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Multi-Scale Modeling, Data Assimilation and Reanalysis in Support of SPURS Data Synthesis

Multi-Scale Modeling, Data Assimilation and Reanalysis in Support of SPURS Data Synthesis. Zhijin Li (JPL) Julius Busecke , Arnold Gordon (LDRO), Fred Bingham (UNC), and Peggy Li (JPL) April 16 , 2014, Pasadena, CA. Eddies Spanning a Spectrum of Meso-Scales. How?.

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Multi-Scale Modeling, Data Assimilation and Reanalysis in Support of SPURS Data Synthesis

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  1. Multi-Scale Modeling, Data Assimilation and Reanalysis in Support of SPURS Data Synthesis Zhijin Li (JPL) Julius Busecke, Arnold Gordon (LDRO), Fred Bingham (UNC), and Peggy Li (JPL) April 16, 2014, Pasadena, CA

  2. Eddies Spanning a Spectrum of Meso-Scales How?

  3. Significant Transports from Fine Structure Features (Busecke, et al. 2014)

  4. Communication Satellite Argos or irridium SPURS Network: Hierarchical Observations WMO Global Telecomm System Individual Lab Servers WaveGlider Flux Mooring Prawler Mooring Ship-based instruments Argo Float Surface Drifter (From Tom Farrar) SeaGlider

  5. Synthesis Method: Optimal Interpolation (OI) vs Variational Algorithm • FOR EACH grid point to be estimate • Select the observations placed inside the influence bubble • Compute the observation covariance matrix (A) and its Inversion (A-1) • Calculation of the correlation vector (C) • Compute the estimation Bretherton et al. (1976) When observations are at grid points, H becomes a diagonal matrix: Equivalence

  6. Variational Algorithm with Multi-Decorrelation Length Scales: From Satellite to Fine SPURS Observations Low resolution obs High resolution obs (Li et al., 2012) 2D 2D

  7. AVISO vs ROMSFrom Low to High Resolution 9KM 3KM

  8. Detection and Attribution of Multi-Scale Features: Maximum SSSs and Convergence of Three Water Masses Cold/Fresh Front SSS-max/ Warm Water Warm/Fresh Front

  9. Quantifying and Characterizing Contributions for a Hierarchy of Spatial and Temporal Scales

  10. “Warm Front?” March 08, 2013 March 18, 2013

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