: 1. Forward model and the influence of realistic versus climatological forcing, J. Geophys. Res., 114, C04015, doi:10.1029/2008JC004774.
- Veneziani ,M., C. A. Edwards and A. M. Moore (2009). A central California coastal ocean modeling study: 2. Adjoint sensitivities to local and remote forcing mechanisms. J Geophys Res, 114, doi:10.1029/2008JC004775.
- Broquet G., C. A. Edwards, A. M. Moore, B. S. Powell, M. Veneziani and J. D. Doyle, (2009), Application of 4D-Variational data assimilation to the California Current System, Dyn. Atmos. Oceans, doi:10.1016/j.dynatmoce.2009.03.001.
- Broquet G., A. M. Moore, H. G. Arango, C. A. Edwards, and B. S. Powell (2009), Ocean state and surface forcing correction using the ROMS-IS4DVAR data assimilation system, Mercator Ocean Quarterly Newsletter, Mercator Ocean Quarterly Newsletter, 34, pp. 5-13.
- Broquet, G., A. M. Moore, H. G. Arango, and C.A. Edwards (2010), Corrections to ocean surface forcing in the California Current System using 4D variational data assimilation, Ocean Mod. 36, doi:10.1016/j.ocemod.2010.10.005.
- Moore, A.M., Arango, H.G., Broquet, G., Powell, B.S., Zavala-Garay, J., Weaver, A.T., in press-a. The regional ocean modeling system (ROMS) 4-dimensional variational data assimilation systems. I: System overview and formulation. Prog. Oceanogr. doi:10.1016/j.pocean.2011.05.004.
- Moore, A.M., Arango, H.G., Broquet, G., Edwards, C.A., Veneziani, M., Powell, B.S., Foley, D., Doyle, J.D., Costa, D., Robinson, P., in press b. The regional ocean modeling system (ROMS) 4-dimensional variational data assimilation systems. II: Performance and application to the California current system. Prog. Oceanogr.doi:10.1016/j.pocean.2011.05.003.
- Moore, A.M., Arango, H.G., Broquet, G., Edwards, C.A., Veneziani, M., Powell, B.S., Foley, D., Doyle, J.D., Costa, D., Robinson, P., in press-b. The regional ocean modeling system (ROMS) 4-dimensional variational data assimilation systems: III Observation impact and observation sensitivity in the California Current system. Prog. Oceanogr. doi:10.1016/j.pocean.2011.05.005.
This web-page and the near-real-time ocean state estimation system is supported by the National Oceanographic and Atmospheric Administration (NOAA) through a grant from the Central and Northern California Ocean Observing System (CeNCOOS).
We gratefully acknowledge financial support for various elements of this data assimilative system by the National Oceanographic Partnership Program (NOPP) , the Office of Naval Research (ONR), and the National Science Foundation (NSF).