Active and Passive Rain Retrieval for QuikSCAT Satellite

1. Combined Active & Passive Rain Retrieval for QuikSCAT Satellite Khalil A. Ahmad Central Florida Remote Sensing Laboratory University of Central Florida Orlando, FL, USA http://www.engr.ucf.edu/centers/cfrsl/ End of Semester Group Presentation Dec 10, 2005

2. Presentation Outline: • Description of QuikSCAT Rain Algorithm • Passive Rain Rate retrievals (QRad Algorithm) • Physical Basis • QRad Algo. Tuning • Validation of QRad retrievals (JPL L2B Data Product) • Active rain rate algorithm development • Physical Basis • Sigma-0 Forward Model • Summary & Concluding Remarks

3. QuikSCAT Rain Algorithm Description • Oceanic instantaneous integrated rain rate, 0.25 deggrid resolution. • Uses SeaWinds remote sensor on the QuikSCAT satellite • Polarized Microwave brightness temperatures. • Polarized normalized radar cross section (Sigma-0s) • Retrieved wind speeds • Based upon near-simultaneous collocations with TRMM Microwave Imager (TMI) oceanic rain rates (TRMM 2A12 Data product)

4. QuikSCAT Rain Algorithm Description • Passive rain retrieval component (QRad): • Statistical retrieval algorithm (Tex – IRR relationship) • Improved DT by averaging / spatial filtering • Provides simultaneous, collocated precipitation measurements with QuikSCAT ocean surface wind vectors for rain-flagging contaminated wind vector retrievals • Increase Oceanic rain sampling by ~ 10%

5. SeaWinds Measurement Geometry

6. Passive Rain Retrieval (QRad Algorithm tuning)

7. Excess Brightness Temperature • Rain absorbs and re-emits radiation, thus increases the observed microwave brightness temperature • The polarized microwave “excess brightness” (Texp) is proportional to the integrated rain rate • Tb ocean = ocean background (includes atmospheric Emissions • without rain) • based upon 7 year SSMI climatology • Tb w.speed = wind speed brightness bias

8. QRad Rain Rate Block Diagram QRad Tb (L2A) Calc. Polarized Excess Brightness Tex @ 25 km Ocean Tb background QuikSCAT wind Speed (L2B) Spatial Filtering 3x3 Window Using ( Tex- IRR ) Calc. Polarized Instantaneous Rain Rate Combine using a weighted average Apply threshold Instantaneous Rain Rate Product By orbit, 25 km resolution

9. 1st Quarter ~ 106 2nd Quarter~ 121 3rd Quarter~ 167 4th Quarter~ 27 QRad – TRMM Collocation Data Base

10. ~ 300 Revs ~ 15,000,000 points H-pol eToh= 1 k V-pol eTov= - 0.8 k Tex Remove Tex Biases

11. QRad Tex – TMI IRR Transfer Functions (421 Collocated Rain events) • 3rd order polynomial • Odd symmetry

12. QRad – TMI IRR scatter

13. QRad – Rain Threshold TMI Oceanic Coverage

14. Comparisons of QRad Retrievals with TMI 2A12 Rain Rates (JPL L2B Validation)

15. Validation Data Set • JPL Data: 173 Revs, sampled from April ~ Oct ’03 • Rain Collocation Data: 70 Collocated Rain events < 30 min

16. Tex Biases / Rain (173 Revs Apr’03~ Oct’03) ± 1 K ± 1 K ± 1 km mm/hr

17. Comparison of ~ 70 Instantaneous QRad – TRMM 2A12 Collocated Rain Events

18. Rain Statistics – (70 Collocated events) • Rain Pattern: • Agreement percentage ~ 83.43 % • Mis-Rain ~ 7.42% • False Alarm ~ 9.14 % • Rain Magnitude : • Within 3dB ~ 80.54 % • Within 1dB ~ 58.99 % • Within 0.5 dB ~ 52.52%

19. Rain Image Comparison QRad TMI

20. QRad / TMI Rain Pattern Classification TMI >0, QRad >0 TMI =0, QRad >0 TMI =0, QRad=0 TMI >0, QRad =0 Agree = 89.52% False alram = 6.08% Mis-rain = 3.35%

21. Rain Image Comparison QRad TMI

22. QRad / TMI Rain Pattern Classification TMI >0, QRad >0 TMI =0, QRad >0 TMI =0, QRad=0 TMI >0, QRad =0 Agree = 89.31% False alram =3.94% Mis-rain = 6.76%

23. Active Rain Retrieval Algorithm Development

24. SeaWinds Scatterometer: Ocean Surface •  o: Normalized Radar Cross Section (NRCS) of the ocean surface

25. Ocean Backscattering: •  o is a function of incidence angle, frequency, polarization and ocean wind vector (speed and direction) • The geophysical model function (GMF):An empirical relationship between  oand the ocean near surface wind velocity:

26. Rain Effects on Ocean  o • In the presence of Rain, three major factors affect the measured ocean surface  o : • Two way path attenuation • Reduces received power • Volume backscatter • Enhances received power • Surface perturbation “Splash Effect” • Alters ocean surface roughness structure

27. SeaWinds Backscatter Forward Model σ0m : Measured SeaWinds backscatter σ0wind : Wind induced backscatter σ0rain-vol : Volume-backscatter due to rain σ0surf : Surface perturbation due to rain σ0Ex-rain : Excess-backscatter due to rain α : Two-way path attenuation

28. WVC Geolocation L2B Data Product Model Wind speed Model Wind Dir QuikSCAT GMF QSCAT-1 4-flavour σ 0w Combine FWD/AFT & Earth Locate Calc. Relative Azimuth L2B Cell Azimuth L2B Cell Incidence L2A Data Product Co-register On L2B Grid Cell Azimuth Cell Incidence Wind Induced Backscatter (σ0wind) Model H/V Polarized Wind induced Sigma-0’s By orbit, 25 km resolution

29. Wind Induced Backscatter (σ0w)

30. Attenuation derived from PR: H-Pol V-Pol

31. Rain Volume BackScatter derived from PR:

32. Rain Backscatter (σ0EX-rain) H-Pol V-Pol

33. Future Work • Combine/Validate Sigma-0 Model • Develop a complementary active rain retrieval • Combine Active/Passive Rain retrievals Minimize:

34. Summary: • JPL rain processing is in excellent agreement with CFRSL processing • QRad provides quantitative estimates of • instantaneous rain rates over oceans • QRad rain measurements are in good agreement • with TRMM 2A12