Assimilating NOAA SST data into BSH operational circulation model for the North and Baltic Seas: Part 2. Sensitivity of the forecast's skill to the prior model error statistics

A data assimilation (DA) system has been developed for the operational circulation model of the German Federal Maritime and Hydrographic Agency (BSH) in order to improve the forecast of hydrographic characteristics in the North and Baltic Seas. It is based on the local Singular Evolutive Interpolated Kalman (SEIK) filter algorithm and assimilation of the NOAA AVHRR-derived sea surface temperature (SST). The DA system allows one to improve the agreement of the SST forecast with the satellite observations by 27% on average over the period of October 2007–September 2008. However, a sensitivity analysis of the forecasting system performance shows a significant impact of initial model error statistics on ice fields and bottom temperature. A reinitialisation of model error covariances in accordance with seasonality of the model error statistics was required in order to maintain the predictive skill with respect to these variables. The success of the DA system is quantified by the comparison with independent data from MARNET stations as well as sea ice concentration measurements. In addition, the Maximum Entropy approach is used to assess the system performance and the prior and posterior model error statistics.

[1]  Svetlana N. Losa,et al.  Weak constraint parameter estimation for a simple ocean ecosystem model: what can we learn about the model and data? , 2004 .

[2]  Lars Nerger,et al.  Software for ensemble-based data assimilation systems - Implementation strategies and scalability , 2013, Comput. Geosci..

[3]  G. Kivman,et al.  An entropy approach to tuning weights and smoothing in the generalized inversion , 2001 .

[4]  L. Bertino,et al.  Application of a hybrid EnKF-OI to ocean forecasting , 2009 .

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

[6]  Jens Schröter,et al.  On domain localization in ensemble based Kalman Flter algorithms , 2008 .

[7]  Jens Schröter,et al.  Using sea-level data to constrain a finite-element primitive-equation ocean model with a local SEIK filter , 2006 .

[8]  Jun She,et al.  Application of an Ensemble Optimal Interpolation in a North/Baltic Sea model: Assimilating temperature and salinity profiles , 2011 .

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

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

[11]  O. Mayol-Bracero,et al.  Speciation of Water‐Soluble Inorganic, Organic, and Total Nitrogen in a Background Marine Environment: Cloud Water, Rainwater, and Aerosol Particles , 2011 .

[12]  G. Evensen,et al.  Data assimilation and inverse methods in terms of a probabilistic formulation , 1996 .

[13]  Thierry Penduff,et al.  A four-year eddy-permitting assimilation of sea-surface temperature and altimetric data in the South Atlantic Ocean , 2002 .

[14]  Jean-Michel Brankart,et al.  Assimilation of sea-surface temperature and altimetric observations during 1992–1993 into an eddy permitting primitive equation model of the North Atlantic Ocean , 2003 .

[15]  Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data , 2009 .

[16]  Chris Snyder,et al.  Model Uncertainty in a Mesoscale Ensemble Prediction System: Stochastic versus Multiphysics Representations , 2011 .

[17]  Laurent Bertino,et al.  The TOPAZ monitoring and prediction system for the Atlantic and Arctic Oceans , 2008 .

[18]  Jens Schröter,et al.  Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments , 2005 .

[19]  Laurent Bertino,et al.  Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation: Application to a 1D ocean ecosystem model , 2012 .

[20]  Lars Nerger,et al.  PDAF - The Parallel Data Assimilation Framework: Experiences with Kalman Filtering , 2005 .

[21]  Jens Schröter,et al.  Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data , 2012 .

[22]  Pierre F. J. Lermusiaux,et al.  Adaptive modeling, adaptive data assimilation and adaptive sampling , 2007 .

[23]  G. Egbert,et al.  Variational assimilation of satellite observations in a coastal ocean model off Oregon , 2011 .

[24]  K. Brusdala,et al.  A demonstration of ensemble-based assimilation methods with a layered OGCM from the perspective of operational ocean forecasting systems , 2003 .

[25]  John Siddorn,et al.  Forecasting the ocean state using NEMO:The new FOAM system , 2010 .