A pre-operational three Dimensional variational data assimilation system in the North/Baltic Sea

Abstract. This paper describes the implementation and evaluation of a pre-operational three dimensional variational (3DVAR) data assimilation system for the North/Baltic Sea. Univariate analysis for both temperature and salinity is applied in a 3DVAR scheme in which the horizontal component of the background error covariance is modeled by an isotropic recursive filter (IRF) and the vertical component is represented by dominant Empirical Orthogonal Functions (EOFs). Observations of temperature and salinity (T/S) profiles in the North/Baltic Sea are assimilated in the year of 2005. Effect of the 3DVAR scheme is assessed by a comparison between data assimilation run and control run. The statistical analysis indicates that the model simulation is significantly improved with the 3DVAR scheme. On average, the root mean square errors (RMSE) of temperature and salinity are reduced by 0.2 °C and 0.25 psu in the North/Baltic Sea. In addition, the bias of temperature and salinity is also decreased by 0.1 °C and 0.2 psu, respectively. Starting from an analyzed initial state, one month simulation without assimilation is carried out with the aim of examining the persistence of the initial impact. It is shown that the assimilated initial state can impact the model simulation for nearly two weeks. The influence on salinity is more pronounced than temperature.

[1]  R. James Purser,et al.  Recursive Filter Objective Analysis of Meteorological Fields: Applications to NESDIS Operational Processing , 1995 .

[2]  V. Canuto,et al.  Ocean Turbulence. Part II: Vertical Diffusivities of Momentum, Heat, Salt, Mass, and Passive Scalars , 2002 .

[3]  R. J. Purser,et al.  Three-Dimensional Recursive Filter Objective Analysis of Meteorological Fields , 1988 .

[4]  Andrew C. Lorenc,et al.  Development of an Operational Variational Assimilation Scheme (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .

[5]  Wei Huang,et al.  A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results , 2004 .

[6]  H. Arango,et al.  Towards an integrated observation and modeling system in the New York Bight using variational methods. Part I: 4DVAR data assimilation , 2010 .

[7]  Zhao Jun,et al.  GRAPES Global 3D-Var System—Basic Scheme Design and Single Observation Test , 2005 .

[8]  N. Pinardi,et al.  An oceanographic three-dimensional variational data assimilation scheme , 2008 .

[9]  N. Roberts,et al.  Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part II: Spatially Inhomogeneous and Anisotropic General Covariances , 2003 .

[10]  Jiang Zhu,et al.  Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model , 2009 .

[11]  Jérôme Vialard,et al.  Three- and Four-Dimensional Variational Assimilation with a General Circulation Model of the Tropical Pacific Ocean. Part I: Formulation, Internal Diagnostics, and Consistency Checks , 2003 .

[12]  N. B. Ingleby,et al.  The Met. Office global three‐dimensional variational data assimilation scheme , 2000 .

[13]  Jun She,et al.  Application of an Ensemble Optimal Interpolation in a North/Baltic Sea model: Assimilating temperature and salinity profiles , 2011 .

[14]  Robert N. Miller,et al.  Representer‐based variational data assimilation in a nonlinear model of nearshore circulation , 2007 .

[15]  P. Courtier,et al.  Correlation modelling on the sphere using a generalized diffusion equation , 2001 .

[16]  J. Smagorinsky,et al.  GENERAL CIRCULATION EXPERIMENTS WITH THE PRIMITIVE EQUATIONS , 1963 .

[17]  P. Courtier,et al.  A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .

[18]  Michel Rixen,et al.  Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea , 2007 .

[19]  Assessment of the three dimensional temperature and salinity observational networks in the Baltic Sea and North Sea , 2011 .

[20]  K. Hutter,et al.  Extending the k- ω turbulence model towards oceanic applications , 2003 .

[21]  Akio Arakawa,et al.  Computational Design of the Basic Dynamical Processes of the UCLA General Circulation Model , 1977 .

[22]  D. P. DEE,et al.  Bias and data assimilation , 2005 .

[23]  P. Bergthórsson,et al.  Numerical Weather Map Analysis , 1955 .

[24]  Pierre F. J. Lermusiaux,et al.  Lagoon of Venice ecosystem: Seasonal dynamics and environmental guidance with uncertainty analyses and error subspace data assimilation , 2009 .

[25]  J. Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..