A Parallel Three-dimensional Variational Data Assimilation Scheme

2011 ◽  
Author(s):  
L. D’Amore ◽  
R. Arcucci ◽  
L. Marcellino ◽  
A. Murli ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

The Met. Office global three-dimensional variational data assimilation scheme

2000 ◽  
Vol 126 (570) ◽  
pp. 2991-3012 ◽  
Author(s):  
A. C. Lorenc ◽  
S. P. Ballard ◽  
R. S. Bell ◽  
N. B. Ingleby ◽  
P. L. F. Andrews ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

scholarly journals Displacing Potential Vorticity Structures by the Assimilation of Pseudo-Observations

2011 ◽  
Vol 139 (2) ◽  
pp. 549-565 ◽  
Author(s):  
Yann Michel
Keyword(s):  
Data Assimilation ◽  
Potential Vorticity ◽  
Variational Data Assimilation ◽  
Tuning Parameter ◽  
Data Assimilation Scheme ◽  
The Difference ◽  
Upper Level ◽  
Meteorological Features ◽  
Assimilation Scheme ◽  
Dry Intrusions

Abstract Classic formulations of variational data assimilation in amplitude space are not able to directly handle observations that measure the geographical positions of meteorological features like fronts and vortices. These observations can be derived from satellite images, as is already the case for tropical cyclones. Although some advanced data assimilation algorithms have been specifically designed to tackle the problem, a widespread way of dealing with this information is to use so-called bogussing pseudo-observations: user-specified artificial observations are inserted in a traditional data assimilation scheme. At the midlatitudes, there is a relationship between dry intrusions in water vapor images and upper-level potential vorticity structures. Some prior work has also shown that it was possible to automatically identify dry intrusions with tracking algorithms. The difference of positions between model and image dry intrusions could therefore be used as observations of the misplacement of potential vorticity structures. One strategy to achieve the displacement of potential vorticity anomalies is to sample them, and assimilate the values at displaced locations. The uncertainty of these pseudo-observations is left as a tuning parameter to try to make the displacement both effective and robust. A simple one-dimensional assimilation model is used to study the displacement of curves defined by Gaussian humps. The concept is then illustrated in realistic examples from real synoptic systems, where pseudo-observations of potential vorticity are incorporated in a global variational data assimilation scheme. Overall and despite reasonable optimization, the results contain artifacts. This suggests that the use of pseudo-observations to displace identifiable structures is not an effective strategy.

A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System

2008 ◽  
Vol 25 (11) ◽  
pp. 2074-2090 ◽  
Author(s):  
Zhijin Li ◽  
Yi Chao ◽  
James C. McWilliams ◽  
Kayo Ide
Keyword(s):  
Data Assimilation ◽  
Bottom Topography ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Ocean Modeling ◽  
Small Scale ◽  
Regional Ocean Modeling System ◽  
Dynamic Constraints ◽  
The Cost ◽  
Assimilation Scheme

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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