The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part II – Performance and application to the California Current System

The Regional Ocean Modeling System (ROMS) 4-dimensional variational (4D-Var) data assimilation systems have been systematically applied to the mesoscale circulation environment of the California Current to demonstrate the performance and practical utility of the various components of ROMS 4D-Var. In particular, we present a comparison of three approaches to 4D-Var, namely: the primal formulation of the incremental strong constraint approach; the dual formulation “physical-space statistical analysis system”; and the dual formulation indirect representer approach. In agreement with theoretical considerations all three approaches converge to the same ocean circulation estimate when using the same observations and prior information. However, the rate of convergence of the dual formulation was found to be inferior to that of the primal formulation. Other aspects of the 4D-Var performance that relate to the use of multiple outer-loops, preconditioning, and the weak constraint are also explored. A systematic evaluation of the impact of the various components of the 4D-Var control vector (i.e. the initial conditions, surface forcing and open boundary conditions) is also presented. It is shown that correcting for uncertainties in the model initial conditions exerts the largest influence on the ability of the model to fit the available observations. Various important diagnostics of 4D-Var are also examined, including estimates of the posterior error, the information content of the observation array, and innovation-based consistency checks on the prior error assumptions. Using these diagnostic tools, we find that more than 90% of the observations assimilated into the model provide redundant information. This is a symptom of the large percentage of satellite data that are used and to some extent the nature of the data processing employed. This is the second in a series of three papers describing the ROMS 4D-Var systems.

[1]  Hernan G. Arango,et al.  4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS) , 2008 .

[2]  Sophie Ricci,et al.  Incorporating State-Dependent Temperature–Salinity Constraints in the Background Error Covariance of Variational Ocean Data Assimilation , 2005 .

[3]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory , 2007 .

[4]  Andrew M. Moore,et al.  Application of 4D-Variational data assimilation to the California Current System , 2009 .

[5]  J. Willis,et al.  Interannual variability in upper ocean heat content, temperature, and thermosteric expansion on global scales , 2004 .

[6]  L. Berre,et al.  The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model , 2006 .

[7]  S. CohnData Assessing the Eeects of Data Selection with Dao's Physical-space Statistical Analysis System , 1994 .

[8]  Andrew C. Lorenc,et al.  Modelling of error covariances by 4D‐Var data assimilation , 2003 .

[9]  Yannick Trémolet,et al.  Computation of observation sensitivity and observation impact in incremental variational data assimilation , 2008 .

[10]  A. Bennett Inverse Methods in Physical Oceanography , 1992 .

[11]  S. Cohn,et al.  Assessing the Effects of Data Selection with the DAO Physical-Space Statistical Analysis System* , 1998 .

[12]  M. Huddleston,et al.  Quality control of ocean temperature and salinity profiles — Historical and real-time data , 2007 .

[13]  R. Errico Interpretations of an adjoint-derived observational impact measure , 2007 .

[14]  A. Bennett,et al.  TOPEX/POSEIDON tides estimated using a global inverse model , 1994 .

[15]  A. Moore,et al.  The Inverse Ocean Modeling System. Part II: Applications , 2008 .

[16]  James C. McWilliams,et al.  A method for computing horizontal pressure‐gradient force in an oceanic model with a nonaligned vertical coordinate , 2003 .

[17]  A. Bennett Inverse methods for assessing ship-of-opportunity networks and estimating circulation and winds from tropical expendable bathythermograph data , 1990 .

[18]  P. L. Houtekamer,et al.  Ensemble Kalman filtering , 2005 .

[19]  John C. Warner,et al.  Ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the Regional Ocean Modeling System , 2008, J. Comput. Phys..

[20]  Robert N. Miller,et al.  Representer-based analyses in the coastal upwelling system , 2009 .

[21]  G. Golub,et al.  Some large-scale matrix computation problems , 1996 .

[22]  F. Gesztesy,et al.  On a theorem of Picard , 1998 .

[23]  Stephen E. Cohn,et al.  Treatment of Observation Error due to Unresolved Scales in Atmospheric Data Assimilation , 2006 .

[24]  Hernan G. Arango,et al.  Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System (ROMS): Development and application for a baroclinic coastal upwelling system , 2007 .

[25]  Alexander F. Shchepetkin,et al.  Open boundary conditions for long-term integration of regional oceanic models , 2001 .

[26]  Erik Andersson,et al.  Influence‐matrix diagnostic of a data assimilation system , 2004 .

[27]  D. Daescu On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation , 2008 .

[28]  Alexander F. Shchepetkin,et al.  The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model , 2005 .

[29]  Julia Levin,et al.  Towards an integrated observation and modeling system in the New York Bight using variational methods. Part II : repressenter-based observing strategy evaluation , 2010 .

[30]  R. Gelaro,et al.  Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System , 2008 .

[31]  D. Menemenlis Inverse Modeling of the Ocean and Atmosphere , 2002 .

[32]  Philippe Courtier,et al.  Dual formulation of four‐dimensional variational assimilation , 1997 .

[33]  S. Ricci,et al.  A multivariate balance operator for variational ocean data assimilation , 2005 .

[34]  Y. Trémolet Model‐error estimation in 4D‐Var , 2007 .

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

[36]  Jean-Michel Brankart,et al.  Efficient Parameterization of the Observation Error Covariance Matrix for Square Root or Ensemble Kalman Filters: Application to Ocean Altimetry , 2009 .

[37]  Ecmwf Newsletter,et al.  EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS , 2004 .

[38]  Rolf H. Langland,et al.  Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system , 2004 .

[39]  Athanasios C. Antoulas,et al.  Approximation of Large-Scale Dynamical Systems , 2005, Advances in Design and Control.

[40]  A. Moore,et al.  Corrections to ocean surface forcing in the California Current System using 4D variational data assimilation , 2011 .

[41]  D. Stammer,et al.  Optimal Observations for Variational Data Assimilation , 2004 .

[42]  Serge Gratton,et al.  Limited‐memory preconditioners, with application to incremental four‐dimensional variational data assimilation , 2008 .

[43]  A. Bennett,et al.  Open Ocean Modeling as an Inverse Problem: Tidal Theory , 1982 .

[44]  D. Chelton,et al.  Geographical Variability of the First Baroclinic Rossby Radius of Deformation , 1998 .

[45]  David M. Checkley,et al.  Patterns and processes in the California Current System , 2009 .

[46]  D. Costa,et al.  The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part III - Observation impact and observation sensitivity in the California Current System , 2011 .

[47]  E. F. Bradley,et al.  Cool‐skin and warm‐layer effects on sea surface temperature , 1996 .

[48]  D. Chapman Numerical Treatment of Cross-Shelf Open Boundaries in a Barotropic Coastal Ocean Model , 1985 .

[49]  C. Edwards,et al.  A central California coastal ocean modeling study: 1. Forward model and the influence of realistic versus climatological forcing , 2009 .

[50]  Michael Ghil,et al.  Dynamic Meteorology: Data Assimilation Methods , 1981 .

[51]  A. Moore,et al.  A central California coastal ocean modeling study: 2. Adjoint sensitivities to local and remote forcing mechanisms , 2009 .

[52]  Andrew F. Bennett,et al.  An inverse ocean modeling system , 2001 .

[53]  A. Moore,et al.  Estimating the 4DVAR analysis error of GODAE products , 2009 .

[54]  W. Liu,et al.  Bulk Parameterization of Air-Sea Exchanges of Heat and Water Vapor Including the Molecular Constraints at the Interface , 1979 .

[55]  E. F. Bradley,et al.  Bulk parameterization of air‐sea fluxes for Tropical Ocean‐Global Atmosphere Coupled‐Ocean Atmosphere Response Experiment , 1996 .

[56]  Yi Chao,et al.  High-resolution real-time modeling of the marine atmospheric boundary layer in support of the AOSN-II field campaign , 2009 .

[57]  A. Bennett,et al.  Array design by inverse methods , 1985 .

[58]  F. L. Dimet,et al.  Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects , 1986 .

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

[60]  Hernan G. Arango,et al.  A comprehensive ocean prediction and analysis system based on the tangent linear and adjoint of a regional ocean model , 2004 .

[61]  William Carlisle Thacker,et al.  The role of the Hessian matrix in fitting models to measurements , 1989 .

[62]  C. Lanczos An iteration method for the solution of the eigenvalue problem of linear differential and integral operators , 1950 .

[63]  The estimation of the ocean Mean Dynamic Topography through the combination of altimetric data, in-situ measurements and GRACE geoid: From global to regional studies , 2005 .

[64]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. Ii: Numerical Results , 2007 .

[65]  Carl Wunsch,et al.  Global ocean circulation during 1992-1997, estimated from ocean observations and a general circulation model , 2002 .

[66]  Ronald Gelaro,et al.  Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models , 2009 .

[67]  J. Derber Variational Four-dimensional Analysis Using Quasi-Geostrophic Constraints , 1987 .

[68]  C. Wunsch The Ocean Circulation Inverse Problem , 1996 .

[69]  Andrew C. Lorenc,et al.  Analysis methods for numerical weather prediction , 1986 .

[70]  Olivier Talagrand,et al.  Assimilation of Observations, an Introduction (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .

[71]  Stephen E. Cohn,et al.  An Introduction to Estimation Theory (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .

[72]  Thomas M. Powell,et al.  Multi‐scale modeling of the North Pacific Ocean: Assessment and analysis of simulated basin‐scale variability (1996–2003) , 2005 .

[73]  J. Derber,et al.  A reformulation of the background error covariance in the ECMWF global data assimilation system , 1999 .

[74]  K. Ide,et al.  A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System , 2008 .

[75]  Philippe Courtier,et al.  Unified Notation for Data Assimilation : Operational, Sequential and Variational , 1997 .

[76]  P. Gauthier,et al.  Convergence properties of the primal and dual forms of variational data assimilation , 2010 .

[77]  O. Talagrand,et al.  Diagnosis and tuning of observational error in a quasi‐operational data assimilation setting , 2006 .

[78]  S. Bograd,et al.  Long-term variability in the Southern California Current System , 2003 .

[79]  Lance M. Leslie,et al.  Tropical Cyclone Prediction Using a Barotropic Model Initialized by a Generalized Inverse Method , 1993 .

[80]  M. Saunders,et al.  Solution of Sparse Indefinite Systems of Linear Equations , 1975 .

[81]  Paul Poli,et al.  Diagnosis of observation, background and analysis‐error statistics in observation space , 2005 .

[82]  R. Daley Atmospheric Data Analysis , 1991 .

[83]  P. M. Kosro,et al.  Estimates of sea surface height and near‐surface alongshore coastal currents from combinations of altimeters and tide gauges , 2008 .

[84]  Gene H. Golub,et al.  Matrix computations , 1983 .

[85]  Carl Wunsch,et al.  Practical global oceanic state estimation , 2007 .

[86]  Alexander F. Shchepetkin,et al.  Model evaluation experiments in the North Atlantic Basin : simulations in nonlinear terrain-following coordinates , 2000 .

[87]  Ichiro Fukumori,et al.  Nature of global large‐scale sea level variability in relation to atmospheric forcing: A modeling study , 1998 .

[88]  Y. Sasaki SOME BASIC FORMALISMS IN NUMERICAL VARIATIONAL ANALYSIS , 1970 .

[89]  M. Ghil,et al.  Data assimilation in meteorology and oceanography , 1991 .

[90]  J. Paduan,et al.  Variability of the near‐surface eddy kinetic energy in the California Current based on altimetric, drifter, and moored current data , 1998 .

[91]  C. Wunsch Discrete Inverse and State Estimation Problems: With Geophysical Fluid Applications , 2006 .

[92]  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 .

[93]  Keith Haines,et al.  Salinity Assimilation Using S(T): Covariance Relationships , 2006 .

[94]  A. Cavanie Equations des ressauts internes dans un système à deux couches et leurs solutions , 1973 .

[95]  Y. Trémolet Accounting for an imperfect model in 4D‐Var , 2006 .

[96]  Hernan G. Arango,et al.  Estimates of Analysis and Forecast Error Variances Derived from the Adjoint of 4D-Var , 2012 .

[97]  W. Thacker,et al.  Fitting dynamics to data , 1988 .

[98]  M. Balmaseda,et al.  Ensemble estimation of background‐error variances in a three‐dimensional variational data assimilation system for the global ocean , 2009 .

[99]  M. Benzi Preconditioning techniques for large linear systems: a survey , 2002 .

[100]  Hernan G. Arango,et al.  The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation , 2011 .

[101]  R. Flather,et al.  Results from a storm surge prediction model of the north-west European continental shelf for April, November and December, 1973 , 1976 .

[102]  R. Errico,et al.  Examination of various-order adjoint-based approximations of observation impact , 2007 .

[103]  David L. T. Anderson,et al.  The ECMWF Ocean Analysis System: ORA-S3 , 2008 .

[104]  L. Berre,et al.  A Posteriori Diagnostics in an Ensemble of Perturbed Analyses , 2009 .

[105]  A. Moore,et al.  Ocean State and Surface Forcing Correction using the ROMS- IS4DVAR Data Assimilation System , 2009 .

[106]  J. M. Lewis,et al.  The use of adjoint equations to solve a variational adjustment problem with advective constraints , 1985 .

[107]  John Derber,et al.  A Global Oceanic Data Assimilation System , 1989 .