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1_kasapoglu_igarss11.ppt

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  • 1. Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis N. G ökhan K asapoğlu Dept . of Electronics and Communication Engineering İ stanbul Technical University , Turkey
  • 2. Outline <ul><li>Introduction </li></ul><ul><li>0D-Var SAR Data Assimilation </li></ul><ul><li>SAR Features </li></ul><ul><li>SAR Forward Model </li></ul><ul><li>Sea Ice Open Water Discrimination </li></ul><ul><li>A Simple Forward Model </li></ul><ul><li>SAR 0D-Var Analysis Results </li></ul><ul><li>Optimal SAR Feature Selection </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 3. SAR Data Assimilation R2 Dual Polarized SCWA SAR DATA Forecast Analysis Forecast SAR Feature Extraction Assimilated Observations SAR Forward Model DATA Assimilation Mode l N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 4. Variational data assimilation <ul><li>Basic idea: Bayes theorem is used to update pdf of model forecast ( x ) using all available observations ( y ) </li></ul><ul><li>If all pdfs Gaussian, maximum likelihood estimate minimizes the function: </li></ul>J obs J b <ul><li>where: </li></ul><ul><ul><li>x b is the short-term ice-ocean model forecast used as the background state </li></ul></ul><ul><ul><li>B is the background error (forecast error) covariance matrix </li></ul></ul><ul><ul><li>y is the vector of observations </li></ul></ul><ul><ul><li>R is the observation error covariance matrix </li></ul></ul><ul><ul><li>H maps the analysis vector into observation space </li></ul></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 5. Variational method of solution <ul><li>Use an iterative descent algorithm to find the analysis increment ∆x = x-x b that minimizes the cost function J ( requires gradient of J ) </li></ul><ul><li>If obs operator is linear , then J is quadratic and possible to easily find unique minimum </li></ul><ul><li>Otherwise, obs operators can be periodically re-linearized during minimization </li></ul><ul><li>Can be difficult to find the global minimum of a non-quadratic cost function (when H highly nonlinear) </li></ul><ul><li>Grey is original J with nonlinear H , Red curves are from linearizing H </li></ul>-> N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis J Analysis Variable
  • 6. 0D-Var SAR Data Assimilation R2 Dual Polarized SCWA SAR DATA SAR Feature Extraction Assimilated Observations SAR Forward Model H(x,  ) DATA Assimilation (0D-Var), J(x) Analysis N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 7. Data Assimilation Formulation <ul><li>y : the observed SAR features </li></ul><ul><li>H : the forward model, H(x,  ) is the predicted SAR feature </li></ul><ul><li>x : ice concentration </li></ul><ul><li> : incidence angle </li></ul><ul><li>R : observation error covariance matrix </li></ul><ul><li>B : background error covariance matrix </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 8. RadarSAT-2 ScanSAR data <ul><li>RadarSAT-2 SCWA Dual-polarized data </li></ul><ul><ul><li>C Band, 5.6 GHz </li></ul></ul><ul><ul><li>HH and HV channels </li></ul></ul><ul><ul><li>500 km 2 coverage </li></ul></ul><ul><ul><li>100 m pixel spacing </li></ul></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 9. SAR Features r2_20090226_215200 <ul><li>RadarSAT-2 SCWA SGF Product – HH channel </li></ul>r2_20090301_220402 RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2009) - All Rights Reserved. N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 10. SAR Features <ul><li>RadarSAT-2 SCWA SGF Product – HV channel </li></ul>r2_20090226_215200 r2_20090301_220402 RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2009) - All Rights Reserved. N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 11. SAR Features N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 12. SAR Features Observed HH-Variance from r 2_20090226_215200 Observed HH-Dissimilarity from r 2_20090301_220402 <ul><li>Texture Feature Examples </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 13. SAR Features Observed HV-Lee Filtered Image from R2_20090226_215200 Observed HV-Entropy from R2_20090301_220402 <ul><li>Texture Feature Examples </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 14. Forward Model IT h = ice thickness (from CIS image analysis chart) IC = ice concentration (from CIS image analysis chart)  = incidence angle (know) SAT = surface air temperature (from GEM) SD = snow depth (from GEM) WS = wind speed (from GEM)  = angle between instrument angle (know) and wind direction (from GEM)   = “optimal” model coefficients estimated from “training data” N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 15. Simulated SAR Features Predicted HH from obs operator and image analysis Observed H H from RadarSAT-2 image <ul><li>r 2_20090 301 _2 20402 </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 16. Simulated SAR Features Predicted HV-Entropy from obs operator and image analysis HV-Entropy from RadarSAT-2 image <ul><li>r 2_20090 301 _2 20402 </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 17. Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0 HH Mean ) for Sea ice (Red) with IC>%95 and Open Water (Blue) versus incidence Angle N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 18. Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0 HV) for Sea ice (Red) with IC>%95 and Open Water (Blue) versus incidence Angle N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 19. Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0 HV Entropy) for Sea ice (Red) with IC>%95 and Open Water (Blue) versus incidence Angle N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 20. Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0 HV Data Range ) for Sea ice (Red) with IC>%95 and Open Water (Blue) versus incidence Angle N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 21. Simple Forward Model H : Observation operator (forward model operator) IC : Ice Concentration  i : Incidence Angle  o = floor (  i )  o : Rounded Incidence Angle  o = 19,20,...,49 for SCWA Number of Incidence Angle quantization level: 31 N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 22. 0D-Var Analysis Results Background_Bias Background_Std Analysis_Bias Analysis_std - 0.074 4 0.19 9 -0.0667 0.194 F_ID: 3_4_7_8_9_11_13_23 X a =X b +  x 0D-Var Analysis Result X b : Background State; PM data only R2_20090226_215200 N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 23. 0D-Var Analysis Results Analysis Increment:  x R2_20090226_215200 R: HH Lee-Filtered Image G: HH Variance B: HV Mean N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 24. 0D-Var Analysis Results Analysis Increment:  x R2_20090226_215200 N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 25. 0D-Var Analysis Results Background_Bias Background_Std Analysis_Bias Analysis_std 0.1471 0.3057 0.088 9 0.264 5 R2_201002 21 _ 10 3028 X a =X b +  x X b : Background State; PM data only N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 26. 0D-Var Analysis Results R2_201002 21 _10 3028 Analysis Increment:  x HH H V N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 27. 0D-Var Analysis Results N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 28. Optimal SAR Feature Selection <ul><li>Statistical ly independent features  </li></ul><ul><li>Forward model generalization capability  </li></ul><ul><li>Sea-Ice and open water tie points distributions  </li></ul><ul><li>Analysis results statistics  </li></ul><ul><li>Weights of factors listed above x </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 29. Separability Measures and Discrimination Analysis N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 30. SAR Feature Selection N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis Best SAR feature combination selection Top-Down & Bottom-Up Strategies Analysis Bias as a selection criteria Feature Selection for Incidence Angle Intervals
  • 31. Feature Selection for Incidence Angle Intervals N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 32. Acknowledgements <ul><li>Canadian Ice Service (CIS) </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  • 33. Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis N. G ökhan K asapoğlu [email_address] Thank you for your attention! N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11

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