Abstract
A fully three dimensional, multivariate, variational ocean data assimilation system has been developed that produces simultaneous analyses of temperature, salinity, geopotential and vector velocity. The analysis is run in real-time and is being evaluated as the data assimilation component of the Hybrid Coordinate Ocean Model (HYCOM) forecast system at the U.S. Naval Oceanographic Office. Global prediction of the ocean weather requires that the ocean model is run at very high resolution. Currently, global HYCOM is executed at 1/12 degree resolution ( ∼ 7 km mid-latitude grid mesh), with plans to move to a 1/25 degree resolution grid in the near future ( ∼ 3 km mid-latitude grid mesh). These high resolution global grids present challenges for the analysis given the huge model state vector and the ever increasing number of satellite and in situ ocean observations available for the assimilation. In this paper the development and evaluation of the new oceanographic three-dimensional variational (3DVAR) data assimilation is described. Special emphasis is placed on documenting the capabilities built into the 3DVAR to make the system efficient for use in global HYCOM.
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Acknowledgements
The first author gratefully acknowledges the work of Roger Daley and Ed Barker in developing the NAVDAS solution algorithm which forms the basis of the NCODA 3DVAR. This work was funded in part by NRL base projects “Variational Data Assimilation for Ocean Prediction” and “Observation Impact using a Variational Adjoint System”. Funding was also received from the National Ocean Partnership Program (NOPP) through the project “US GODAE: Global-Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM)” and the Office of Naval Research (ONR) under program element number 61153 N. The Department of Defense High Performance Computing Modernization Program provided grants of computer time at Major Shared Resource Centers operated by the Naval Oceanographic Office, Stennis Space Center, MS, USA. This is NRL contribution NRL/BC/7320-12-1125 and has been approved for public release. Distribution is unlimited.
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Cummings, J.A., Smedstad, O.M. (2013). Variational Data Assimilation for the Global Ocean. In: Park, S., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35088-7_13
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Keywords
- Background Error
- Mean Dynamic Topography
- Background Error Covariance
- Correlation Length Scale
- Ensemble Transform
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
