Hindcasts and forecasts of Loop Current and eddies in the Gulf of Mexico using local ensemble transform Kalman filter and optimum-interpolation assimilation schemes

Abstract The Local Ensemble Transform Kalman Filter (LETKF) is applied to the parallelized version of the Princeton Ocean Model to estimate the states of Loop Current and eddies in the Gulf of Mexico from April/20 to July/21, 2010 when detailed in situ current measurements were available. Tests are conducted to explore the sensitivity of the LETKF estimates to different parameters, and to systematic additions of different observational datasets which include satellite sea surface height anomaly (SSHA), satellite sea surface temperature (SST), and moored ADCP’s. The results are compared against observations to assess model skills, and also against estimates based on a simpler optimum interpolation (OI) assimilation scheme. With appropriate values of parameters and observational errors, LETKF provides improved estimates of Loop Current and eddy. In particular, the Loop Current in the late spring to summer of 2010 underwent a shedding-reattachment-shedding process. It is shown that such a nonlinear behavior is more accurately captured by LETKF, but not by OI, due to the former’s time-evolving error covariance. Finally, the accuracies of 8-week forecasts initialized from the OI and LETKF analyses and forced by reanalysis winds are compared. This period is particularly challenging to forecast because, instead of a more easily simulated westward propagation at approximately the first-mode baroclinic Rossby wave speed, the newly-shed eddy propagated very slowly, stalled, and finally decayed in the eastern Gulf. Both OI and LETKF beat persistence, but the LETKF significantly improves the eddy’s position and strength throughout the 8-week forecast.

[1]  T. Waseda,et al.  Open and coastal seas interactions south of Japan represented by an ensemble Kalman filter , 2012, Ocean Dynamics.

[2]  G. Forristall,et al.  Velocity and hydrographic structure of two Gulf of Mexico warm‐core rings , 1990 .

[3]  P. Hamilton,et al.  Observations of high speed deep currents in the northern Gulf of Mexico , 2001 .

[4]  Christopher K. Wikle,et al.  Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.

[5]  Jiang Zhu,et al.  Assimilating remote sensing and in situ observations into a coastal model of northern South China Sea using ensemble Kalman filter , 2011 .

[6]  P. K. Kundu,et al.  An Analysis of Inertial Oscillations Observed Near Oregon Coast , 1976 .

[7]  Lie-Yauw Oey,et al.  Bred-ensemble ocean forecast of loop current and rings , 2007 .

[8]  Patrick Heimbach,et al.  A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico , 2013 .

[9]  Yu Lin Chang,et al.  Loop Current Growth and Eddy Shedding Using Models and Observations: Analyses of the July 2011 Eddy-Shedding Event* , 2013 .

[10]  George L. Mellor,et al.  A Gulf Stream model and an altimetry assimilation scheme , 1991 .

[11]  Yu-Lin Chang,et al.  Loop Current Growth and Eddy Shedding Using Models and Observations: Numerical Process Experiments and Satellite Altimetry Data , 2013 .

[12]  E. Kalnay,et al.  Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model , 2009 .

[13]  M. Katz.  Validation of models , 2006 .

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

[15]  Gilles Larnicol,et al.  New CNES‐CLS09 global mean dynamic topography computed from the combination of GRACE data, altimetry, and in situ measurements , 2011 .

[16]  L. Oey,et al.  Stalling of near‐inertial waves in a cyclone , 2008 .

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

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

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

[20]  N. Walker,et al.  The dynamics of the Mississippi River plume: Impact of topography, wind and offshore forcing on the fate of plume waters , 2011 .

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

[22]  Istvan Szunyogh,et al.  Assessing a local ensemble Kalman filter : perfect model experiments with the National Centers for Environmental Prediction global model , 2004 .

[23]  L. Oey.  Loop Current and Deep Eddies , 2008 .

[24]  Takemasa Miyoshi,et al.  Ensemble Kalman Filter and 4D-Var Intercomparison with the Japanese Operational Global Analysis and Prediction System , 2010 .

[25]  Hyun-chul Lee,et al.  Loop Current, Rings and Related Circulation in the Gulf of Mexico: A Review of Numerical Models and Future Challenges , 2013 .

[26]  R. Leben,et al.  Loop Current Observations During Spring and Summer of 2010: Description and Historical Perspective , 2011 .

[27]  Andrew F. Loughe,et al.  The Relationship between Ensemble Spread and Ensemble Mean Skill , 1998 .

[28]  Laurent Bertino,et al.  High-resolution ensemble forecasting for the Gulf of Mexico eddies and fronts , 2009 .

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

[30]  Xianyao Chen,et al.  Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean , 2012, Ocean Dynamics.

[31]  Yu-Lin Chang,et al.  ATOP -The advanced Taiwan ocean prediction system based on the mpiPOM. Part 1: Model descriptions, analyses and results , 2013 .

[32]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

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

[34]  Remy Baraille,et al.  The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system , 2007 .

[35]  Claire B. Paris,et al.  Larval transport pathways from Cuban snapper (Lutjanidae) spawning aggregations based on biophysical modeling , 2005 .

[36]  L. Oey,et al.  Why does the Loop Current tend to shed more eddies in summer and winter? , 2012 .

[37]  Thomas M. Hamill,et al.  Ensemble Data Assimilation with the NCEP Global Forecast System , 2008 .

[38]  X.-H. Lin,et al.  Altimetry and drifter data assimilations of loop current and eddies , 2007 .

[39]  Istvan Szunyogh,et al.  Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model , 2005 .

[40]  Yu-Lin Chang,et al.  Coupled response of the trade wind, SST gradient, and SST in the caribbean sea, and the potential impact on loop current's interannual variability , 2013 .

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

[42]  Lie-Yauw Oey,et al.  Hindcast of Waves and Currents in Hurricane Katrina , 2008 .

[43]  George L. Mellor,et al.  Diagnostic and prognostic calculations of the North Atlantic circulation and sea level using a sigma coordinate ocean model , 1994 .

[44]  Andrew T. Cox,et al.  Hindcast Study Of Winds, Waves, and Currents In Northern Gulf Of Mexico In Hurricane Ivan (2004) , 2005 .

[45]  Istvan Szunyogh,et al.  A Local Ensemble Kalman Filter for Atmospheric Data Assimilation , 2002 .

[46]  G. Forristall,et al.  Evolution and kinematics of a loop current eddy in the Gulf of Mexico during 1985 , 1992 .

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

[48]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.