Status and future of data assimilation in operational oceanography

The GODAE OceanView systems use various data assimilation algorithms, including 3DVar, EnOI, EnKF and the SEEK filter with a fixed basis, using different time windows. The main outputs of the operational data assimilation systems, the increments, have been compared for February 2014 in various regions. The eddy-permitting systems’ increments are similar in a number of the regions, indicating similar forecast errors are being corrected, while the eddy-resolving systems represent smaller-scale structures in the mid-latitude regions investigated and appear to have smaller biases. Monthly average temperature increments show significant SST biases, particularly in the systems which assimilate swath satellite SST data, indicating systematic errors in the surface heat fluxes and the way in which they are propagated vertically by the ocean models. On-going developments to the data assimilation systems include improvements to the specification of error covariances, improving assimilation of data near the equator, and understanding the effect of assimilation on the Atlantic Meridional Overturning Circulation. Longer term developments are expected to include the implementation of more advanced algorithms to make use of flow-dependent error covariance information. Assimilation of new data sources over the coming years, such as wide-swath altimetry, is also expected to improve the accuracy of ocean state estimates and forecasts provided by the GODAE OceanView systems.

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

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

[3]  K. Haines,et al.  Assimilation of RAPID array observations into an ocean model , 2012 .

[4]  Gary B. Brassington,et al.  Status and future of global and regional ocean prediction systems , 2015 .

[5]  Gary B. Brassington,et al.  Operational Oceanography in the 21st Century , 2011 .

[6]  Jean-Michel Brankart,et al.  Impact of uncertainties in the horizontal density gradient upon low resolution global ocean modelling , 2013 .

[7]  Tong Lee,et al.  The Ocean Reanalyses Intercomparison Project (ORA-IP) , 2015 .

[8]  Keith Haines,et al.  Ocean altimeter assimilation with observational‐ and model‐bias correction , 2008 .

[9]  Andrew Ryan,et al.  Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts , 2013 .

[10]  Peter R. Oke,et al.  TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic , 2012 .

[11]  Hideyuki Nakano,et al.  Simulating present climate of the global ocean–ice system using the Meteorological Research Institute Community Ocean Model (MRI.COM): simulation characteristics and variability in the Pacific sector , 2011 .

[12]  Nancy Nichols,et al.  Assimilation of data into an ocean model with systematic errors near the equator , 2004 .

[13]  Peter R. Oke,et al.  Ocean Data Assimilation Systems for GODAE , 2009 .

[14]  Mark Buehner,et al.  Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System , 2016 .

[15]  M. Drévillon,et al.  Evaluation of global monitoring and forecasting systems at Mercator Océan , 2013 .

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

[17]  Gary B. Brassington,et al.  Adaptive nonlinear dynamical initialization , 2011 .

[18]  Y. Fujii,et al.  Three-dimensional analysis of temperature and salinity in the equatorial Pacific using a variational method with vertical coupled temperature-salinity empirical orthogonal function modes , 2003 .

[19]  Jean-Michel Brankart,et al.  Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations , 2011 .

[20]  G. Larnicol,et al.  Assessing the impact of observations on ocean forecasts and reanalyses: Part 2, Regional applications , 2015 .

[21]  P. Oke,et al.  Towards a dynamically balanced eddy-resolving ocean reanalysis: BRAN3 , 2013 .

[22]  E. Greiner,et al.  Impact of the Incremental Analysis Updates on a Real-Time System of the North Atlantic Ocean , 2008 .

[23]  Yosuke Fujii,et al.  Meteorological research institute multivariate ocean variational estimation (MOVE) system : Some early results , 2006 .

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

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

[26]  A. Weaver,et al.  Representation of correlation functions in variational assimilation using an implicit diffusion operator , 2010 .

[27]  Arthur Vidard,et al.  A multivariate treatment of bias for sequential data assimilation: Application to the tropical oceans , 2007 .

[28]  Peter R. Oke,et al.  Representation Error of Oceanic Observations for Data Assimilation , 2008 .

[29]  Kristian Mogensen,et al.  The NEMOVAR ocean data assimilation system as implemented in the ECMWF ocean analysis for System 4 , 2012 .

[30]  Geir Evensen,et al.  The ensemble Kalman filter for combined state and parameter estimation: MONTE CARLO TECHNIQUES FOR DATA ASSIMILATION IN LARGE SYSTEMS , 2009 .

[31]  M. Balmaseda,et al.  Evaluation of the ECMWF ocean reanalysis system ORAS4 , 2013 .

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

[33]  G. Brassington.  Multicycle ensemble forecasting of sea surface temperature , 2013 .

[34]  E. Zaron.  Introduction to Ocean Data Assimilation , 2011 .

[35]  Ole Martin Smedstad,et al.  Variational Data Assimilation for the Global Ocean , 2013 .

[36]  A. Weaver,et al.  Implementing a variational data assimilation system in an operational 1/4 degree global ocean model , 2015 .

[37]  Seon Ki Park,et al.  Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III) , 2009 .