Ensemble Optimal Interpolation: multivariate properties in the Gulf of Mexico

Abstract High-resolution models can reproduce mesoscale dynamics and the variability in the Gulf of Mexico (GOM), but cannot provide accurate locations of currents without data assimilation (DA). We use the computationally cheap Ensemble Optimal Interpolation (EnOI) in conjunction with the Hybrid Coordinate Ocean Model (HYCOM) model for assimilating altimetry data. The covariance matrix extracted from a historical ensemble, is three-dimensional and multivariate. This study shows that the multivariate correlations with sea level anomaly are coherent with the known dynamics of the area at two locations: the central part of the GOM and the upper slope of the northern shelf. The correlations in the first location are suitable for an eddy forecasting system, but the correlations in the second location show some limitations due to seasonal variability. The multivariate relationships between variables are reasonably linear, as assumed by the EnOI. Our DA set-up produces little noise that is dampened within 2 d, when the model is pulled strongly towards observations. Part of it is caused by density perturbations in the isopycnal layers, or artificial caballing. The DA system is demonstrated for a realistic case of Loop Current eddy shedding, namely Eddy Yankee.

[1]  D. Rivas,et al.  The Ventilation of the Deep Gulf of Mexico , 2005 .

[2]  Hyun-chul Lee,et al.  Effects of winds and Caribbean eddies on the frequency of Loop Current eddy shedding: A numerical model study , 2003 .

[3]  Tal Ezer,et al.  An exercise in forecasting loop current and eddy frontal positions in the Gulf of Mexico , 2005 .

[4]  Geir Evensen,et al.  Efficiency of high order numerical schemes for momentum advection , 2007 .

[5]  L. Dümenil,et al.  Discharge data from 50 selected rivers for GCM validation , 1993 .

[6]  Initialization of the shallow water equations with open boundaries by the bounded derivative method , 2010 .

[7]  Pierre Brasseur,et al.  The SEEK filter method for data assimilation in oceanography: a synthesis , 2006 .

[8]  J. O'Brien,et al.  Export pathways for river discharged fresh water in the northern Gulf of Mexico , 2003 .

[9]  L. Oey,et al.  A Synopsis of the Circulation in the Gulf of Mexico and on its Continental Margins , 2013 .

[10]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[11]  Kevin E. Trenberth,et al.  Estimates of Freshwater Discharge from Continents: Latitudinal and Seasonal Variations , 2002 .

[12]  Fred M. Vukovich,et al.  Climatology of Ocean Features in the Gulf of Mexico , 2005 .

[13]  L. Kantha,et al.  A Regional Data-Assimilative Model for Operational Use in the Gulf of Mexico , 2013 .

[14]  Peter R. Oke,et al.  Impacts of localisation in the EnKF and EnOI: experiments with a small model , 2007 .

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

[16]  Keith Haines,et al.  Altimetric assimilation with water property conservation , 1996 .

[17]  Brady A. Elliott,et al.  Anticyclonic Rings in the Gulf of Mexico , 1982 .

[18]  J. Cummings,et al.  Operational multivariate ocean data assimilation , 2005 .

[19]  C. Russell,et al.  Characterizing the long-period ULF response to magnetic storms , 2003 .

[20]  Geir Evensen,et al.  Coordinate Transformation on a Sphere Using Conformal Mapping , 1999 .

[21]  F. Hernandez,et al.  A mean dynamic topography computed over the world ocean from altimetry, in situ measurements, and a geoid model , 2004 .

[22]  J. Sheinbaum,et al.  Flow structure and transport in the Yucatan Channel , 2002 .

[23]  Charlie N. Barron,et al.  The Modular Ocean Data Assimilation System (MODAS) , 2002 .

[24]  F. Vukovich.  Loop Current boundary variations , 1988 .

[25]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[26]  J. Sheinbaum,et al.  Analysis of flow variability in the Yucatan Channel , 2003 .

[27]  William Carlisle Thacker,et al.  Data assimilation with inequality constraints , 2007 .

[28]  G. Browning,et al.  Initialization of the shallow water equations with open boundaries by the bounded derivative method , 1982 .

[29]  Robert R. Leben,et al.  Frequency of Ring Separations from the Loop Current in the Gulf of Mexico: A Revised Estimate , 2000 .

[30]  Peter R. Oke,et al.  Assimilation of surface velocity data into a primitive equation coastal ocean model , 2002 .

[31]  P.Y. Le Traon,et al.  SSALTO/DUACS and operational altimetry , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[32]  A. Bennett,et al.  Inverse Modeling of the Ocean and Atmosphere , 2002 .

[33]  G. Evensen,et al.  Sequential Data Assimilation Techniques in Oceanography , 2003 .

[34]  Peter R. Oke,et al.  Ensemble data assimilation for an eddy‐resolving ocean model of the Australian region , 2005 .

[35]  Rainer Bleck,et al.  An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates , 2002 .

[36]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[37]  Gilles Reverdin,et al.  Global high-resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and -2 , 2000 .

[38]  Peter R. Oke,et al.  Objective Array Design: Application to the Tropical Indian Ocean , 2008 .

[39]  Eric P. Chassignet,et al.  Loop Current Ring Shedding: The Formation of Cyclones and the Effect of Topography , 2005 .

[40]  W. Teague,et al.  A Comparison Between the Generalized Digital Environmental Model and Levitus climatologies , 1990 .

[41]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .