Estimating and Predicting Ocean Currents in the U.S. coastal oceans John D. Farrara*, Yi Chao, Zhijin Li, Xiaochun Wang*, Hongchun Zhang*, Peggy Li, Quoc Vu NASA Jet Propulsion Laboratory *Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles Ocean Currents and End User Feedback Workshop, May 5-6, 2011, Atlanta 1
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Estimating and Predicting Ocean Currents in the U.S. coastal oceans John D. Farrara*, Yi Chao, Zhijin Li, Xiaochun Wang*, Hongchun Zhang*, Peggy Li, Quoc.
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Estimating and Predicting Ocean Currents in the U.S. coastal oceans
John D. Farrara*, Yi Chao, Zhijin Li, Xiaochun Wang*,
Hongchun Zhang*, Peggy Li, Quoc Vu
NASA Jet Propulsion Laboratory
*Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles
Ocean Currents and End User Feedback Workshop, May 5-6, 2011, Atlanta 1
DataAssimilation
Model
Products
UsersObservations
Feedback
Forecasting
Ocean Hindcast/Nowcast/Forecast
Ocean observing is rapidly expanding, ocean models are maturing; To what extent can the regional oceans be predicted from synoptic weather (days) to climate (seasons, interannual, decadal) time scales?
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A Portable, Data-Assimilative Coastal Ocean Nowcast/Forecast/Hindcast System
- Based on the ROMS regional ocean model A typical configuration has a horizontal resolution of a few km or less,
and extends several hundred kilometers offshore
Nowcasts typically generated every 6 hours, with a daily 48 or 72 hour forecast
- A multi-scale 3DVAR data assimilation scheme
Large and small spatial scales separately assimilated
Scale-dependent error covariances
Scale-dependent dynamic balance constraints
Typical data assimilated includes satellite SSTs, HF radar surface currents
and glider/mooring temperature and salinity vertical profiles
- Tides: Oregon State University global tidal forcing
- Interactive trajectory tool, real-time execution is fully automated3
U. S. Coastal Regions where the system has been applied
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15-km 5-km 1.5-km
Multi-level Nested Regional Ocean Modeling System (ROMS)
A multi-scale (or “nested”) ROMS modeling approach has been developed in order to simulate the 3D ocean at the spatial scale (e.g., 1-km) measured by satellites and coastal HF
radars in a way that is computationally efficient enough to allow real-time operations.
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x is obtained by minimizing the Cost Function
J = (x)T B-1 (x) + (h x-y)T R-1 (h x-y)
xa = xf + x
3DVAR Data Assimilation(3-dimensional variational)
References: Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008a: A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System. Journal of Atmospheric and Oceanic Technology, 25, 2074-2090.Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008b: A three-dimensional variational data assimilation schemefor the Regional Ocean Modeling System: Implementation and basic experiments, J. Geophys. Res., 113, C05002, doi:10.1029/2006JC004042.
Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2011: A multi-scale three-dimensional variational data assimilation scheme and its application to coastal
oceans. Quart. J. Roy. Meteorol. Soc., submitted.
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3DVAR Data Assimilation With Geostrophic Constraint
xuv
xuvf Gdx
TSF
adx
a yc
Min[(Xuvo-Xuv
f)2]
uvG
TSGeostrophic Balance
HF Radar
Current Obs.
Geostrophic vs. Non-geostrophic
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3DVAR Data Assimilation With Hydrostatic Constraint