Error Covariance Modeling in the GMAO Ocean Ensemble Kalman Filter

Abstract In practical applications of the ensemble Kalman filter (EnKF) for ocean data assimilation, the computational burden and memory limitations usually require a trade-off between ensemble size and model resolution. This is certainly true for the NASA Global Modeling and Assimilation Office (GMAO) ocean EnKF used for ocean climate analyses. The importance of resolution for the adequate representation of the dominant current systems means that small ensembles, with their concomitant sampling biases, have to be used. Hence, strategies have been sought to address sampling problems and to improve the performance of the EnKF for a given ensemble size. Approaches assessed herein consist of spatiotemporal filtering of background-error covariances, improving the system-noise representation, imposing a steady-state error covariance model, and speeding up the analysis by performing the most expensive operation of the analysis on a coarser computational grid. A judicious combination of these approaches leads to...

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

[2]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[3]  Song Yang,et al.  Sensitivity of the tropical Pacific Ocean to precipitation-induced freshwater flux , 1999 .

[4]  Arlindo da Silva,et al.  Data assimilation in the presence of forecast bias , 1998 .

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

[6]  D. Pham.  Stochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems , 2001 .

[7]  Andrew F. Loughe,et al.  A Reduced-Gravity Isopycnal Ocean Model: Hindcasts of El Niño , 1995 .

[8]  P. Houtekamer,et al.  A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation , 2001 .

[9]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

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

[11]  J. Whitaker,et al.  Ensemble Square Root Filters , 2003, Statistical Methods for Climate Scientists.

[12]  Christian L. Keppenne,et al.  Assimilation of temperature into an isopycnal ocean general circulation model using a parallel ensemble Kalman filter , 2003 .

[13]  Arun K. Sood,et al.  Implementation and Performance Evaluation of a Parallel Ocean Model , 1998, Parallel Comput..

[14]  Christian L. Keppenne,et al.  Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction , 2005 .

[15]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[16]  Lawrence L. Takacs,et al.  Data Assimilation Using Incremental Analysis Updates , 1996 .

[17]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

[18]  R. Pacanowski,et al.  Parameterization of Vertical Mixing in Numerical Models of Tropical Oceans , 1981 .

[19]  Embedding a mixed layer model into an ocean general circulation model of the Atlantic : the importance of surface mixing for heat flux and temperature , 1994 .

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

[21]  D. Adamec.  Modulation of the seasonal signal of the Kuroshio Extension during 1994 from satellite data , 1998 .