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End of Semester Meeting

End of Semester Meeting. C onically Scanning A ctive/ P assive S ensor Simulation Tool (CAPS). Pete Laupattarakasem Liang Hong. Presentation Outline. CAPS Overview Module Descriptions Simulation Improvements Results & Verification Future Work & Summary. CAPS Overview.

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End of Semester Meeting

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  1. End of Semester Meeting Conically Scanning Active/Passive Sensor Simulation Tool (CAPS) Pete Laupattarakasem Liang Hong

  2. Presentation Outline • CAPS Overview • Module Descriptions • Simulation Improvements • Results & Verification • Future Work & Summary

  3. CAPS Overview • CAPS is a computer simulation aimed to simulate space borne conically scanning radar • Cover end-to-end mission operation • Define orbit/sensor/beam parameters • Simulate realistic environment • Retrieve geophysical parameters • Active Sensor (Scatterometer) • Wind 0 calculations • Passive Sensor (Radiometer) • Rain effects • Future simulation include Tb contributions from rain effects

  4. Current Phase • Simulate SeaWinds on QuikSCAT measurement over oceans • Rain-free simulation • Verify wind retrieval result with compass simulation • Performance test of wind retrieval algorithm • Compass simulation assigns known wind field and compares with retrieved wind vectors • WS = 5, 10, 20 ms • Wind Dir = 0, 30, 60 … 330 • Noise-free and noisy • Run whole mission • ECMWF serves as wind field surface truth

  5. CAPS Structure • CAPS architecture • MATLAB • Main program (GUI) calls subroutine m-Files • Fast matrix calculations • User friendly and easy output displays • Fortran (WRET subroutine in Wind Retrieval ) • Computation intensive task, efficient loop calculations

  6. Calculate Microwave Sensor Observables Geophysical Retrieval Simulation Block Diagram Geom. Interpolation ECMWF Satellite Orbit Geometry Calculation User Inputs Interpolate Geophysical Parameters (Tb, wind vector, rain rate) Model Geophysical Parameters Group Measurements Grouping Compare Geophysical Parameters and Calculate Statistics Wind Ret.

  7. Presentation Outline • CAPS Overview • Module Descriptions • Simulation Improvements • Results & Verification • Future Work & Summary

  8. Orbital Inputs Start/stop point (lat/lon) Height Sensor Inputs Scan rate Overlapping factor Frequency, pulse length Beam Parameters Beam angles (cone, HPBW angles) Polarization Calculates lat/lon of center of IFOV with curved earth geometry based on user-defined inputs Geometry Module

  9. Forw-V Forw-H Scan direction 1800 km 1400 km Aft-H Aft-V Footprint & Flavors • Perform 360 conical scan • Inclination angle = 98.61

  10. Geometry Output Example • Ran from -40 to 40 lat • Set as 50% overlapping (az) Start point Geo. Foot print Measurements in ¼ box

  11. Interpolation Module • Load ECMWF model wind vector and interpolate to IFOV points calculated by Geometry Module • Rain effects • Tb due to rain rate • 0 due to rain volume backscatter • Atmospheric attenuation due to averaged monthly climatology and rain contamination. • Land and ice masks are applied • Antenna pattern convolution is the most time consuming part

  12. Grouping Module • Assign WVC index to each entry • Group measurements into user-defined cell size • SeaWinds cell box = 0.25x0.25 • Approximately 10-14 measurements in a box • The least time consuming module

  13. Wind Retrieval (WRET) • Assign 0 to grouped wind vectors 0tot =  0wind + 0rain 0wind is from Geophysical Model Function (GMF) 0rain is delta NRCS due to rain volume backscatter • is atmospheric + rain attenuation • Random noise can be added at user preference • MLE: • Rank wind vectors with MLE values • Select the one closest to the truth

  14. Presentation Outline • CAPS Overview • Module Descriptions • Simulation Improvements • Results & Verification • Future Work & Summary

  15. Simulation Improvements

  16. Negative  handling Improper method using “mod” Major Fixes (1) 3+ flavors 4 flavors 3+ flavors Corrected Wrong

  17. Major Fixes (2) • Flavor Selection • Previously, # of records were counted instead of # of flavors • With flavor records introduced from Geometry module, flavors can be sorted instantly • “For” loops substitutions • MATLAB is good at matrix calculations in stead of loops • Reduce execution time by the factor of 10+ • E.g. in grouping, from >20 mins. to <2 mins • More work to do in antenna convolution

  18. Presentation Outline • CAPS Overview • Module Descriptions • Simulation Improvements • Results & Verification • Future Work & Summary

  19. Case Description • Geometry is run from • -40 to 40 degrees in Lat. • 220 degrees in Lon. • Ascending pass orbit • 50% overlap in cross track scanning • Over 50,000 measurements made, ~ 13,000 WVC’s after grouped

  20. Compass Simulation Results (Noise-free) WS = 5 m/s Wind Dir = 120 deg Mean = 4.98 STD = 0.01 Mean = 119.98 STD = 1.28

  21. Compass Simulation Results (Noisy) WS = 5 m/s Wind Dir = 120 deg Mean = 4.98 STD = 0.28 Mean = 120.22 STD = 13.49

  22. WRET: Results in scatter plots

  23. WRET: Results in histograms

  24. Presentation Outline • CAPS Overview • Module Descriptions • Simulation Improvements • Results & Verification • Future Work & Summary

  25. Future Works • Code Optimization • Crucial • ‘Out of memory’ when runs antenna pattern convolution. Code needs modification • Performance enhancement • Reduce loops • More MATLAB built-in functions • Verify More Cases • Ascending, descending • With/without noise • Incorporate More Features • Rain effect • New RadTb algorithm in Interpolation Module

  26. Work Summary & Conclusion • Code Debugging • Crucial Errors • Algorithm logics • Incompatibility in parameter transferring • Improper parameter treatment • Performance Improvements • Add more user interface parameter • Preserve useful parameters (for verification) • Replace loop and redundant calculations • More bugs expected! • CAPS is a powerful spaceborne radar simulation tool

  27. Back Ups

  28. WRET: Low Wind (0~5m/s)

  29. WRET: Mid Wind (5~10m/s)

  30. WRET: High Wind (10~15m/s)

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