Physically-based data assimilation

Abstract. Ideally, a validation and assimilation scheme should maintain the physical principles embodied in the model and be able to evaluate and assimilate lower dimensional features (e.g., discontinuities) contained within a bulk simulation, even when these features are not directly observed or represented by model variables. We present such a scheme and suggest its potential to resolve or alleviate some outstanding problems that stem from making and applying required, yet often non-physical, assumptions and procedures in common operational data assimilation. As proof of concept, we use a sea-ice model with remotely sensed observations of leads in a one-step assimilation cycle. Using the new scheme in a sixteen day simulation experiment introduces model skill (against persistence) several days earlier than in the control run, improves the overall model skill and delays its drop off at later stages of the simulation. The potential and requirements to extend this scheme to different applications, and to both empirical and statistical multivariate and full cycle data assimilation schemes, are discussed.

[1]  Kenneth J. Westrick,et al.  Does Increasing Horizontal Resolution Produce More Skillful Forecasts , 2002 .

[2]  D. Sulsky,et al.  Evaluating Sea Ice Deformation in the Beaufort Sea Using a Kinematic Crack Algorithm with RGPS Data , 2011 .

[3]  R. Kwok.  The RADARSAT Geophysical Processor System , 1998 .

[4]  J. Hoke,et al.  The Initialization of Numerical Models by a Dynamic-Initialization Technique , 1976 .

[5]  Ron Lindsay,et al.  Assimilation of Ice Concentration in an Ice–Ocean Model , 2006 .

[6]  Gad Levy,et al.  Metrics for evaluating linear features , 2008 .

[7]  Ross M. McConnell,et al.  An ice-motion tracking system at the Alaska SAR facility , 1990 .

[8]  Olivier Talagrand,et al.  Assimilation of Observations, an Introduction (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .

[9]  D. Sulsky,et al.  Using the material‐point method to model sea ice dynamics , 2007 .

[10]  Jonathan D. Beezley,et al.  A wildland fire model with data assimilation , 2007, Math. Comput. Simul..

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

[12]  W. Meier,et al.  Data assimilation of Sea-ice motion vectors: Sensitivity to the parameterization of Sea-ice Strength , 2006, Annals of Glaciology.

[13]  L. Perelman,et al.  A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers , 1997 .

[14]  A. H. Murphy,et al.  Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient , 1988 .

[15]  E. Schulson,et al.  Compressive shear faults within arctic sea ice: Fracture on scales large and small , 2004 .

[16]  A. A. Griffith.  The Phenomena of Rupture and Flow in Solids , 1921 .

[17]  D. Sulsky,et al.  Arctic Ice Dynamics Joint Experiment (AIDJEX) assumptions revisited and found inadequate , 2007 .

[18]  Y. Kurihara,et al.  An Initialization Scheme of Hurricane Models by Vortex Specification , 1993 .

[19]  Ron Kwok,et al.  Deformation of the Arctic Ocean Sea Ice Cover between November 1996 and April 1997: A Qualitative Survey , 2001 .

[20]  T. Vukicevic,et al.  The Effect of Linearization Errors on 4DVAR Data Assimilation , 1998 .

[21]  Ron Kwok,et al.  Elastic‐decohesive constitutive model for sea ice , 2006 .